End-to-End Google Earth Engine (Full Course Material)

A hands-on introduction to applied remote sensing using Google Earth Engine.

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Introduction

Google Earth Engine is a cloud-based platform that enables large-scale processing of satellite imagery to detect changes, map trends, and quantify differences on the Earth’s surface. This course covers the full range of topics in Earth Engine to give the participants practical skills to master the platform and implement their remote sensing projects.

View the Presentation ↗

Setting up the Environment

Sign-up for Google Earth Engine

If you already have a Google Earth Engine account, you can skip this step.

Visit https://signup.earthengine.google.com/ and sign-up with your Google account. You can use your existing gmail account to sign-up. It usually takes 1-2 days for approval. Hence do this step as soon as possible.

Tips to ensure smooth sign-up process:

• Use Google Chrome browser.
• When signing up for Earth Engine, please log out of all Google accounts and ensure you are logged into only 1 account which you want associated with Earth Engine.
• Prefer using your university/organization email for signing up.
• Access to Google Earth Engine is granted via Google Groups. The default settings should be fine, but verify you have the correct setting enabled.
  • Visit groups.google.com
  • Click on Settings (gear icon) and select Global Settings.
  • Make sure the option Allow group managers to add me to their groups is checked.

Complete the Class Pre-Work

This class needs about 2-hours of pre-work. Please watch the following videos to get a good understanding of remote sensing and how Earth Engine works. Videos are available online and can be streamed using the video links below.

Introduction to Remote Sensing

This video introduces the remote sensing concepts, terminology and techniques.

• Watch the Video
• View the Presentation ↗

Introduction to Google Earth Engine

This video gives a broad overview of Google Earth Engine with selected case studies and application. The video also covers the Earth Engine architecture and how it is different than traditional remote sensing software.

• Watch the Video
• View the Presentation ↗

Take the Quizes

After you watch the videos, please complete the following 2 Quizzes

1. Quiz-1 Remote Sensing Fundamentals.
2. Quiz-2 Google Earth Engine Fundamentals.

Get the Course Materials

The course material and exercises are in the form of Earth Engine scripts shared via a code repository.

1. Click this link to open Google Earth Engine code editor and add the repository to your account.
2. If successful, you will have a new repository named users/ujavalgandhi/End-to-End-GEE in the Scripts tab in the Reader section.
3. Verify that your code editor looks like below

If you do not see the repository in the Reader section, refresh your browser tab and it will show up.

Code Editor After Adding the Class Repository

Module 1: Earth Engine Basics

Module 1 is designed to give you basic skills to be able to find datasets you need for your project, filter them to your region of interest, apply basic processing and export the results. Mastering this will allow you to start using Earth Engine for your project quickly and save a lot of time pre-processing the data.

01. Hello World

This script introduces the basic Javascript syntax and the video covers the programming concepts you need to learn when using Earth Engine. To learn more, visit Introduction to JavaScript for Earth Engine section of the Earth Engine User Guide.

• Watch the Video

The Code Editor is an Integrated Development Environment (IDE) for Earth Engine Javascript API.. It offers an easy way to type, debug, run and manage code. Type the code below and click Run to execute it and see the output in the Console tab.

Tip: You can use the keyboard shortcut Ctrl+Enter to run the code in the Code Editor

Hello World

Open in Code Editor ↗

print('Hello World');

// Variables
var city = 'Bengaluru';
var country = 'India';
print(city, country);

var population = 8400000;
print(population);
 
// List
var majorCities = ['Mumbai', 'Delhi', 'Chennai', 'Kolkata'];
print(majorCities);

// Dictionary
var cityData = {
  'city': city,
  'population': 8400000,
  'elevation': 930
};
print(cityData);

// Function
var greet = function(name) {
    return 'Hello ' + name;
};
print(greet('World'));

// This is a comment

Exercise

Try in Code Editor ↗

// These are the 5 largest cities in the world: 
// Tokyo, Delhi, Shanghai, Mexico City, Sao Paulo

// Create a list named 'largeCities'
// The list should have names of all the above cities
// Print the list 

Saving Your Work

When you modify any script for the course repository, you may want to save a copy for yourself. If you try to click the Save button, you will get an error message like below

This is because the shared class repository is a Read-only repository. You can click Yes to save a copy in your repository. If this is the first time you are using Earth Engine, you will be prompted to choose the name of your home folder. Choose the name carefully, as it cannot be changed once created.

02. Working with Image Collections

Most datasets in Earth Engine come as a ImageCollection. An ImageCollection is a dataset that consists of images takes at different time and locations - usually from the same satellite or data provider. You can load a collection by searching the Earth Engine Data Catalog for the ImageCollection ID. Search for the Sentinel-2 Level 1C dataset and you will find its id COPERNICUS/S2_SR. Visit the Sentinel-2, Level 1C page and see Explore in Earth Engine section to find the code snippet to load and visualize the collection. This snippet is a great starting point for your work with this dataset. Click the Copy Code Sample button and paste the code into the code editor. Click Run and you will see the image tiles load in the map.

In the code snippet, You will see a function Map.setCenter() which sets the viewport to a specific location and zoom level. The function takes the X coordinate (longitude), Y coordinate (latitude) and Zoom Level parameters. Replace the X and Y coordinates with the coordinates of your city and click Run to see the images of your city.

Exercise

Try in Code Editor ↗

// Find the 'Sentinel-2 Level-1C' dataset page
// https://developers.google.com/earth-engine/datasets

// Copy/page the code snippet

// Change the code to display images for your home city

03. Filtering Image Collections

The collection contains all imagery ever collected by the sensor. The entire collections are not very useful. Most applications require a subset of the images. We use filters to select the appropriate images. There are many types of filter functions, look at ee.Filter... module to see all available filters. Select a filter and then run the filter() function with the filter parameters.

We will learn about 3 main types of filtering techniques

• Filter by metadata: You can apply a filter on the image metadata using filters such as ee.Filter.eq(), ee.Filter.lt() etc. You can filter by PATH/ROW values, Orbit number, Cloud cover etc.
• Filter by date: You can select images in a particular date range using filters such as ee.Filter.date().
• Filter by location: You can select the subset of images with a bounding box, location or geometry using the ee.Filter.bounds(). You can also use the drawing tools to draw a geometry for filtering.

After applying the filters, you can use the size() function to check how many images match the filters.

Open in Code Editor ↗

var geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241])
Map.centerObject(geometry, 10)

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

// Filter by metadata
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30));

// Filter by date
var filtered = s2.filter(ee.Filter.date('2019-01-01', '2020-01-01'));

// Filter by location
var filtered = s2.filter(ee.Filter.bounds(geometry));

// Let's apply all the 3 filters together on the collection

// First apply metadata fileter
var filtered1 = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30));
// Apply date filter on the results
var filtered2 = filtered1.filter(
  ee.Filter.date('2019-01-01', '2020-01-01'));
// Lastly apply the location filter
var filtered3 = filtered2.filter(ee.Filter.bounds(geometry));

// Instead of applying filters one after the other, we can 'chain' them
// Use the . notation to apply all the filters together
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));
  
print(filtered.size());

Exercise

Try in Code Editor ↗

// Add one more filter in the script below to select images
// from only one of the satellites - Sentinel-2A - from the
// Sentinel-2 constellation

// Hint1: Use the 'SPACECRAFT_NAME' property
// Hint2: Use the ee.Filter.eq() filter

var geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241]);
Map.centerObject(geometry, 10);

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));
  // Remove this comment and add a filter here
  
print(filtered.size());

04. Creating Mosaics and Composites from ImageCollections

The default order of the collection is by date. So when you display the collection, it implicitly creates a mosaic with the latest pixels on top. You can call .mosaic() on a ImageCollection to create a mosaic image from the pixels at the top.

We can also create a composite image by applying selection criteria to each pixel from all pixels in the stack. Here we use the median() function to create a composite where each pixel value is the median of all pixels from the stack.

Tip: If you need to create a mosaic where the images are in a specific order, you can use the .sort() function to sort your collection by a property first.

Mosaic vs. Composite

Open in Code Editor ↗

var geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241]);
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));
 
var mosaic = filtered.mosaic();
 
var medianComposite = filtered.median();

Map.addLayer(filtered, rgbVis, 'Filtered Collection');
Map.addLayer(mosaic, rgbVis, 'Mosaic');
Map.addLayer(medianComposite, rgbVis, 'Median Composite');

Exercise

Try in Code Editor ↗

// Create a median composite for the year 2020 and load it to the map

// Compare both the composites to see the changes in your city

05. Working with Feature Collections

Feature Collections are similar to Image Collections - but they contain Features, not images. They are equivalent to Vector Layers in a GIS. We can load, filter and display Feature Collections using similar techniques that we have learned so far.

Search for GAUL Second Level Administrative Boundaries and load the collection. This is a global collection that contains all Admin2 boundaries. We can apply a filter using the ADM1_NAME property to get all Admin2 boundaries (i.e. Districts) from a state.

Open in Code Editor ↗

var admin2 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2');

var karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'));

var visParams = {'color': 'red'};
Map.addLayer(karnataka, visParams, 'Karnataka Districts');

Exercise

Try in Code Editor ↗

// Apply a filter to select only the 'Bangalore Urban' district
// Display only the selected district

// Hint: The district names are in ADM2_NAME property

var admin2 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2');
var karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'));

var visParams = {'color': 'red'};

06. Importing Data

You can import vector or raster data into Earth Engine. We will now import a shapefile of Urban Areas for Natural Earth. Unzip the ne_10m_urban_areas.zip into a folder on your computer. In the Code Editor, go to Assets → New → Table Upload → Shape Files. Select the .shp, .shx, .dbf and .prj files. Enter ne_10m_urban_areas as the Asset Name and click Upload. Once the upload and ingest finishes, you will have a new asset in the Assets tab. The shapefile is imported as a Feature Collection in Earth Engine. Select the ne_10m_urban_areas asset and click Import. You can then visualize the imported data.

Importing a Shapefile

Open in Code Editor ↗

// Let's import some data to Earth Engine
// Upload the ne_10m_urban_areas.shp shapefile

// Import the collection
var urban = ee.FeatureCollection('users/ujavalgandhi/e2e/ne_10m_urban_areas');

// Visualize the collection
Map.addLayer(urban, {color: 'blue'}, 'Urban Areas');

Exercise

Try in Code Editor ↗

// Apply a filter to select only large urban areas
// Use the property 'area_sqkm' and select all features 
// with area > 400 sq.km

var urban = ee.FeatureCollection('users/ujavalgandhi/e2e/ne_10m_urban_areas');

07. Clipping Images

It is often desirable to clip the images to your area of interest. You can use the clip() function to mask out an image using a geometry.

While in a Desktop software, clipping is desirable to remove unnecessary portion of a large image and save computation time, in Earth Engine clipping can actually increase the computation time. As described in the Earth Engine Coding Best Practices guide, avoid clipping the images or do it at the end of your script.

Original vs. Clipped Image

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var urban = ee.FeatureCollection("users/ujavalgandhi/e2e/ne_10m_urban_areas");

// Find the feature id by adding the layer to the map and using Inspector.
var filtered = urban.filter(ee.Filter.eq('system:index', '00000000000000002bf8'));

var geometry = filtered.geometry();

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'], 
};
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

var image = filtered.median(); 

var clipped = image.clip(geometry);

Map.addLayer(clipped, rgbVis, 'Clipped');

Exercise

Try in Code Editor ↗

// Add the imported table to the Map
// Use the Inspector to find the id of your home city or any urban area of your choice
// Change the filter to use the id of the selected feature

08. Exporting Data

Earth Engine allows for exporting both vector and raster data to be used in an external program. Vector data can be exported as a CSV or a Shapefile, while Rasters can be exported as GeoTIFF files. We will now export the Sentinel-2 Composite as a GeoTIFF file.

Tip: Code Editor supports autocompletion of API functions using the combination Ctrl+Space. Type a few characters of a function and press Ctrl+Space to see autocomplete suggestions. You can also use the same key combination to fill all parameters of the function automatically.

Once you run this script, the Tasks tab will be highlighted. Switch to the tab and you will see the tasks waiting. Click Run next to each task to start the process.

On clicking the Run button, you will be prompted for a confirmation dialog. Verify the settings and click Run to start the export.

Once the Export finishes, a GeoTiff file for each export task will be added to your Google Drive in the specified folder. You can download them and use it in a GIS software.

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var urban = ee.FeatureCollection('users/ujavalgandhi/e2e/ne_10m_urban_areas');

var filtered = urban.filter(ee.Filter.eq('system:index', '00000000000000002bf8'));
var geometry = filtered.geometry();

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'], 
};
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

var image = filtered.median(); 

var clipped = image.clip(geometry);

Map.addLayer(clipped, rgbVis, 'Clipped');

var exportImage = clipped.select('B.*');

Export.image.toDrive({
    image: exportImage,
    description: 'Bangalore_Composite_Raw',
    folder: 'earthengine',
    fileNamePrefix: 'bangalore_composite_raw',
    region: geometry,
    scale: 10,
    maxPixels: 1e9
});

// Rather than exporting raw bands, we can apply a rendered image
// visualize() function allows you to apply the same parameters 
// that are used in earth engine which exports a 3-band RGB image
print(clipped);
var visualized = clipped.visualize(rgbVis);
print(visualized);
// Now the 'visualized' image is RGB image, no need to give visParams
Map.addLayer(visualized, {}, 'Visualized Image');

Export.image.toDrive({
    image: visualized,
    description: 'Bangalore_Composite_Visualized',
    folder: 'earthengine',
    fileNamePrefix: 'bangalore_composite_visualized',
    region: geometry,
    scale: 10,
    maxPixels: 1e9
});

Exercise

Try in Code Editor ↗

// Write the export function to export the results for your chosen urban area

Assignment 1

Load the Night Lights Data for 2019 and 2020. Compare the imagery for your region and find the changes in the city.

Assignment1 Expected Output

Try in Code Editor ↗

// Assignment
// Export the Night Lights images for May,2015 and May,2020

// Workflow:
// Load the VIIRS Nighttime Day/Night Band Composites collection
// Filter the collection to the date range
// Extract the 'avg_rad' band which represents the nighttime lights
// Clip the image to the geometry of your city
// Export the resulting image as a GeoTIFF file.

// Hint1: 

// There are 2 VIIRS Nighttime Day/Night collections
// Use the one that corrects for stray light

// Hint2: 

// The collection contains 1 global image per month
// After filtering for the month, there will be only 1 image in the collection

// You can use the following technique to extract that image
// var image = ee.Image(filtered.first())

Module 2: Earth Engine Intermediate

Module 2 builds on the basic Earth Engine skills you have gained. This model introduces the parallel programming concepts using Map/Reduce - which is key in effectively using Earth Engine for analyzing large volumes of data. You will learn how to use the Earth Engine API for calculating various spectral indices, do cloud masking and then use map/reduce to do apply these computations to collections of imagery. You will also learn how to take long time-series of data and create charts.

View the Presentation ↗

01. Earth Engine Objects

This script introduces the basics of the Earth Engine API. When programming in Earth Engine, you must use the Earth Engine API so that your computations can use the Google Earth Engine servers. To learn more, visit Earth Engine Objects and Methods section of the Earth Engine User Guide.

Open in Code Editor ↗

// Let's see how to take a list of numbers and add 1 to each element
var myList = ee.List.sequence(1, 10);

// Define a function that takes a number and adds 1 to it
var myFunction = function(number) {
  return number + 1;
}
print(myFunction(1));

//Re-Define a function using Earth Engine API
var myFunction = function(number) { 
  return ee.Number(number).add(1);
}

// Map the function of the list
var newList = myList.map(myFunction);
print(newList); 

// Extracting value from a list

var value = newList.get(0);
print(value);

// Casting

// Let's try to do some computation on the extracted value
//var newValue = value.add(1)
//print(newValue)

// You get an error because Earth Engine doesn't know what is the type of 'value'
// We need to cast it to appropriate type first
var value = ee.Number(value);
var newValue = value.add(1);
print(newValue);

// Dictionary
// Convert javascript objects to EE Objects
var data = {'city': 'Bengaluru', 'population': 8400000, 'elevation': 930};
var eeData = ee.Dictionary(data);
// Once converted, you can use the methods from the
// ee.Dictionary module
print(eeData.get('city'))

// Dates
// For any date computation, you should use ee.Date module

var date = ee.Date('2019-01-01')
var futureDate = date.advance(1, 'year')
print(futureDate)

As a general rule, you should always use Earth Engine API methods in your code, there is one exception where you will need to use client-side Javascript method. If you want to get the current time, the server doesn’t know your time. You need to use javascript method and cast it to an Earth Engine object.

var now = Date.now()
print(now)
var now = ee.Date(now)
print(now)

Exercise

Try in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241]);

var now = Date.now()
var now = ee.Date(now) 
// Apply another filter to the collection below to filter images
// collected in the last 1-month
// Do not hard-code the dates, it should always show images
// from the past 1-month whenever you run the script
// Hint: Use ee.Date.advance() function
//   to compute the date 1 month before now
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))

02. Calculating Indices

Spectral Indices are central to many aspects of remote sensing. Whether you are studying vegetation or tracking fires - you will need to compute a pixel-wise ratio of 2 or more bands. The most commonly used formula for calculating an index is the Normalized Difference between 2 bands. Earth Engine provides a helper function normalizedDifference() to help calculate normalized indices, such as Normalized Difference Vegetation Index (NDVI). For more complex formulae, you can also use the expression() function to describe the calculation.

MNDWI, SAVI and NDVI images

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var admin2 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2');

var bangalore = admin2.filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'));
var geometry = bangalore.geometry();

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

var image = filtered.median(); 

// Calculate  Normalized Difference Vegetation Index (NDVI)
// 'NIR' (B8) and 'RED' (B4)
var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);

// Calculate Modified Normalized Difference Water Index (MNDWI)
// 'GREEN' (B3) and 'SWIR1' (B11)
var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 

// Calculate Soil-adjusted Vegetation Index (SAVI)
// 1.5 * ((NIR - RED) / (NIR + RED + 0.5))

// For more complex indices, you can use the expression() function

// Note: 
// For the SAVI formula, the pixel values need to converted to reflectances
// Multiplyng the pixel values by 'scale' gives us the reflectance value
// The scale value is 0.0001 for Sentinel-2 dataset

var savi = image.expression(
    '1.5 * ((NIR - RED) / (NIR + RED + 0.5))', {
      'NIR': image.select('B8').multiply(0.0001),
      'RED': image.select('B4').multiply(0.0001),
}).rename('savi');

var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
var ndviVis = {min:0, max:1, palette: ['white', 'green']};
var ndwiVis = {min:0, max:0.5, palette: ['white', 'blue']};

Map.addLayer(image.clip(geometry), rgbVis, 'Image');
Map.addLayer(mndwi.clip(geometry), ndwiVis, 'mndwi');
Map.addLayer(savi.clip(geometry), ndviVis, 'savi');
Map.addLayer(ndvi.clip(geometry), ndviVis, 'ndvi');

Exercise

Try in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var admin2 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level2');

var bangalore = admin2.filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'));
var geometry = bangalore.geometry();
Map.centerObject(geometry);

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

var image = filtered.median(); 

// Exercise

// Calculate the Normalized Difference Built-Up Index (NDBI) for the image
// Hint: NDBI = (SWIR1 – NIR) / (SWIR1 + NIR)
// Visualize the built-up area using a 'red' palette

03. Computation on ImageCollections

So far we have learnt how to run computation on single images. If you want to apply some computation - such as calculating an index - to many images, you need to use map(). You first define a function that takes 1 image and returns the result of the computation on that image. Then you can map() that function over the ImageCollection which results in a new ImageCollection with the results of the computation. This is similar to a for-loop that you maybe familiar with - but using map() allows the computation to run in parallel. Learn more at Mapping over an ImageCollection

NDVI computation on an ImageCollection

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var admin1 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level1');
    
var karnataka = admin1.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'));
var geometry = karnataka.geometry();
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

var composite = filtered.median().clip(geometry);
Map.addLayer(composite, rgbVis, 'Karnataka Composite');


// Write a function that computes NDVI for an image and adds it as a band
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
}

// Map the function over the collection
var withNdvi = filtered.map(addNDVI);

var composite = withNdvi.median();

var ndviComposite = composite.select('ndvi').clip(karnataka);

var palette = [
  'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
  '74A901', '66A000', '529400', '3E8601', '207401', '056201',
  '004C00', '023B01', '012E01', '011D01', '011301'];

var ndviVis = {min:0, max:0.5, palette: palette };
Map.addLayer(ndviComposite, ndviVis, 'ndvi');

Exercise

Try in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var admin1 = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level1');
  
var karnataka = admin1.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'));
var geometry = karnataka.geometry();
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));

   
var composite = filtered.median().clip(geometry);
Map.addLayer(composite, rgbVis, 'Karnataka Composite');

// This function calculates both NDVI an d NDWI indices
// and returns an image with 2 new bands added to the original image.
function addIndices(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  var ndwi = image.normalizedDifference(['B3', 'B8']).rename('ndwi');
  return image.addBands(ndvi).addBands(ndwi);
}

// Map the function over the collection
var withIndices = filtered.map(addIndices);

// Composite
var composite = withIndices.median();
print(composite);

// Extract the 'ndwi' band and display a NDWI map
// use the palette ['white', 'blue']
// Hint: Use .select() function to select a band

04. Cloud Masking

Masking pixels in an image makes those pixels transparent and excludes them from analysis and visualization. To mask an image, we can use the updateMask() function and pass it an image with 0 and 1 values. All pixels where the mask image is 0 will be masked.

Most remote sensing datasets come with a QA or Cloud Mask band that contains the information on whether pixels is cloudy or not. Your Code Editor contains pre-defined functions for masking clouds for popular datasets under Scripts Tab → Examples → Cloud Masking.

The script below takes the Sentinel-2 masking function and shows how to apply it on an image.

Applying pixel-wise QA bitmask

Open in Code Editor ↗

var image = ee.Image('COPERNICUS/S2_HARMONIZED/20190703T050701_20190703T052312_T43PGP')
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};

Map.centerObject(image);
Map.addLayer(image, rgbVis, 'Full Image', false);

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask)
      .select('B.*')
      .copyProperties(image, ['system:time_start']);
}

var maskedImage = ee.Image(maskS2clouds(image));
Map.addLayer(maskedImage, rgbVis, 'Masked Image');

Exercise

Try in Code Editor ↗

// Get the Level-2A Surface Reflectance image
var imageSR = ee.Image('COPERNICUS/S2_SR_HARMONIZED/20190703T050701_20190703T052312_T43PGP');
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
Map.centerObject(imageSR);
Map.addLayer(imageSR, rgbVis, 'SR Image');


// Function to remove cloud and snow pixels from Sentinel-2 SR image
function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(5);
  var snow = snowProb.lt(5);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 5% or cloud shadow classification
  var mask = (cloud.and(snow)).and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}

// Exercise
// Apply the above cloud masking function to SR image
// Add the masked image to the map

// Hint: After adding the masked image to the map, turn-off
// the original image layer to see the result of the masking function

If you are using Sentinel-2 data, do check out the an alternative cloud masking techninque using the S2 Cloudless dataset. Learn more

var imageSR = ee.Image('COPERNICUS/S2_SR/20190703T050701_20190703T052312_T43PGP')
var s2Cloudless = ee.Image('COPERNICUS/S2_CLOUD_PROBABILITY/20190703T050701_20190703T052312_T43PGP')
var clouds = s2Cloudless.lt(50)
var cloudlessMasked = imageSR.mask(clouds)
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
Map.addLayer(cloudlessMasked, rgbVis, 'S2 Cloudless Masked Image')

05. Reducers

When writing parallel computing code, a Reduce operation allows you to compute statistics on a large amount of inputs. In Earth Engine, you need to run reduction operation when creating composites, calculating statistics, doing regression analysis etc. The Earth Engine API comes with a large number of built-in reducer functions (such as ee.Reducer.sum(), ee.Reducer.histogram(), ee.Reducer.linearFit() etc.) that can perform a variety of statistical operations on input data. You can run reducers using the reduce() function. Earth Engine supports running reducers on all data structures that can hold multiple values, such as Images (reducers run on different bands), ImageCollection, FeatureCollection, List, Dictionary etc. The script below introduces basic concepts related to reducers.

Open in Code Editor ↗

// Computing stats on a list
var myList = ee.List.sequence(1, 10);
print(myList)

// Use a reducer to compute min and max in the list
var mean = myList.reduce(ee.Reducer.mean());
print(mean);

var geometry = ee.Geometry.Polygon([[
  [82.60642647743225, 27.16350437805251],
  [82.60984897613525, 27.1618529901377],
  [82.61088967323303, 27.163695288375266],
  [82.60757446289062, 27.16517483230927]
]]);
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
Map.centerObject(geometry);

// Apply a reducer on a image collection
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .select('B.*');

print(filtered.size());
var collMean = filtered.reduce(ee.Reducer.mean());
print('Reducer on Collection', collMean);

var image = ee.Image('COPERNICUS/S2/20190223T050811_20190223T051829_T44RPR');
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
Map.addLayer(image, rgbVis, 'Image');
Map.addLayer(geometry, {color: 'red'}, 'Farm');
// If we want to compute the average value in each band,
// we can use reduceRegion instead
var stats = image.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: geometry,
  scale: 100,
  maxPixels: 1e10
  });
print(stats);

// Result of reduceRegion is a dictionary. 
// We can extract the values using .get() function
print('Average value in B4', stats.get('B4'));

Exercise

Try in Code Editor ↗

var geometry = ee.Geometry.Polygon([[
  [82.60642647743225, 27.16350437805251],
  [82.60984897613525, 27.1618529901377],
  [82.61088967323303, 27.163695288375266],
  [82.60757446289062, 27.16517483230927]
]]);
          
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
var image = ee.Image('COPERNICUS/S2_HARMONIZED/20190223T050811_20190223T051829_T44RPR');
Map.addLayer(image, rgbVis, 'Image');
Map.addLayer(geometry, {color: 'red'}, 'Farm');
Map.centerObject(geometry);

var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');

// Exercise
// Compute the average NDVI for the farm from the given image
// Hint: Use the reduceRegion() function

06. Time-Series Charts

Now we can put together all the skills we have learnt so far - filter, map, reduce, and cloud-masking to create a chart of average NDVI values for a given farm over 1 year. Earth Engine API comes with support for charting functions based on the Google Chart API. Here we use the ui.Chart.image.series() function to create a time-series chart.

Computing NDVI Time-series for a Farm

NDVI Time-series showing Dual-Cropping Cycle

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var geometry = ee.Geometry.Polygon([[
  [82.60642647743225, 27.16350437805251],
  [82.60984897613525, 27.1618529901377],
  [82.61088967323303, 27.163695288375266],
  [82.60757446289062, 27.16517483230927]
]]);
Map.addLayer(geometry, {color: 'red'}, 'Farm');
Map.centerObject(geometry);
var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};

var filtered = s2
  .filter(ee.Filter.date('2017-01-01', '2018-01-01'))
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry));

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask)//.divide(10000)
      .select('B.*')
      .copyProperties(image, ['system:time_start']);
}

var filtered = filtered.map(maskS2clouds);
// Write a function that computes NDVI for an image and adds it as a band
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
}

// Map the function over the collection
var withNdvi = filtered.map(addNDVI);


// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: withNdvi.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 10
}).setOptions({
      lineWidth: 1,
      title: 'NDVI Time Series',
      interpolateNulls: true,
      vAxis: {title: 'NDVI'},
      hAxis: {title: '', format: 'YYYY-MMM'}
    })
print(chart);

Exercise

Try in Code Editor ↗

// Delete the farm boundary from the previous script 
// and add another farm at a location of your choice

// Print the chart.

Assignment 2

Assignment2 Expected Output

Try in Code Editor ↗

var terraclimate = ee.ImageCollection("IDAHO_EPSCOR/TERRACLIMATE");
var geometry = ee.Geometry.Point([77.54849920033682, 12.91215102400037]);
    
// Assignment
// Use Gridded Climate dataset to chart a 40+ year time series
// if temparature at any location


// Workflow
// Load the TerraClimate collection
// Select the 'tmmx' band
// Scale the band values
// Filter the scaled collection
// Use ui.Chart.image.series() function to create the chart


// Hint1
// Data needed to be scaled by 0.1
// map() a function and multiply each image
// Multiplying creates a new image that doesn't have the same properties
// Use copyProperties() function to copy timestamp to new image
var tmax = terraclimate.select('tmmx')
var tmaxScaled = tmax.map(function(image) {
  return image.multiply(0.1)
    .copyProperties(image,['system:time_start']);
})

// Hint2
// You will need to specify a scale in meters for charting
// Use projection().nominalScale() to find the 
// image resolution in meters
var image = ee.Image(terraclimate.first())
print(image.projection().nominalScale())

Module 3: Supervised Classification

Introduction to Machine Learning and Supervised Classification

Supervised classification is arguably the most important classical machine learning techniques in remote sensing. Applications range from generating Land Use/Land Cover maps to change detection. Google Earth Engine is unique suited to do supervised classification at scale. The interactive nature of Earth Engine development allows for iterative development of supervised classification workflows by combining many different datasets into the model. This module covers basic supervised classification workflow, accuracy assessment, hyperparameter tuning and change detection.

View the Presentation ↗

01. Basic Supervised Classification

We will learn how to do a basic land cover classification using training samples collected from the Code Editor using the High Resolution basemap imagery provided by Google Maps. This method requires no prior training data and is quite effective to generate high quality classification samples anywhere in the world. The goal is to classify each source pixel into one of the following classes - urban, bare, water or vegetation. Using the drawing tools in the code editor, you create 4 new feature collection with points representing pixels of that class. Each feature collection has a property called landcover with values of 0, 1, 2 or 3 indicating whether the feature collection represents urban, bare, water or vegetation respectively. We then train a Random Forest classifier using these training set to build a model and apply it to all the pixels of the image to create a 4 class image.

Fun fact: The classifiers in Earth Engine API have names starting with smile - such as ee.Classifier.smileRandomForest(). The smile part refers to the Statistical Machine Intelligence and Learning Engine (SMILE) JAVA library which is used by Google Earth Engine to implement these algorithms.

Supervised Classification Output

Open in Code Editor ↗

var bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary');
var geometry = bangalore.geometry();

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
// The following collections were created using the 
// Drawing Tools in the code editor 
var urban = ee.FeatureCollection('users/ujavalgandhi/e2e/urban_gcps');
var bare = ee.FeatureCollection('users/ujavalgandhi/e2e/bare_gcps');
var water = ee.FeatureCollection('users/ujavalgandhi/e2e/water_gcps');
var vegetation = ee.FeatureCollection('users/ujavalgandhi/e2e/vegetation_gcps');

var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .select('B.*');

var composite = filtered.median().clip(geometry);

// Display the input composite.
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
Map.addLayer(composite, rgbVis, 'image');

var gcps = urban.merge(bare).merge(water).merge(vegetation);

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: gcps, 
  properties: ['landcover'], 
  scale: 10
});


// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50).train({
  features: training,  
  classProperty: 'landcover', 
  inputProperties: composite.bandNames()
});
// // Classify the image.
var classified = composite.classify(classifier);
Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019'); 

// Display the GCPs
// We use the style() function to style the GCPs
var palette = ee.List(['gray','brown','blue','green'])
var landcover = ee.List([0, 1, 2, 3])

var gcpsStyled = ee.FeatureCollection(
  landcover.map(function(lc){
    var color = palette.get(landcover.indexOf(lc));
    var markerStyle = { color: 'white', pointShape: 'diamond', 
      pointSize: 4, width: 1, fillColor: color}
    return gcps.filter(ee.Filter.eq('landcover', lc))
                .map(function(point){
                  return point.set('style', markerStyle)
                })
      })).flatten();
      
Map.addLayer(gcpsStyled.style({styleProperty:"style"}), {}, 'GCPs')
Map.centerObject(gcpsStyled)

Exercise

Try in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
var urbanAreas = ee.FeatureCollection('users/ujavalgandhi/e2e/ne_10m_urban_areas');

// Perform supervised classification for your city
// Find the feature id by adding the layer to the map and using Inspector.
var city = urbanAreas.filter(ee.Filter.eq('system:index', '00000000000000002bf8'));
var geometry = city.geometry();
Map.centerObject(geometry);

var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .select('B.*');

var composite = filtered.median().clip(geometry);

// Display the input composite.

var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
Map.addLayer(composite, rgbVis, 'image');

// Exercise
// Add training points for 4 classes
// Assign the 'landcover' property as follows

// urban: 0
// bare: 1
// water: 2
// vegetation: 3

// After adding points, uncomments lines below

// var gcps = urban.merge(bare).merge(water).merge(vegetation);

// // Overlay the point on the image to get training data.
// var training = composite.sampleRegions({
//   collection: gcps, 
//   properties: ['landcover'], 
//   scale: 10,
//   tileScale: 16
// });
// print(training);


// // Train a classifier.
// var classifier = ee.Classifier.smileRandomForest(50).train({
//   features: training,  
//   classProperty: 'landcover', 
//   inputProperties: composite.bandNames()
// });
// // // Classify the image.
// var classified = composite.classify(classifier);
// Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019'); 

02. Accuracy Assessment

It is important to get a quantitative estimate of the accuracy of the classification. To do this, a common strategy is to divide your training samples into 2 random fractions - one used for training the model and the other for validation of the predictions. Once a classifier is trained, it can be used to classify the entire image. We can then compare the classified values with the ones in the validation fraction. We can use the ee.Classifier.confusionMatrix() method to calculate a Confusion Matrix representing expected accuracy.

Classification results are evaluated based on the following metrics

• Overall Accuracy: How many samples were classified correctly.
• Producer’s Accuracy: How well did the classification predict each class.
• Consumer’s Accuracy (Reliability): How reliable is the prediction in each class.
• Kappa Coefficient: How well the classification performed as compared to random assignment.

Accuracy Assessment

Don’t get carried away tweaking your model to give you the highest validation accuracy. You must use both qualitative measures (such as visual inspection of results) along with quantitative measures to assess the results.

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
var basin = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_7");
var gcp = ee.FeatureCollection("users/ujavalgandhi/e2e/arkavathy_gcps");
    
var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640));
var geometry = arkavathy.geometry();
Map.centerObject(geometry);

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
 
var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .select('B.*');

var composite = filtered.median().clip(geometry);

// Display the input composite.
Map.addLayer(composite, rgbVis, 'image');


// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn();

// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

// Classify the image.
var classified = composite.classify(classifier);

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

//************************************************************************** 
// Accuracy Assessment
//************************************************************************** 

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
var test = classified.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  tileScale: 16,
  scale: 10,
});

var testConfusionMatrix = test.errorMatrix('landcover', 'classification')
// Printing of confusion matrix may time out. Alternatively, you can export it as CSV
print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy());

// Alternate workflow 
// This is similar to machine learning practice
var validation = composite.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});

var test = validation.classify(classifier);

var testConfusionMatrix = test.errorMatrix('landcover', 'classification')
// Printing of confusion matrix may time out. Alternatively, you can export it as CSV
print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy());

Exercise

Try in Code Editor ↗

var classified = ee.Image('users/ujavalgandhi/e2e/arkavathy_base_classification');
var gcp = ee.FeatureCollection('users/ujavalgandhi/e2e/arkavathy_gcps');
var gcp = gcp.randomColumn();

var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));

//************************************************************************** 
// Accuracy Assessment
//************************************************************************** 

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
var test = classified.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  tileScale: 16,
  scale: 10,
});

var testConfusionMatrix = test.errorMatrix('landcover', 'classification');
print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy());

// Exercise

// Calculate and print the following assessment metrics
// 1. Producer's accuracy
// 2. Consumer's accuracy
// 3. Kappa coefficient

// Hint: Look at the ee.ConfusionMatrix module for appropriate methods

03. Improving the Classification

The Earth Engine data-model is especially well suited for machine learning tasks because of its ability to easily incorporate data sources of different spatial resolutions, projections and data types together By giving additional information to the classifier, it is able to separate different classes easily. Here we take the same example and augment it with the following techniques

• Apply Cloud Masking
• Add Spectral Indices: We add bands for different spectral indices such as - NDVI, NDBI, MNDWI and BSI.
• Add Elevation and Slope: We also add slope and elevation bands from the ALOS DEM.
• Normalize the Inputs: Machine learning models work best when all the inputs have the same scale. We will divide each band with the maximum value. This method ensures that all input values are between 0-1. A more complete and robust technique for image normalization is provided in the course Supplement.

Our training features have more parameters and contain values of the same scale. The result is a much improved classification.

Improved Classification Accuracy with use of Spectral Indices and Elevation Data

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
var basin = ee.FeatureCollection('WWF/HydroSHEDS/v1/Basins/hybas_7');
var gcp = ee.FeatureCollection('users/ujavalgandhi/e2e/arkavathy_gcps');
var alos = ee.Image('JAXA/ALOS/AW3D30/V2_2');


var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640));
var geometry = arkavathy.geometry();
Map.centerObject(geometry);

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
// Function to remove cloud and snow pixels from Sentinel-2 SR image

function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(10);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 10% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}


var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .map(maskCloudAndShadowsSR)
  .select('B.*');

var composite = filtered.median().clip(geometry);


var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi);
};

var composite = addIndices(composite);


// Calculate Slope and Elevation
var elev = alos.select('AVE_DSM').rename('elev');
var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');

var composite = composite.addBands(elev).addBands(slope);

var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
Map.addLayer(composite, visParams, 'RGB');

// Normalize the image 

// Machine learning algorithms work best on images when all features have
// the same range

// Function to Normalize Image
// Pixel Values should be between 0 and 1
// Formula is (x - xmin) / (xmax - xmin)
//************************************************************************** 
function normalize(image){
  var bandNames = image.bandNames();
  // Compute min and max of the image
  var minDict = image.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var maxDict = image.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var mins = ee.Image.constant(minDict.values(bandNames));
  var maxs = ee.Image.constant(maxDict.values(bandNames));

  var normalized = image.subtract(mins).divide(maxs.subtract(mins));
  return normalized;
}

var composite = normalize(composite);
// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn();

// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});
print(training);

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

// Classify the image.
var classified = composite.classify(classifier);

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

//************************************************************************** 
// Accuracy Assessment
//************************************************************************** 

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
var test = classified.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});

var testConfusionMatrix = test.errorMatrix('landcover', 'classification');

// Printing of confusion matrix may time out. Alternatively, you can export it as CSV
print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy());
 

Exercise

Try in Code Editor ↗

// Exercise

// Improve your classification from Exercise 01c 
// Add different spectral indicies to your composite
// by using the function below

var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi);
};

 

04. Exporting Classification Results

When working with complex classifiers over large regions, you may get a User memory limit exceeded or Computation timed out error in the Code Editor. The reason for this is that there is a fixed time limit and smaller memory allocated for code that is run with the On-Demand Computation mode. For larger computations, you can use the Batch mode with the Export functions. Exports run in the background and can run longer than 5-minutes time allocated to the computation code run from the Code Editor. This allows you to process very large and complex datasets. Here’s an example showing how to export your classification results to Google Drive.

We can only export Images or FeatureCollections. What if you wanted to export a number that is the result of a long computation? A useful hack is to create a FeatureCollection with just 1 feature containing null geometry and a property containing the number you want to export.

Exported Classification Outputs

Open in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
var basin = ee.FeatureCollection('WWF/HydroSHEDS/v1/Basins/hybas_7');
var gcp = ee.FeatureCollection('users/ujavalgandhi/e2e/arkavathy_gcps');
var alos = ee.Image('JAXA/ALOS/AW3D30/V2_2');


var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))
var geometry = arkavathy.geometry()

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
// Function to remove cloud and snow pixels from Sentinel-2 SR image

function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(10);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 10% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}


var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .map(maskCloudAndShadowsSR)
  .select('B.*');

var composite = filtered.median().clip(geometry) ;

var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi);
};

var composite = addIndices(composite);


// Calculate Slope and Elevation
var elev = alos.select('AVE_DSM').rename('elev');
var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');

var composite = composite.addBands(elev).addBands(slope);

var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
Map.addLayer(composite, visParams, 'RGB');

// Normalize the image 

// Machine learning algorithms work best on images when all features have
// the same range

// Function to Normalize Image
// Pixel Values should be between 0 and 1
// Formula is (x - xmin) / (xmax - xmin)
//************************************************************************** 
function normalize(image){
  var bandNames = image.bandNames();
  // Compute min and max of the image
  var minDict = image.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: geometry,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var maxDict = image.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var mins = ee.Image.constant(minDict.values(bandNames));
  var maxs = ee.Image.constant(maxDict.values(bandNames));

  var normalized = image.subtract(mins).divide(maxs.subtract(mins));
  return normalized;
}

var composite = normalize(composite);
// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn();

// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));
Map.addLayer(validationGcp);

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});
print(training);

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

// Classify the image.
var classified = composite.classify(classifier);

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

//************************************************************************** 
// Accuracy Assessment
//************************************************************************** 

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
var test = classified.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});

var testConfusionMatrix = test.errorMatrix('landcover', 'classification');

print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy()); 

//************************************************************************** 
// Exporting Results
//************************************************************************** 

// Create a Feature with null geometry and the value we want to export.
// Use .array() to convert Confusion Matrix to an Array so it can be
// exported in a CSV file
var fc = ee.FeatureCollection([
  ee.Feature(null, {
    'accuracy': testConfusionMatrix.accuracy(),
    'matrix': testConfusionMatrix.array()
  })
]);

print(fc);

Export.table.toDrive({
  collection: fc,
  description: 'Accuracy_Export',
  folder: 'earthengine',
  fileNamePrefix: 'accuracy',
  fileFormat: 'CSV'
});

Exercise

It is also a good idea to export the classified image as an Asset. This will allows you to import the classified image in another script without running the whole classification workflow. Use the Export.image.toAsset() function to export the classified image as an asset.

Try in Code Editor ↗

var s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
var basin = ee.FeatureCollection('WWF/HydroSHEDS/v1/Basins/hybas_7');
var gcp = ee.FeatureCollection('users/ujavalgandhi/e2e/arkavathy_gcps');
var alos = ee.Image('JAXA/ALOS/AW3D30/V2_2');


var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))
var geometry = arkavathy.geometry()

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
// Function to remove cloud and snow pixels from Sentinel-2 SR image

function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(10);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 10% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}


var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .map(maskCloudAndShadowsSR)
  .select('B.*');

var composite = filtered.median().clip(geometry) ;

var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi);
};

var composite = addIndices(composite);


// Calculate Slope and Elevation
var elev = alos.select('AVE_DSM').rename('elev');
var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');

var composite = composite.addBands(elev).addBands(slope);

var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
Map.addLayer(composite, visParams, 'RGB');

// Normalize the image 

// Machine learning algorithms work best on images when all features have
// the same range

// Function to Normalize Image
// Pixel Values should be between 0 and 1
// Formula is (x - xmin) / (xmax - xmin)
//************************************************************************** 
function normalize(image){
  var bandNames = image.bandNames();
  // Compute min and max of the image
  var minDict = image.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: geometry,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var maxDict = image.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var mins = ee.Image.constant(minDict.values(bandNames));
  var maxs = ee.Image.constant(maxDict.values(bandNames));

  var normalized = image.subtract(mins).divide(maxs.subtract(mins));
  return normalized;
}

var composite = normalize(composite);
// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn();

// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));
Map.addLayer(validationGcp);

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});
print(training);

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

// Classify the image.
var classified = composite.classify(classifier);

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

// Exercise 

// Use the Export.image.toAsset() function to export the 
// classified image as a Earth Engine Asset.

// This will allows you to import the classified image in another script
// without running the whole classification workflow.

// Hint: For images with discrete pixel values, we must set the
// pyramidingPolicy to 'mode'.
// The pyramidingPolicy parameter should a dictionary specifying
// the policy for each band. A simpler way to specify it for all
// bands is to use {'.default': 'mode'}

05. Calculating Area

Now that we have the results of our classification, we will learn how to calculate the area for pixels in each class. Calculating area for features is done using the area() function and for images using the ee.Image.pixelArea() function. The ee.Image.pixelArea() function creates an image where each pixel’s value is the area of the pixel. We multiply this pixel area image with our image and sum up the area using the reduceRegion() function.

The ee.Image.pixelArea() function uses a custom equal-area projection for area calculation. The result is area in square meters regardless of the projection of the input image. Learn more

Calculating Green Cover from Classified Image

Open in Code Editor ↗

var classified = ee.Image('users/ujavalgandhi/e2e/bangalore_classified');
var bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary');

Map.addLayer(bangalore, {color: 'blue'}, 'Bangalore City');

Map.addLayer(classified,
  {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']},
  'Classified Image 2019');

// Calling .geometry() on a feature collection gives the
// dissolved geometry of all features in the collection

// .area() function calculates the area in square meters
var cityArea = bangalore.geometry().area();

// We can cast the result to a ee.Number() and calculate the
// area in square kilometers
var cityAreaSqKm = ee.Number(cityArea).divide(1e6).round();
print(cityAreaSqKm);


// Area Calculation for Images
var vegetation = classified.eq(3);
// If the image contains values 0 or 1, we can calculate the
// total area using reduceRegion() function

// The result of .eq() operation is a binary image with pixels
// values of 1 where the condition matched and 0 where it didn't
Map.addLayer(vegetation, {min:0, max:1, palette: ['white', 'green']}, 'Green Cover');

// Since our image has only 0 and 1 pixel values, the vegetation
// pixels will have values equal to their area
var areaImage = vegetation.multiply(ee.Image.pixelArea());


// Now that each pixel for vegetation class in the image has the value
// equal to its area, we can sum up all the values in the region
// to get the total green cover.

var area = areaImage.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: bangalore.geometry(),
  scale: 10,
  maxPixels: 1e10
});
// The result of the reduceRegion() function is a dictionary with the key
// being the band name. We can extract the area number and convert it to
// square kilometers
var vegetationAreaSqKm = ee.Number(area.get('classification')).divide(1e6).round();
print(vegetationAreaSqKm);

If you want to compute area covered by each class, you can use a Grouped Reducer. See the Supplement to see a code snippet.

Exercise

Try in Code Editor ↗

// Exercise
// Compute and print the percentage green cover of the city

Module 4: Change Detection

Introduction to Change Detection

Many earth observation datasets are available at regular intervals over long periods of time. This enables us to detect changes on the Earth’s surface. Change detection technique in remote sensing fall in the following categories

• Single Band Change: Measuring change in a single band image or a spectral index using a threshold
• Multi Band Change: Measuring spectral distance and spectral angle between two multiband images
• Classification of Change: One-pass classification using stacked image containing bands from before and after an event
• Post Classification Comparison: Comparing two classified images and computing class transitions

In the following sections, we will apply the supervised classification techniques for change detection using multi-temporal images.

View the Presentation ↗

01. Spectral Index Change

Many types of change can be detected by measuring the change in a spectral index and applying a threshold. This technique is suitable when there is a suitable spectral index is available for the type of change you are interested in detecting.

Here we apply this technique to map the extent and severity of a forest fire. The Normalized Burn Ratio (NBR) is an index that is designed to highlight burnt vegetation areas. We compute the NBR for before and after images. Then we apply a suitable threshold to find burnt areas.

Spectral Index Change Detection

Open in Code Editor ↗

// On 21st February 2019, massive forest fires broke out in
// numerous places across the Bandipur National Park of
// the Karnataka state in India.
// By 25 February 2019 most fire was brought under control
// This script shows how to do damage assessment using
// spectral index change detection technique.

// Define the area of interest
var geometry = ee.Geometry.Polygon([[
  [76.37639666685044, 11.766523239445169],
  [76.37639666685044, 11.519036946599561],
  [76.78426409849106, 11.519036946599561],
  [76.78426409849106, 11.766523239445169]
]]);
var fireStart = ee.Date('2019-02-20');
var fireEnd = ee.Date('2019-02-25');

Map.centerObject(geometry, 10)

var s2 = ee.ImageCollection("COPERNICUS/S2")

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
} 

// Apply filters and cloud mask
var filtered = s2
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .select('B.*')

// Create Before and After composites
var before = filtered
  .filter(ee.Filter.date(
    fireStart.advance(-2, 'month'), fireStart))
  .median()

var after = filtered
  .filter(ee.Filter.date(
    fireEnd, fireEnd.advance(1, 'month')))
  .median()

// Freshly burnt regions appeat bright in SWIR-bands
// Use a False Color Visualization
var swirVis = {
  min: 0.0,
  max: 3000,
  bands: ['B12', 'B8', 'B4'],
};
Map.addLayer(before, swirVis, 'Before')
Map.addLayer(after, swirVis, 'After')

// Write a function to calculate  Normalized Burn Ratio (NBR)
// 'NIR' (B8) and 'SWIR-2' (B12)
var addNBR = function(image) {
  var nbr = image.normalizedDifference(['B8', 'B12']).rename(['nbr']);
  return image.addBands(nbr)
}

var beforeNbr = addNBR(before).select('nbr');
var afterNbr = addNBR(after).select('nbr');

var nbrVis = {min: -0.5, max: 0.5, palette: ['white', 'black']}

Map.addLayer(beforeNbr, nbrVis, 'Prefire NBR');
Map.addLayer(afterNbr, nbrVis, 'Postfire NBR');

// Calculate Change in NBR (dNBR)
var change = beforeNbr.subtract(afterNbr)

// Apply a threshold
var threshold = 0.3

// Display Burned Areas
var burned = change.gt(threshold)
Map.addLayer(burned, {min:0, max:1, palette: ['white', 'red']}, 'Burned', false) 

Exercise

Classifying the Change Image

Try in Code Editor ↗

// Define the area of interest
var geometry = ee.Geometry.Polygon([[
  [76.37639666685044, 11.766523239445169],
  [76.37639666685044, 11.519036946599561],
  [76.78426409849106, 11.519036946599561],
  [76.78426409849106, 11.766523239445169]
]]);
var fireStart = ee.Date('2019-02-20');
var fireEnd = ee.Date('2019-02-25');

Map.centerObject(geometry, 10)

var s2 = ee.ImageCollection("COPERNICUS/S2")

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
} 

// Apply filters and cloud mask
var filtered = s2
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .select('B.*')

// Create Before and After composites
var before = filtered
  .filter(ee.Filter.date(
    fireStart.advance(-2, 'month'), fireStart))
  .median()

var after = filtered
  .filter(ee.Filter.date(
    fireEnd, fireEnd.advance(1, 'month')))
  .median()


// Write a function to calculate  Normalized Burn Ratio (NBR)
// 'NIR' (B8) and 'SWIR-2' (B12)
var addNBR = function(image) {
  var nbr = image.normalizedDifference(['B8', 'B12']).rename(['nbr']);
  return image.addBands(nbr)
}

var beforeNbr = addNBR(before).select('nbr');
var afterNbr = addNBR(after).select('nbr');

// Calculate Change in NBR (dNBR)
var change = beforeNbr.subtract(afterNbr)

var dnbrPalette = ['#ffffb2','#fecc5c','#fd8d3c','#f03b20','#bd0026'];
// Display the change image
Map.addLayer(change, {min:0.1, max: 0.7, palette: dnbrPalette},
  'Change in NBR')

// We can also classify the change image according to
// burn severity

// United States Geological Survey (USGS) proposed
// a classification table to interpret the burn severity
// We will assign a discrete class value and visualize it
// | Severity     | dNBR Range         | Class |
// |--------------|--------------------|-------|
// | Unburned     | < 0.1              | 0     |
// | Low Severity | >= 0.10 and <0.27  | 1     |
// | Moderate-Low | >= 0.27 and <0.44  | 2     |
// | Moderate-High| >= 0.44 and< 0.66  | 3     |
// | High         | >= 0.66            | 4     |

// Classification of continuous values can be done
// using the .where() function
var severity = change
  .where(change.lt(0.10), 0)
  .where(change.gte(0.10).and(change.lt(0.27)), 1)
  .where(change.gte(0.27).and(change.lt(0.44)), 2)
  .where(change.gte(0.44).and(change.lt(0.66)), 3)
  .where(change.gt(0.66), 4)


// Exercise

// The resulting image 'severity' is a discrete image with 
// pixel values from 0-4 representing the severity class

// Display the image according to the following color table

// | Severity     | Class | Color   |
// |--------------|-------|---------|
// | Unburned     | 0     | green   |
// | Low Severity | 1     | yellow  |
// | Moderate-Low | 2     | organge |
// | Moderate-High| 3     | red     |
// | High         | 4     | magenta |

02. Spectral Distance Change

When you want to detect changes from multi-band images, a useful technique is to compute the Spectral Distance and Spectral Angle between the two images. Pixels that exhibit a large change will have a larger distance compared to those that did not change. This technique is particularly useful when there are no suitable index to detect the change. It can be applied to detect change after natural disasters or human conflicts.

Here we use this technique to detect landslides using before/after composites. You may learn more about this technique at Craig D’Souza’s Change Detection presentation.

Spectral Distance Change Detection

Open in Code Editor ↗

var geometry = ee.Geometry.Polygon([[
  [75.70357667713435, 12.49723970868507],
  [75.70357667713435, 12.470171844429931],
  [75.7528434923199, 12.470171844429931],
  [75.7528434923199, 12.49723970868507]
]]);
Map.centerObject(geometry)
var s2 = ee.ImageCollection("COPERNICUS/S2")
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
} 

var filtered = s2
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .select('B.*')
  
var dateOfIncident = ee.Date('2018-12-15')
var before = filtered
  .filter(ee.Filter.date(
    dateOfIncident.advance(-2, 'year'), dateOfIncident))
  .filter(ee.Filter.calendarRange(12, 12, 'month'))
  .median()
  
var after = filtered
  .filter(ee.Filter.date(
    dateOfIncident, dateOfIncident.advance(1, 'month')))
  .median()


Map.addLayer(before, rgbVis, 'Before')
Map.addLayer(after, rgbVis, 'After')

// Use the spectralDistnace() function to get spectral distance measures

// Use the metric 'Spectral Angle Mapper (SAM)
// The result is the spectral angle in radians
var angle = after.spectralDistance(before, 'sam');
Map.addLayer(angle, {min: 0, max: 1, palette: ['white', 'purple']}, 'Spectral Angle');

// Use the metric 'Squared Euclidian Distance (SED)'
var sed = after.spectralDistance(before, 'sed');
// Take square root to get euclidian distance
var distance = sed.sqrt();

Map.addLayer(distance, {min: 0, max: 1500, palette: ['white', 'red']}, 'spectral distance');

Exercise

Try in Code Editor ↗

var geometry = ee.Geometry.Polygon([[
  [75.70357667713435, 12.49723970868507],
  [75.70357667713435, 12.470171844429931],
  [75.7528434923199, 12.470171844429931],
  [75.7528434923199, 12.49723970868507]
]]);
Map.centerObject(geometry)
var s2 = ee.ImageCollection("COPERNICUS/S2")
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
} 

var filtered = s2
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .select('B.*')
  
var dateOfIncident = ee.Date('2018-12-15')
var before = filtered
  .filter(ee.Filter.date(
    dateOfIncident.advance(-2, 'year'), dateOfIncident))
  .filter(ee.Filter.calendarRange(12, 12, 'month'))
  .median()
var after = filtered.filter(ee.Filter.date(
  dateOfIncident, dateOfIncident.advance(1, 'month'))).median()


Map.addLayer(before, rgbVis, 'Before')
Map.addLayer(after, rgbVis, 'After')

var sed = after.spectralDistance(before, 'sed');
var distance = sed.sqrt();

Map.addLayer(distance, {min: 0, max: 1500, palette: ['white', 'red']}, 'spectral distance');

// Exercise
// Inspect the distance image and find a suitable threshold
// that signifies damage after the landslides
// Apply the threshold and create a new image showing landslides
// Display the results

// Hint: Use the .gt() method to apply the threshold

03. Direct Classification of Change

This technique of change detection is also known as One-pass Classification or Direct Multi-date Classification. Here we create a single stacked image containing bands from before and after images. We train a classifier with training data sampled from the stacked image and apply the classifier on the stacked image to find all change pixels.

All pixels that changed from bare ground to built-up

Open in Code Editor ↗

var bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary');
var change = ee.FeatureCollection('users/ujavalgandhi/e2e/bangalore_change_gcps');
var nochange = ee.FeatureCollection('users/ujavalgandhi/e2e/bangalore_nochange_gcps')
var s2 = ee.ImageCollection("COPERNICUS/S2")

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
} 

var filtered = s2
  .filter(ee.Filter.date('2019-01-01', '2019-02-01'))
  .filter(ee.Filter.bounds(bangalore))
  .map(maskS2clouds)

  
var image2019 = filtered.median().clip(bangalore)
// Display the input composite.
Map.addLayer(image2019, rgbVis, '2019');

var filtered = s2
  .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
  .filter(ee.Filter.bounds(bangalore))
  .map(maskS2clouds)

var image2020 = filtered.median().clip(bangalore)

Map.addLayer(image2020, rgbVis, '2020');

var stackedImage = image2019.addBands(image2020)

// Overlay the point on the image to get training data.
var training = stackedImage.sampleRegions({
  collection: change.merge(nochange), 
  properties: ['class'], 
  scale: 10
});

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50).train({
  features: training,  
  classProperty: 'class', 
  inputProperties: stackedImage.bandNames()
});

// Classify the image.
var classified = stackedImage.classify(classifier);
Map.addLayer(classified, {min: 0, max: 1, palette: ['white', 'red']}, 'change'); 

Exercise

Try in Code Editor ↗

// Add an NDBI band to improve the detection of changes.

var addNDBI = function(image) {
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  return image.addBands(ndbi)
}

// use addNDBI() function to add the NDBI band to both 2019 and 2020 composite images
// Hint1: You can save the resulting image in the same variable to avoid changing 
// a lot of code.
// var image = addNDBI(image)

04. Post-classification Comparison

We dealing with multi-class images, a useful metric for change detection is to know how many pixels from class X changed to class Y. This can be accomplished using the ee.Reducer.frequencyHistogram() reducer as shown below.

Open in Code Editor ↗

var bangalore = ee.FeatureCollection("users/ujavalgandhi/public/bangalore_boundary")
var urban = ee.FeatureCollection("users/ujavalgandhi/e2e/urban_gcps")
var bare = ee.FeatureCollection("users/ujavalgandhi/e2e/bare_gcps")
var water = ee.FeatureCollection("users/ujavalgandhi/e2e/water_gcps")
var vegetation = ee.FeatureCollection("users/ujavalgandhi/e2e/vegetation_gcps")
var s2 = ee.ImageCollection("COPERNICUS/S2_SR")

Map.centerObject(bangalore)
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'], 
};

// 2019 Jan
var filtered = s2
  .filter(ee.Filter.date('2019-01-01', '2019-02-01'))
  .filter(ee.Filter.bounds(bangalore))
  .select('B.*')

  
var before = filtered.median().clip(bangalore)
// Display the input composite.
Map.addLayer(before, rgbVis, 'before');

var training = urban.merge(bare).merge(water).merge(vegetation)

// Overlay the point on the image to get training data.
var training = before.sampleRegions({
  collection: training, 
  properties: ['landcover'], 
  scale: 10
});

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50).train({
  features: training,  
  classProperty: 'landcover', 
  inputProperties: before.bandNames()
});

// // Classify the image.
var beforeClassified = before.classify(classifier);
Map.addLayer(beforeClassified,
  {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, 'before_classified');


// 2020 Jan
var after = s2
  .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
  .filter(ee.Filter.bounds(bangalore))
  .select('B.*')
  .median()
  .clip(bangalore)

Map.addLayer(after, rgbVis, 'after');

// Classify the image.
var afterClassified= after.classify(classifier);
Map.addLayer(afterClassified,
  {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, 'after_classified');

// Reclassify from 0-3 to 1-4
var beforeClasses = beforeClassified.remap([0, 1, 2, 3], [1, 2, 3, 4])
var afterClasses = afterClassified.remap([0, 1, 2, 3], [1, 2, 3, 4])

// Show all changed areas
var changed = afterClasses.subtract(beforeClasses).neq(0)
Map.addLayer(changed, {min:0, max:1, palette: ['white', 'red']}, 'Change')

// We multiply the before image with 100 and add the after image
// The resulting pixel values will be unique and will represent each unique transition
// i.e. 102 is urban to bare, 103 urban to water etc.
var merged = beforeClasses.multiply(100).add(afterClasses).rename('transitions')

// Use a frequencyHistogram to get a pixel count per class
var transitionMatrix = merged.reduceRegion({
  reducer: ee.Reducer.frequencyHistogram(), 
  geometry: bangalore,
  maxPixels: 1e10,
  scale:10,
  tileScale: 16
})
// This prints number of pixels for each class transition
print(transitionMatrix.get('transitions'))

// If we want to calculate the area of each class transition
// we can use a grouped reducer

// Divide by 1e6 to get the area in sq.km.
var areaImage = ee.Image.pixelArea().divide(1e6).addBands(merged);
// Calculate Area by each Transition Class
// using a Grouped Reducer
var areas = areaImage.reduceRegion({
      reducer: ee.Reducer.sum().group({
      groupField: 1,
      groupName: 'transitions',
    }),
    geometry: bangalore,
    scale: 100,
    tileScale: 4,
    maxPixels: 1e10
    }); 

// Post-process the result to generate a clean output
var classAreas = ee.List(areas.get('groups'))
var classAreaLists = classAreas.map(function(item) {
      var areaDict = ee.Dictionary(item)
      var classNumber = ee.Number(areaDict.get('transitions')).format()
      var area = ee.Number(areaDict.get('sum')).round()
      return ee.List([classNumber, area])
    })
var classTransitionsAreaDict = ee.Dictionary(classAreaLists.flatten())
print(classTransitionsAreaDict)

Exercise

Lost water pixels between 2019 and 2020

Try in Code Editor ↗

// Exercise
// Show all areas where water became other classes and display the result
// Hint1: Select class 3 pixels from before image and NOT class 3 pixels from after image
// Hint2: use the .and() operation to select pixels matching both conditions

Module 5: Earth Engine Apps

This module is focused the concepts related to client vs. server that will help you in creating web apps. We will be building an app using the Earth Engine User Interface API and publishing it to Google Cloud.

01. Client vs. Server

The User Interface elements in your Code Editor - Map View, Drawing Tools etc. are ‘client-side’ elements. They run in YOUR browser. Image Collections, feature collections, calculations on Earth Engine objects etc. are ‘server-side’ elements. They run in Google’s data center. You cannot mix both these objects. To learn more, visit the Client vs. Server section of the Earth Engine User Guide.

• To convert client-side objects to server-side objects, you can use the appropriate API function. Server-side functions start with ee., such ee.Date(), ee.Image() etc.
• To convert server-side objects to client-side objects, you can call .getInfo() on am Earth Engine object. For the Python API, this is the only way to extract information from a server-side object, but the Javascript API provides a better (and preferred) - method for bring server-side objects to client-side using the evaluate() method. This method asynchronously retrieves the value of the object, without blocking the user interface - meaning it will let your code continue to execute while it fetches the value.

Tip: You can use ee.Algorithms.ObjectType() to get the type of a server-side object

Open in Code Editor ↗

var date = '2020-01-01' // This is client-side
print(typeof(date))

var eedate = ee.Date('2020-01-01').format() // This is server-side
print(typeof(eedate)) 

// To bring server-side objects to client-side, you can call .getInfo()

// var clientdate = eedate.getInfo()
// print(clientdate)
// print(typeof(clientdate)) 

// getInfo() blocks the execution of your code till the value is fetched
// If the value takes time to compute, your code editor will freeze
// This is not a good user experience
var s2 = ee.ImageCollection("COPERNICUS/S2_SR")
var filtered = s2.filter(ee.Filter.date('2020-01-01', '2021-01-01'))

//var numImages = filtered.size().getInfo()
//print(numImages)

// A better approach is to use evaluate() function

// You need to define a 'callback' function which will be called once the 
// value has been computed and ready to be used.

var myCallback = function(object) {
  print(object)
}
print('Computing the size of the collection')
var numImages = filtered.size().evaluate(myCallback)

Exercise

Try in Code Editor ↗

var date = ee.Date.fromYMD(2019, 1, 1)
print(date)

// We can use the format() function to create
// a string from a date object
var dateString = date.format('dd MMM, YYYY')
print(dateString)

// Exercise
// The print statement below combines a client-side string
// with a server-side string - resulting in an error.

// Fix the code so that the following message is printed
// 'The date is 01 Jan, 2019'
var message = 'The date is ' + dateString
print(message)

// Hint: 
// Convert the client-side string to a server-side string
// Use ee.String() to create a server-side string
// Use the .cat() function instead of + to combine 2 strings

02. Using UI Elements

Earth Engine comes with a User Interface API that allows you to build an interactive web application powered by Earth Engine.

The Earth Engine API provides a library of User Interface (UI) widgets - such as Buttons, Drop-down Menus, Sliders etc. - that can be used to create interactive apps. All the user interface functions are contained in the ui. package - such as ui.Select(), ui.Button(). You can create those elements by calling these functions with appropriate parameters. Learn more in the Earth Engine User Interface API section of the Earth Engine User Guide.

This section shows how to build a drop-down selector using the ui.Select() widget.

Open in Code Editor ↗

// You can add any widgets from the ui.* module to the map
var years = ['2014', '2015', '2016', '2017'];

// Let's create a ui.Select() dropdown with the above values
var yearSelector = ui.Select({
  items: years,
  value: '2014',
  placeholder: 'Select a year',
  })
Map.add(yearSelector);

var loadImage = function() {
  var year = yearSelector.getValue();
  var col = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG");
  var startDate = ee.Date.fromYMD(
    ee.Number.parse(year), 1, 1);
  var endDate = startDate.advance(1, 'year');
  var filtered = col.filter(ee.Filter.date(startDate, endDate));
  var composite = filtered.mean().select('avg_rad');
  var layerName = 'Night Lights ' + year;
  var nighttimeVis = {min: 0.0, max: 60.0};
  Map.addLayer(composite, nighttimeVis, layerName);
};

var button = ui.Button({
  label: 'Click to Load Image',
  onClick: loadImage,
  });
Map.add(button);

Exercise

Try in Code Editor ↗

// Instead of manually creating a list of years like before
// we can create a list of years using ee.List.sequence()
var years = ee.List.sequence(2014, 2020)

// Convert them to strings using format() function
var yearStrings = years.map(function(year){
  return ee.Number(year).format('%04d')
})
print(yearStrings);

// Convert the server-side object to client-side using
// evaluate() and use it with ui.Select()
yearStrings.evaluate(function(yearList) {
  var yearSelector = ui.Select({
    items: yearList,
    value: '2014',
    placeholder: 'Select a year',
    })
  Map.add(yearSelector)
});

// Exercise

// Create another dropdown with months from 1 to 12
// and add it to the map.

03. Building and Publishing an App

Building a web mapping application typically requires the skills of a full stack developer and are out of reach for most analysts and scientists. But the Earth Engine User Interface API makes this process much more accessible by providing ready-to-use widgets and free cloud hosting to allow anyone to publish an app with just a few lines of code. The main container object is the ui.Panel() which can contain different types of widgets.

The code below shows how to build an app called Night Lights Explorer that allows anyone to pick a year/month and load the VIIRS Nighttime Day/Night Band Composite for the selected month. Copy/paste the code below to your Code Editor and click Run.

You will see a panel on the right-hand side with 2 drop-down boxes and a button. These are User Interface (UI) widgets provided by the Earth Engine API that allows the user to interactively select the values. You can select the values for year and month and click Load button to see the image for the selected month.

Open in Code Editor ↗

// Panels are the main container widgets
var mainPanel = ui.Panel({
  style: {width: '300px'}
});


var title = ui.Label({
  value: 'Night Lights Explorer',
  style: {'fontSize': '24px'}
});
// You can add widgets to the panel
mainPanel.add(title)

// You can even add panels to other panels
var dropdownPanel = ui.Panel({
  layout: ui.Panel.Layout.flow('horizontal'),
});
mainPanel.add(dropdownPanel);

var yearSelector = ui.Select({
  placeholder: 'please wait..',
  })

var monthSelector = ui.Select({
  placeholder: 'please wait..',
  })

var button = ui.Button('Load')
dropdownPanel.add(yearSelector)
dropdownPanel.add(monthSelector)
dropdownPanel.add(button)


// Let's add a dropdown with the years
var years = ee.List.sequence(2014, 2020)
var months = ee.List.sequence(1, 12)

// Dropdown items need to be strings
var yearStrings = years.map(function(year){
  return ee.Number(year).format('%04d')
})
var monthStrings = months.map(function(month){
  return ee.Number(month).format('%02d')
})

// Evaluate the results and populate the dropdown
yearStrings.evaluate(function(yearList) {
  yearSelector.items().reset(yearList)
  yearSelector.setPlaceholder('select a year')
})

monthStrings.evaluate(function(monthList) {
  monthSelector.items().reset(monthList)
  monthSelector.setPlaceholder('select a month')

})

// Define a function that triggers when any value is changed
var loadComposite = function() {
  var col = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG");
  var year = yearSelector.getValue()
  var month = monthSelector.getValue()
  var startDate = ee.Date.fromYMD(
    ee.Number.parse(year), ee.Number.parse(month), 1)
  var endDate = startDate.advance(1, 'month')
  var filtered = col.filter(ee.Filter.date(startDate, endDate))

  var image = ee.Image(filtered.first()).select('avg_rad')
  var nighttimeVis = {min: 0.0, max: 60.0}
  var layerName = 'Night Lights ' + year + '-' + month
  Map.addLayer(image, nighttimeVis, layerName)
}
button.onClick(loadComposite)

Map.setCenter(76.43, 12.41, 8)
ui.root.add(mainPanel);

Exercise

Try in Code Editor ↗

// Exercise
// Add a button called 'Reset'
// Clicking the button should remove all loaded layers

// Hint: Use Map.clear() for removing the layers

04. Publishing the App

We will now publish this app. Click on the Apps button.

App with UI Elements

In the Manage Apps window, click New App.

Enter the name of your app. The app will be hosted on Google Cloud, so you will need to create and link a Google Cloud project with the app. If you don’t have a Google Cloud account, you can click the Not Yet button to create a new project.

When prompted to Choose a Cloud Project for your apps, you can select Create a new Cloud Project and enter an unique id and click Select.

You may get an error asking you to accept the terms of service. Click the Cloud Terms of Service link and follow the instructions. Once done, click OK.

Back in the Publish New App dialog, leave all other settings to default and click Publish.

The app will be hosted on Google Cloud and you can access it by clicking on the App Name of your app in the Manage Apps dialog.

You will see your Earth Engine powered app running in the browser. Anyone can access and interact with the app by just visiting the App URL.

The app publishing process takes a few minutes. So if you get an error that your app is not yet ready, check back in a few minutes.

Explore The App ↗

Exercise

Try in Code Editor ↗

// Panels are the main container widgets
var mainPanel = ui.Panel({
  style: {width: '300px'}
});


var title = ui.Label({
  value: 'Night Lights Explorer',
  style: {'fontSize': '24px'}
});
// You can add widgets to the panel
mainPanel.add(title)

// You can even add panels to other panels
var dropdownPanel = ui.Panel({
  layout: ui.Panel.Layout.flow('horizontal'),
});
mainPanel.add(dropdownPanel);

var yearSelector = ui.Select({
  placeholder: 'please wait..',
  })

var monthSelector = ui.Select({
  placeholder: 'please wait..',
  })

var button = ui.Button('Load')
dropdownPanel.add(yearSelector)
dropdownPanel.add(monthSelector)
dropdownPanel.add(button)


// Let's add a dropdown with the years
var years = ee.List.sequence(2014, 2020)
var months = ee.List.sequence(1, 12)

// Dropdown items need to be strings
var yearStrings = years.map(function(year){
  return ee.Number(year).format('%04d')
})
var monthStrings = months.map(function(month){
  return ee.Number(month).format('%02d')
})

// Evaluate the results and populate the dropdown
yearStrings.evaluate(function(yearList) {
  yearSelector.items().reset(yearList)
  yearSelector.setPlaceholder('select a year')
})

monthStrings.evaluate(function(monthList) {
  monthSelector.items().reset(monthList)
  monthSelector.setPlaceholder('select a month')

})

// Define a function that triggers when any value is changed
var loadComposite = function() {
  var col = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG");
  var year = yearSelector.getValue()
  var month = monthSelector.getValue()
  var startDate = ee.Date.fromYMD(
    ee.Number.parse(year), ee.Number.parse(month), 1)
  var endDate = startDate.advance(1, 'month')
  var filtered = col.filter(ee.Filter.date(startDate, endDate))

  var image = ee.Image(filtered.first()).select('avg_rad')
  var nighttimeVis = {min: 0.0, max: 60.0}
  var layerName = 'Night Lights ' + year + '-' + month
  Map.addLayer(image, nighttimeVis, layerName)
}
button.onClick(loadComposite)

// Exercise
// Set the map center to your area of interst
// Replace the author label with your name
// Publish the app.
Map.setCenter(76.43, 12.41, 8)
var authorLabel = ui.Label('App by: Ujaval Gandhi');
mainPanel.add(authorLabel);

ui.root.add(mainPanel);

05. Create a Split Panel App

Another useful widget that can be used in Apps is ui.SplitPanel(). This allows you to create an app that can display 2 different images of the same region that can be explored interactively by swiping. Here we create an app to explore the ESA WorldCover 10m global classification dataset.

On the left-hand panel, we will load a Sentinel-2 composite for the year 2020. On the right-hand panel, we will load the 11-class landcover classification of the same region.

Open in Code Editor ↗

var admin2 = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level2");
var selected = admin2
  .filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))
  .filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'))
var geometry = selected.geometry();
Map.centerObject(geometry)

var s2 = ee.ImageCollection("COPERNICUS/S2");

// Write a function for Cloud masking
var maskS2clouds = function(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

// Write a function to scale the bands
var scaleImage = function(image) {
  return image
    .multiply(0.0001)
    .copyProperties(image, ["system:time_start"])
}

var filtered = s2
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .map(scaleImage);
  
// Create a median composite for 2020
var composite =  filtered.median();

// Load ESA WorldCover 2020 Classification
var worldcover = ee.ImageCollection("ESA/WorldCover/v100")
var filtered = worldcover
  .filter(ee.Filter.date('2020-01-01', '2021-01-01'));
var classification = ee.Image(filtered.first());

// Create a Split Panel App

// Set a center and zoom level.
var center = {lon: 77.58, lat: 12.97, zoom: 12};

// Create two maps.
var leftMap = ui.Map(center);
var rightMap = ui.Map(center);

// Link them together.
var linker = new ui.Map.Linker([leftMap, rightMap]);

// Create a split panel with the two maps.
var splitPanel = ui.SplitPanel({
  firstPanel: leftMap,
  secondPanel: rightMap,
  orientation: 'horizontal',
  wipe: true
});

// Remove the default map from the root panel.
ui.root.clear();

// Add our split panel to the root panel.
ui.root.add(splitPanel);

// Add the layers to the maps
// Composite goes to the leftMap
var rgbVis = {min: 0.0, max: 0.3, bands: ['B4', 'B3', 'B2']};
leftMap.addLayer(composite.clip(geometry), rgbVis, '2020 Composite');

// Classification foes to the rightMap
rightMap.addLayer(classification.clip(geometry), {}, 'WorldCover Classification');

Exercise

Try in Code Editor ↗

var admin2 = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level2");
var selected = admin2
  .filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))
  .filter(ee.Filter.eq('ADM2_NAME', 'Bangalore Urban'))
var geometry = selected.geometry();
Map.centerObject(geometry)

var s2 = ee.ImageCollection("COPERNICUS/S2");

// Write a function for Cloud masking
var maskS2clouds = function(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

// Write a function to scale the bands
var scaleImage = function(image) {
  return image
    .multiply(0.0001)
    .copyProperties(image, ["system:time_start"])
}

var filtered = s2
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .map(scaleImage);
  
// Create a median composite for 2020
var composite =  filtered.median();

// Load ESA WorldCover 2020 Classification
var worldcover = ee.ImageCollection("ESA/WorldCover/v100")
var filtered = worldcover
  .filter(ee.Filter.date('2020-01-01', '2021-01-01'));
var classification = ee.Image(filtered.first());

// Create a Split Panel App

// Set a center and zoom level.
var center = {lon: 77.58, lat: 12.97, zoom: 12};

// Create two maps.
var leftMap = ui.Map(center);
var rightMap = ui.Map(center);

// Link them together.
var linker = new ui.Map.Linker([leftMap, rightMap]);

// Create a split panel with the two maps.
var splitPanel = ui.SplitPanel({
  firstPanel: leftMap,
  secondPanel: rightMap,
  orientation: 'horizontal',
  wipe: true
});

// Remove the default map from the root panel.
ui.root.clear();

// Add our split panel to the root panel.
ui.root.add(splitPanel);

// Add the layers to the maps
// Composite goes to the leftMap
var rgbVis = {min: 0.0, max: 0.3, bands: ['B4', 'B3', 'B2']};
leftMap.addLayer(composite.clip(geometry), rgbVis, '2020 Composite');

// Classification foes to the rightMap
rightMap.addLayer(classification.clip(geometry), {}, 'WorldCover Classification');

// Adding a Legend
// The following code creates a legend with class names and colors

// Create the panel for the legend items.
var legend = ui.Panel({
  style: {
    position: 'middle-right',
    padding: '8px 15px'
  }
});

// Create and add the legend title.
var legendTitle = ui.Label({
  value: 'ESA WorldCover Classes',
  style: {
    fontWeight: 'bold',
    fontSize: '18px',
    margin: '0 0 4px 0',
    padding: '0'
  }
});
legend.add(legendTitle);

var loading = ui.Label('Loading legend...', {margin: '2px 0 4px 0'});
legend.add(loading);

// Creates and styles 1 row of the legend.
var makeRow = function(color, name) {
  // Create the label that is actually the colored box.
  var colorBox = ui.Label({
    style: {
      backgroundColor: '#' + color,
      // Use padding to give the box height and width.
      padding: '8px',
      margin: '0 0 4px 0'
    }
  });

  // Create the label filled with the description text.
  var description = ui.Label({
    value: name,
    style: {margin: '0 0 4px 6px'}
  });

  return ui.Panel({
    widgets: [colorBox, description],
    layout: ui.Panel.Layout.Flow('horizontal')
  });
};

var BAND_NAME = 'Map';
// Get the list of palette colors and class names from the image.
classification.toDictionary().select([BAND_NAME + ".*"]).evaluate(function(result) {
  var palette = result[BAND_NAME + "_class_palette"];
  var names = result[BAND_NAME + "_class_names"];
  loading.style().set('shown', false);

  for (var i = 0; i < names.length; i++) {
    legend.add(makeRow(palette[i], names[i]));
  }
});

// Print the panel containing the legend
print(legend);

// Exercise

// The 'legend' panel contains the legend for the classification
// Add the legend to the map below

// Hint: UI Widgets can only be shown once in the app. Remove the
//       print statement before adding the legend to the map.
// Hint: Load the legend in the right-hand side map.

Module 6: Google Earth Engine Python API

Introduction to the Python API

Till this point in the course, we have used the Earth Engine Javascript API for all our analysis. Earth Engine also provides a Python API. If you are a Python programmer, you may prefer to use the Python API to integrate Earth Engine in your spatial analysis workflow. There are many options for running Python code that uses the Google Earth Engine API. We will use the following two methods in this course.

View the Presentation ↗

Google Colab

An easy way to start using the Google Earth Engine Python API is via Google Colab. Google Colaboratory provides a hosted environment to run Python notebooks without having to install Python locally. It also comes pre-installed with many useful packages - including the Google Earth Engine Python API. You can simply visit https://colab.research.google.com/ and start a new notebook.

Local Development Environment

An advantage of Python API is that you can use it in your own development environment - so you get a lot more flexibility to automate as well as combine other analysis and visualization libraries with Earth Engine. This requires installing Python and the Earth Engine Python API on your machine or server. You also need to do a one-time authentication and save the token on the machine. The preferred method for installing the Earth Engine Python API is via Anaconda. Please follow our Google Earth Engine Python API Installation Guide for step-by-step instructions.

01. Python API Syntax

Open in Google Colab ↗

Coming from the programming in Earth Engine through the Code Editor, you will need to slightly adapt your scripts to be able to run in Python. For the bulk of your code, you will be using Earth Engine API’s server-side objects and functions - which will be exactly the same in Python. You only need to make a few syntactical changes.

Here’s the full list of differences.

Initialization

First of all, you need to run the following cells to initialize the API and authorize your account. You will be prompted to sign-in to the account and allow access to View and Manage your Earth Engine data. Once you approve, you will get an a verification code that needs to be entered at the prompt. This step needs to be done just once per session.

import ee

ee.Authenticate()

ee.Initialize()

Variables

Python code doesn’t use the ‘var’ keyword

javascript code:

var city = 'San Fransico'
var state = 'California'
print(city, state)

var population = 881549
print(population)

city = 'San Fransico'
state = 'California'
print(city, state)

population = 881549
print(population)

Earth Engine Objects

You can create Earth Engine objects using the ee functions the same way.

s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
geometry = ee.Geometry.Polygon([[
  [82.60642647743225, 27.16350437805251],
  [82.60984897613525, 27.1618529901377],
  [82.61088967323303, 27.163695288375266],
  [82.60757446289062, 27.16517483230927]
]])

Line Continuation

Python doesn’t use a semi-colon for line ending. To indicate line-continuation you need to use the \ character

javascript code:

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-02-01', '2019-03-01'))
  .filter(ee.Filter.bounds(geometry));

filtered = s2 \
    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30)) \
    .filter(ee.Filter.date('2019-02-01', '2019-03-01')) \
    .filter(ee.Filter.bounds(geometry))

Functions

Instead of the function keyword, Python uses the def keyword. Also the in-line functions are defined using lambda anonymous functions.

In the example below, also now the and operator - which is capitalized as And in Python version to avoid conflict with the built-in and operator. The same applies to Or and Not operators. true, false, null in Python are also spelled as True, False and None.

javascript code:

function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)//.divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
}

def maskS2clouds(image):
  qa = image.select('QA60')
  cloudBitMask = 1 << 10
  cirrusBitMask = 1 << 11
  mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask) \
      .select("B.*") \
      .copyProperties(image, ["system:time_start"])

def addNDVI(image):
  ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi')
  return image.addBands(ndvi)

withNdvi = filtered \
    .map(maskS2clouds) \
    .map(addNDVI)

Function Arguments

Named arguments to Earth Engine functions need to be in quotes. Also when passing the named arguments as a dictionary, it needs to be passed using the ** keyword.

javascript code:

var composite = withNdvi.median();
var ndvi = composite.select('ndvi');

var stats = ndvi.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e10
})    

composite = withNdvi.median()
ndvi = composite.select('ndvi')

stats = ndvi.reduceRegion(**{
  'reducer': ee.Reducer.mean(),
  'geometry': geometry,
  'scale': 10,
  'maxPixels': 1e10
  })

Printing Values

The print() function syntax is the same. But you must remember that in the Code Editor when you cann print, the value of the server object is fetched and then printed. You must do that explicitely by calling getInfo() on any server-side object.

javascript code:

print(stats.get('ndvi')

print(stats.get('ndvi').getInfo())

In-line functions

The syntax for defining in-line functions is also slightly different. You need to use the lambda keyword.

javascript code:

var myList = ee.List.sequence(1, 10);
var newList = myList.map(function(number) {
    return ee.Number(number).pow(2);
print(newList);

myList = ee.List.sequence(1, 10)
newList = myList.map(lambda number: ee.Number(number).pow(2))
print(newList.getInfo())

Exercise

Take the Javascript code snippet below and write the equiavalent Python code in the cell below.

• Hint1: Chaining of filters require the use of line continuation character \
• Hint2: Printing of server-side objects requires calling .getInfo() on the object

The correct code should print the value 30.

---

var geometry = ee.Geometry.Point([77.60412933051538, 12.952912912328241]);

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');

var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(geometry));
  
print(filtered.size());

---

import ee
ee.Authenticate()
ee.Initialize()

02. Automatic Conversion of Javascript Code to Python

Open in Google Colab ↗

Interactive leaflet map created by geemap

geemap is an open-source Python package that comes with many helpful features that help you use Earth Engine effectively in Python.

It comes with a function that can help you translate your javascript earth engine code to Python automatically.

Google Colab doesn’t come pre-installed with the package, so we install it via pip.

try:
    import geemap
except ModuleNotFoundError:
    if 'google.colab' in str(get_ipython()):
        print('geemap not found, installing via pip in Google Colab...')
        !pip install geemap --quiet
        import geemap
    else:
        print('geemap not found, please install via conda in your environment')

The automatic conversion of code is done by calling the geemap.js_snippet_to_py() function. We first create a string with the javascript code.

javascript_code = """
var geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766]);
Map.centerObject(geometry, 12)
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};

// Write a function for Cloud masking
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}
 
var filtered = s2
  .filter(ee.Filter.date('2019-01-01', '2019-12-31'))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  

// Write a function that computes NDVI for an image and adds it as a band
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
}

var withNdvi = filtered.map(addNDVI);

var composite = withNdvi.median()
palette = [
  'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
  '74A901', '66A000', '529400', '3E8601', '207401', '056201',
  '004C00', '023B01', '012E01', '011D01', '011301'];

ndviVis = {min:0, max:0.5, palette: palette }
Map.addLayer(withNdvi.select('ndvi'), ndviVis, 'NDVI Composite')

"""

lines = geemap.js_snippet_to_py(
    javascript_code, add_new_cell=False,
    import_ee=True, import_geemap=True, show_map=True)
for line in lines:
    print(line.rstrip())

The automatic conversion works great. Review it and paste it to the cell below.

import ee
import geemap
Map = geemap.Map()

geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766])
Map.centerObject(geometry, 12)
s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
rgbVis = {
  'min': 0.0,
  'max': 3000,
  'bands': ['B4', 'B3', 'B2'],
}

# Write a function for Cloud masking
def maskS2clouds(image):
  qa = image.select('QA60')
  cloudBitMask = 1 << 10
  cirrusBitMask = 1 << 11
  mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask) \
      .select("B.*") \
      .copyProperties(image, ["system:time_start"])

filtered = s2 \
  .filter(ee.Filter.date('2019-01-01', '2019-12-31')) \
  .filter(ee.Filter.bounds(geometry)) \
  .map(maskS2clouds)

# Write a function that computes NDVI for an image and adds it as a band
def addNDVI(image):
  ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi')
  return image.addBands(ndvi)

withNdvi = filtered.map(addNDVI)

composite = withNdvi.median()
palette = [
  'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
  '74A901', '66A000', '529400', '3E8601', '207401', '056201',
  '004C00', '023B01', '012E01', '011D01', '011301']

ndviVis = {'min':0, 'max':0.5, 'palette': palette }
Map.addLayer(withNdvi.select('ndvi'), ndviVis, 'NDVI Composite')
Map

Exercise

Take the Javascript code snippet below and use geemap to automatically convert it to Python.

---

var admin2 = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level2");

var karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))

var visParams = {color: 'red'}
Map.centerObject(karnataka)
Map.addLayer(karnataka, visParams, 'Karnataka Districts')

---

03. Batch Exports

Open in Google Colab ↗

One of the most commonly asked questions by Earth Engine users is - How do I download all images in a collection? The Earth Engine Python API comes with a ee.batch module that allows you to launch batch exports and manage tasks. The recommended way to do batch exports like this is to use the Python API’s ee.batch.Export functions and use a Python for-loop to iterate and export each image. The ee.batch module also gives you ability to control Tasks - allowing you to automate exports.

import ee

ee.Authenticate()

ee.Initialize()

Create a Collection

geometry = ee.Geometry.Point([107.61303468448624, 12.130969369851766])
s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')
rgbVis = {
  'min': 0.0,
  'max': 3000,
  'bands': ['B4', 'B3', 'B2'],
}

# Write a function for Cloud masking
def maskS2clouds(image):
  qa = image.select('QA60')
  cloudBitMask = 1 << 10
  cirrusBitMask = 1 << 11
  mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask) \
      .select("B.*") \
      .copyProperties(image, ["system:time_start"])

filtered = s2 \
  .filter(ee.Filter.date('2019-01-01', '2020-01-01')) \
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30)) \
  .filter(ee.Filter.bounds(geometry)) \
  .map(maskS2clouds)

# Write a function that computes NDVI for an image and adds it as a band
def addNDVI(image):
  ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi')
  return image.addBands(ndvi)

withNdvi = filtered.map(addNDVI)

Export All Images

Exports are done via the ee.batch module. A key difference between javascript and Python version is that the region parameter needs to be supplied with actual geometry coordinates.

image_ids = withNdvi.aggregate_array('system:index').getInfo()
print('Total images: ', len(image_ids))

# Export with 100m resolution for this demo
for i, image_id in enumerate(image_ids):
  image = ee.Image(withNdvi.filter(ee.Filter.eq('system:index', image_id)).first())
  task = ee.batch.Export.image.toDrive(**{
    'image': image.select('ndvi'),
    'description': 'Image Export {}'.format(i+1),
    'fileNamePrefix': image.id().getInfo(),
    'folder':'earthengine',
    'scale': 100,
    'region': image.geometry().bounds().getInfo()['coordinates'],
    'maxPixels': 1e10
  })
  task.start()
  print('Started Task: ', i+1)

Manage Running/Waiting Tasks

You can manage tasks as well. Get a list of tasks and get state information on them

tasks = ee.batch.Task.list()
for task in tasks:
  task_id = task.status()['id']
  task_state = task.status()['state']
  print(task_id, task_state)

You can cancel tasks as well

tasks = ee.batch.Task.list()
for task in tasks:
    task_id = task.status()['id']
    task_state = task.status()['state']
    if task_state == 'RUNNING' or task_state == 'READY':
        task.cancel()
        print('Task {} canceled'.format(task_id))
    else:
        print('Task {} state is {}'.format(task_id, task_state))

Exercise

The code below uses the TerraClimate data and creates an ImageCollection with 12 monthly images of maximum temperature. It also extract the geometry for Australia from the LSIB collection. Add the code to start an export task for each image in the collection for australia.

• Hint1: TerraClimate images have a scale of 4638.3m
• Hint2: You need to export the image contained in the clippedImage variable

import ee

lsib = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')
australia = lsib.filter(ee.Filter.eq('country_na', 'Australia'))
geometry = australia.geometry()

terraclimate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
tmax = terraclimate.select('tmmx')

def scale(image):
  return image.multiply(0.1) \
    .copyProperties(image,['system:time_start'])

tmaxScaled = tmax.map(scale)

filtered = tmaxScaled \
  .filter(ee.Filter.date('2020-01-01', '2021-01-01')) \
  .filter(ee.Filter.bounds(geometry))

image_ids = filtered.aggregate_array('system:index').getInfo()
print('Total images: ', len(image_ids))

Replace the comments with your code.

for i, image_id in enumerate(image_ids):
    exportImage = ee.Image(filtered.filter(ee.Filter.eq('system:index', image_id)).first())
    # Clip the image to the region geometry
    clippedImage = exportImage.clip(geometry)
    
    ## Create the export task using ee.batch.Export.image.toDrive()
    
    ## Start the task

Launching multiple tasks using the Python API

04. Creating Charts in Python

Open in Google Colab ↗

The Google Earth Engine Python API does not come with a charting module. But you can use third-party modules to create interactive charts. You may also convert the Earth Engine objects to a Pandas dataframe and plot it using Python libraries like Matplotlib

This notebook shows how to use the geemap package to create a Time-Series Chart from a ImageCollection.

References:

• geemap Chart module
• geemap Example notebook

import ee

try:
    import geemap
except ModuleNotFoundError:
    if 'google.colab' in str(get_ipython()):
        print('geemap not found, installing via pip in Google Colab...')
        !pip install geemap --quiet
        import geemap
    else:
        print('geemap not found, please install via conda in your environment')

ee.Authenticate()

ee.Initialize()

Load the TerraClimate collection and select the ‘tmmx’ band.

terraclimate = ee.ImageCollection('IDAHO_EPSCOR/TERRACLIMATE')
tmax = terraclimate.select('tmmx')

Define a point location for the chart.

geometry = ee.Geometry.Point([77.57738128916243, 12.964758918835752])

Scale the band values so they are in Degree Celcius.

def scale_image(image):
  return ee.Image(image).multiply(0.1)\
    .copyProperties(image, ['system:time_start'])

tmaxScaled = tmax.map(scale_image)

Filter the collection.

filtered = tmaxScaled.filter(ee.Filter.date('2019-01-01', '2020-01-01')) \
                     .filter(ee.Filter.bounds(geometry))

To chart an image series in Python, we must first extract the values from each image and create a FeatureCollection.

def extract_data(image):
    stats = image.reduceRegion(**{ 
        'reducer':ee.Reducer.mean(),
        'geometry':geometry,
        'scale':5000
    })
    properties = {
        'month': image.get('system:index'),
        'tmmx': stats.get('tmmx')
    }
    return ee.Feature(None, properties)

data = ee.FeatureCollection(filtered.map(extract_data))

print(data.first().getInfo())

Create an Interactive Chart using geemap

from geemap import chart

options = {
    'title': 'Max Monthly Temperature at Bangalore', 
    'legend_location': 'top-right',
    'height': '500px',
    'ylabel': 'Temperature (C)',
    'xlabel': 'Date',
    'colors': ['blue']
}

chart.feature_byFeature(data, 'month', ['tmmx'], **options)

Create a chart using Matplotlib

We can convert a FeatureCollection to a DataFrame using geemap helper function ee_to_pandas.

import geemap
df = geemap.ee_to_pandas(data)

df

Now we have a regular Pandas dataframe that can be plotted with matplotlib.

%matplotlib inline
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
fig.set_size_inches(20,10)


df.plot(ax=ax,
        title='Max Monthly Temperature at Bangalore',
        x='month',
        ylabel='Temperature (C)',
        kind='line')
plt.tight_layout()

Exercise

Customize the above chart by plotting it as a Line Chart in red color.

• Hint1: Use kind='line' along with a color option.

05. Automating Downloads

Another common use of the GEE Python API is to automate data processing and export. You can create a Python script that can be called from a server or launched on a schedule using tools such as Windows Scheduler or crontab.

This script below provides a complete example of automating a download using Google Earth Engine API. It uses the Google Earth Engine API to compute the average soil moisture for the given time period over all districts in a state. The result is then downloaded as a JSON file and saved locally.

Make sure you have completed the one-time authentication flow before running the script.

Follow the steps below to create a script to download data from GEE.

1. Create a new file named download_data.py with the content shown below.

import datetime
import ee
import csv
import os

ee.Initialize()

# Get current date and convert to milliseconds 
start_date = ee.Date.fromYMD(2022, 1, 1)
end_date = start_date.advance(1, 'month')

date_string = end_date.format('YYYY_MM')
filename = 'ssm_{}.csv'.format(date_string.getInfo())

# Saving to current directory. You can change the path to appropriate location
output_path = os.path.join(filename)

# Datasets
# SMAP is in safe mode and not generating new data since August 2022
# https://nsidc.org/data/user-resources/data-announcements/user-notice-smap-safe-mode
soilmoisture = ee.ImageCollection("NASA_USDA/HSL/SMAP10KM_soil_moisture")
admin2 = ee.FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level2")

# Filter to a state
karnataka = admin2.filter(ee.Filter.eq('ADM1_NAME', 'Karnataka'))

# Select the ssm band
ssm  = soilmoisture.select('ssm')

filtered = ssm .filter(ee.Filter.date(start_date, end_date))

mean = filtered.mean()

stats = mean.reduceRegions(**{
  'collection': karnataka,
  'reducer': ee.Reducer.mean().setOutputs(['meanssm']),
  'scale': 10000,
  'crs': 'EPSG:32643'
  })

# Select columns to keep and remove geometry to make the result lightweight
# Change column names to match your uploaded shapefile
columns = ['ADM2_NAME', 'meanssm']
exportCollection = stats.select(**{
    'propertySelectors': columns,
    'retainGeometry': False})

features = exportCollection.getInfo()['features']

data = []

for f in features:
    data.append(f['properties'])

field_names = ['ADM2_NAME', 'meanssm']

with open(output_path, 'w') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames = field_names)
    writer.writeheader()
    writer.writerows(data)
    print('Success: File written at', output_path)

2. From the terminal, navigate to the directory where you have created the file and type the command below to run the script.

python download_data.py

3. The script will download the data from GEE and save a file to your current directory.

Supplement

This section contains useful scripts and code snippets that can be adapted for your projects.

Advanced Supervised Classification Techniques

Hyperparameter Tuning

A recommended best practice for improving the accuracy of your machine learning model is to tune different parameters. For example, when using the ee.Classifier.smileRandomForest() classifier, we must specify the Number of Trees. We know that higher number of trees result in more computation requirement, but it doesn’t necessarily result in better results. Instead of guessing, we programmatically try a range of values and choose the smallest value possible that results in the highest accuracy.

Supervised Classification Output

Open in Code Editor ↗

var s2 = ee.ImageCollection("COPERNICUS/S2_SR");
var basin = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_7");
var gcp = ee.FeatureCollection("users/ujavalgandhi/e2e/arkavathy_gcps");
var alos = ee.Image("JAXA/ALOS/AW3D30/V2_2");


var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))
var boundary = arkavathy.geometry()
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
// Function to remove cloud and snow pixels from Sentinel-2 SR image

function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(10);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 10% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}


var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(boundary))
  .map(maskCloudAndShadowsSR)
  .select('B.*')

var composite = filtered.median().clip(boundary) 

var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
Map.centerObject(boundary)
Map.addLayer(composite, visParams, 'RGB');

var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi)
}

var composite = addIndices(composite);


// Calculate Slope and Elevation
var elev = alos.select('AVE_DSM').rename('elev');
var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');

var composite = composite.addBands(elev).addBands(slope);



// Normalize the image 

// Machine learning algorithms work best on images when all features have
// the same range

// Function to Normalize Image
// Pixel Values should be between 0 and 1
// Formula is (x - xmin) / (xmax - xmin)
//************************************************************************** 
function normalize(image){
  var bandNames = image.bandNames();
  // Compute min and max of the image
  var minDict = image.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: boundary,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var maxDict = image.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: boundary,
    scale: 20,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var mins = ee.Image.constant(minDict.values(bandNames));
  var maxs = ee.Image.constant(maxDict.values(bandNames));

  var normalized = image.subtract(mins).divide(maxs.subtract(mins))
  return normalized
}

var composite = normalize(composite);
// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn()

// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));
Map.addLayer(validationGcp)
// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});
print(training)
// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

//************************************************************************** 
// Feature Importance
//************************************************************************** 

// Run .explain() to see what the classifer looks like
print(classifier.explain())

// Calculate variable importance
var importance = ee.Dictionary(classifier.explain().get('importance'))

// Calculate relative importance
var sum = importance.values().reduce(ee.Reducer.sum())

var relativeImportance = importance.map(function(key, val) {
   return (ee.Number(val).multiply(100)).divide(sum)
  })
print(relativeImportance)

// Create a FeatureCollection so we can chart it
var importanceFc = ee.FeatureCollection([
  ee.Feature(null, relativeImportance)
])

var chart = ui.Chart.feature.byProperty({
  features: importanceFc
}).setOptions({
      title: 'Feature Importance',
      vAxis: {title: 'Importance'},
      hAxis: {title: 'Feature'}
  })
print(chart)

//************************************************************************** 
// Hyperparameter Tuning
//************************************************************************** 

var test = composite.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16
});


// Tune the numberOfTrees parameter.
var numTreesList = ee.List.sequence(10, 150, 10);

var accuracies = numTreesList.map(function(numTrees) {
  var classifier = ee.Classifier.smileRandomForest(numTrees)
      .train({
        features: training,
        classProperty: 'landcover',
        inputProperties: composite.bandNames()
      });

  // Here we are classifying a table instead of an image
  // Classifiers work on both images and tables
  return test
    .classify(classifier)
    .errorMatrix('landcover', 'classification')
    .accuracy();
});

var chart = ui.Chart.array.values({
  array: ee.Array(accuracies),
  axis: 0,
  xLabels: numTreesList
  }).setOptions({
      title: 'Hyperparameter Tuning for the numberOfTrees Parameters',
      vAxis: {title: 'Validation Accuracy'},
      hAxis: {title: 'Number of Tress', gridlines: {count: 15}}
  });
print(chart)

// Tuning Multiple Parameters
// We can tune many parameters together using
// nested map() functions
// Let's tune 2 parameters
// numTrees and bagFraction 
var numTreesList = ee.List.sequence(10, 150, 10);
var bagFractionList = ee.List.sequence(0.1, 0.9, 0.1);

var accuracies = numTreesList.map(function(numTrees) {
  return bagFractionList.map(function(bagFraction) {
     var classifier = ee.Classifier.smileRandomForest({
       numberOfTrees: numTrees,
       bagFraction: bagFraction
     })
      .train({
        features: training,
        classProperty: 'landcover',
        inputProperties: composite.bandNames()
      });

    // Here we are classifying a table instead of an image
    // Classifiers work on both images and tables
    var accuracy = test
      .classify(classifier)
      .errorMatrix('landcover', 'classification')
      .accuracy();
    return ee.Feature(null, {'accuracy': accuracy,
      'numberOfTrees': numTrees,
      'bagFraction': bagFraction})
  })
}).flatten()
var resultFc = ee.FeatureCollection(accuracies)

// Export the result as CSV
Export.table.toDrive({
  collection: resultFc,
  description: 'Multiple_Parameter_Tuning_Results',
  folder: 'earthengine',
  fileNamePrefix: 'numtrees_bagfraction',
  fileFormat: 'CSV'}) 
  

Post-Processing Classification Results

Supervised classification results often contain salt-and-pepper noise caused by mis-classified pixels. It is usually preferable to apply some post-processing techniques to remove such noise. The following script contains the code for two popular techniques for post-processing classification results.

• Using un-supervised clustering to replacing classified value by majority value in each cluster.
• Replacing isolated pixels with surrounding value with a majority filter.

Remember that the neighborhood methods are scale-dependent so the results will change as you zoom in/out. Export the results at the desired scale to see the effect of post-processing.

Open in Code Editor ↗

// Sentinel-2 Median Composite
var composite = ee.Image("users/ujavalgandhi/e2e/arkavathy_2019_composite");
Map.addLayer(composite, {min: 0, max: 0.3,   bands: ['B4', 'B3', 'B2']}, 'RGB Composite');

// Raw Supervised Classification Results
var classified = ee.Image("users/ujavalgandhi/e2e/arkavathy_final_classification");
Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, 'Original');
Map.centerObject(classified, 10)


//************************************************************************** 
// Post process by clustering
//************************************************************************** 

// Cluster using Unsupervised Clustering methods
var seeds = ee.Algorithms.Image.Segmentation.seedGrid(5);

var snic = ee.Algorithms.Image.Segmentation.SNIC({
  image: composite.select('B.*'), 
  compactness: 0,
  connectivity: 4,
  neighborhoodSize: 10,
  size: 2,
  seeds: seeds
})
var clusters = snic.select('clusters')

// Assign class to each cluster based on 'majority' voting (using ee.Reducer.mode()
var smoothed = classified.addBands(clusters);

var clusterMajority = smoothed.reduceConnectedComponents({
  reducer: ee.Reducer.mode(),
  labelBand: 'clusters'
});
Map.addLayer(clusterMajority, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']},
  'Processed using Clusters');


//************************************************************************** 
// Post process by replacing isolated pixels with surrounding value
//************************************************************************** 

// count patch sizes
var patchsize = classified.connectedPixelCount(40, false);

// run a majority filter
var filtered = classified.focal_mode({
    radius: 10,
    kernelType: 'square',
    units: 'meters',
}); 
  
// updated image with majority filter where patch size is small
var connectedClassified =  classified.where(patchsize.lt(25),filtered);
Map.addLayer(connectedClassified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']},
  'Processed using Connected Pixels');

Principal Component Analysis (PCA)

PCA is a very useful technique in improving your supervised classification results. This is a statistical technique that compresses data from a large number of bands into fewer uncorrelated bands. You can run PCA on your image and add the first few (typically 3) principal component bands to the original composite before sampling training points. In the example below, you will notice that 97% of the variance from the 13-band original image is captured in the 3-band PCA image. This sends a stronger signal to the classifier and improves accuracy by allowing it to distinguish different classes better.

Open in Code Editor ↗

// Script showing how to do Principal Component Analysis on images
var composite = ee.Image("users/ujavalgandhi/e2e/arkavathy_2019_composite");
var boundary = ee.FeatureCollection("users/ujavalgandhi/e2e/arkavathy_boundary");

print('Input Composite', composite);
Map.centerObject(composite)
Map.addLayer(composite, {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3, gamma: 1.2}, 'RGB');

// Define the geometry and scale parameters
var geometry = boundary.geometry();
var scale = 20;

// Run the PCA function
var pca = PCA(composite)

// Extract the properties of the pca image
var variance = pca.toDictionary()
print('Variance of Principal Components', variance)

// As you see from the printed results, ~97% of the variance
// from the original image is captured in the first 3 principal components
// We select those and discard others
var pca = PCA(composite).select(['pc1', 'pc2', 'pc3'])
print('First 3 PCA Bands', pca);

// PCA computation is expensive and can time out when displaying on the map
// Export the results and import them back
Export.image.toAsset({
  image: pca,
  description: 'Principal_Components_Image',
  assetId: 'users/ujavalgandhi/e2e/arkavathy_pca',
  region: geometry,
  scale: scale,
  maxPixels: 1e10})
// Once the export finishes, import the asset and display
var pcaImported = ee.Image('users/ujavalgandhi/e2e/arkavathy_pca')
var pcaVisParams = {bands: ['pc1', 'pc2', 'pc3'], min: -2, max: 2};

Map.addLayer(pcaImported, pcaVisParams, 'Principal Components');


//************************************************************************** 
// Function to calculate Principal Components
// Code adapted from https://developers.google.com/earth-engine/guides/arrays_eigen_analysis
//************************************************************************** 
function PCA(maskedImage){
  var image = maskedImage.unmask()
  var scale = scale;
  var region = geometry;
  var bandNames = image.bandNames();
  // Mean center the data to enable a faster covariance reducer
  // and an SD stretch of the principal components.
  var meanDict = image.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: region,
    scale: scale,
    maxPixels: 1e13,
    tileScale: 16
  });
  var means = ee.Image.constant(meanDict.values(bandNames));
  var centered = image.subtract(means);
  // This helper function returns a list of new band names.
  var getNewBandNames = function(prefix) {
    var seq = ee.List.sequence(1, bandNames.length());
    return seq.map(function(b) {
      return ee.String(prefix).cat(ee.Number(b).int());
    });
  };
  // This function accepts mean centered imagery, a scale and
  // a region in which to perform the analysis.  It returns the
  // Principal Components (PC) in the region as a new image.
  var getPrincipalComponents = function(centered, scale, region) {
    // Collapse the bands of the image into a 1D array per pixel.
    var arrays = centered.toArray();
    
    // Compute the covariance of the bands within the region.
    var covar = arrays.reduceRegion({
      reducer: ee.Reducer.centeredCovariance(),
      geometry: region,
      scale: scale,
      maxPixels: 1e13,
      tileScale: 16
    });

    // Get the 'array' covariance result and cast to an array.
    // This represents the band-to-band covariance within the region.
    var covarArray = ee.Array(covar.get('array'));

    // Perform an eigen analysis and slice apart the values and vectors.
    var eigens = covarArray.eigen();

    // This is a P-length vector of Eigenvalues.
    var eigenValues = eigens.slice(1, 0, 1);
    
    // Compute Percentage Variance of each component
    // This will allow us to decide how many components capture
    // most of the variance in the input
    var eigenValuesList = eigenValues.toList().flatten()
    var total = eigenValuesList.reduce(ee.Reducer.sum())

    var percentageVariance = eigenValuesList.map(function(item) {
      var component = eigenValuesList.indexOf(item).add(1).format('%02d')
      var variance = ee.Number(item).divide(total).multiply(100).format('%.2f')
      return ee.List([component, variance])
    })
    // Create a dictionary that will be used to set properties on final image
    var varianceDict = ee.Dictionary(percentageVariance.flatten())
    // This is a PxP matrix with eigenvectors in rows.
    var eigenVectors = eigens.slice(1, 1);
    // Convert the array image to 2D arrays for matrix computations.
    var arrayImage = arrays.toArray(1);

    // Left multiply the image array by the matrix of eigenvectors.
    var principalComponents = ee.Image(eigenVectors).matrixMultiply(arrayImage);

    // Turn the square roots of the Eigenvalues into a P-band image.
    // Call abs() to turn negative eigenvalues to positive before
    // taking the square root
    var sdImage = ee.Image(eigenValues.abs().sqrt())
      .arrayProject([0]).arrayFlatten([getNewBandNames('sd')]);

    // Turn the PCs into a P-band image, normalized by SD.
    return principalComponents
      // Throw out an an unneeded dimension, [[]] -> [].
      .arrayProject([0])
      // Make the one band array image a multi-band image, [] -> image.
      .arrayFlatten([getNewBandNames('pc')])
      // Normalize the PCs by their SDs.
      .divide(sdImage)
      .set(varianceDict);
  };
  var pcImage = getPrincipalComponents(centered, scale, region);
  return pcImage.mask(maskedImage.mask());
}

Multi-temporal Composites for Crop Classification

Crop classification is a difficult problem. A useful technique that aids in clear distinction of crops is to account for crop phenology. This technique can be applied to detect a specific type of crop or distinguish crops from other forms of vegetation. You can create composite images for different periods of the cropping cycle and create a stacked image to be used for classification. This allows the classifier to learn the temporal pattern and detect pixels that exhibit similar patterns.

Capturing Crop Phenology through Seasonal Composites

Open in Code Editor ↗

var s2 = ee.ImageCollection("COPERNICUS/S2_SR")
var basin = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_7")
var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))
var boundary = arkavathy.geometry()
Map.centerObject(boundary, 11)

// Function to remove cloud pixels from Sentinel-2 SR image 
function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(10);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 10% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask).divide(10000)
    .copyProperties(image, ['system:time_start']);
}


var filtered = s2
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(boundary))
  .map(maskCloudAndShadowsSR)
// There are 3 distinct crop seasons in the area of interest
// Jan-March = Winter (Rabi) Crops
// April-June  = Summer Crops / Harvest
// July-December = Monsoon (Kharif) Crops
var cropCalendar = ee.List([[1,3], [4,6], [7,12]])

// We create different composites for each season
var createSeasonComposites = function(months) {
  var startMonth = ee.List(months).get(0)
  var endMonth = ee.List(months).get(1)
  var monthFilter = ee.Filter.calendarRange(startMonth, endMonth, 'month')
  var seasonFiltered = filtered.filter(monthFilter)
  var composite = seasonFiltered.median()
  return composite.select('B.*').clip(boundary)
}

var compositeList = cropCalendar.map(createSeasonComposites)

var rabi = ee.Image(compositeList.get(0))
var harvest = ee.Image(compositeList.get(1))
var kharif = ee.Image(compositeList.get(2))

var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3, gamma: 1.2};
Map.addLayer(rabi, visParams, 'Rabi')
Map.addLayer(harvest, visParams, 'Harvest')
Map.addLayer(kharif, visParams, 'Kharif')

// Create a stacked image with composites from all seasons
// This multi-temporal image is able capture the crop phenology
// Classifier will be able to detect crop-pixels from non-crop pixels
var composite = rabi.addBands(harvest).addBands(kharif)

// This is a 36-band image
// Use this image for sampling training points for
// to train a crop classifier 
print(composite)

Computing Correlation

A useful technique to aid crop classification is to model the correlation between precipitation and changes in vegetation. This allows the model to capture differentiated responses to rainfall (i.e. raid-fed crops vs permanent forests). We first prepare an image collection where each image consists of 2 bands - cumulative rainfall for each month and average NDVI for the next month. This will create 11 images per year which show precipitation and 1-month lagged NDVI at each pixels. The collection is then reduced using the ee.Reducer.pearsonsCorrelation() which outputs a correlation band. Positive values will show regions where precipitation caused an increase in NDVI. Adding this band to your input image for classification will greatly aid the classifier in separating different types of vegetation.

Open in Code Editor ↗

// Calculate Rainfall-NDVI Correlation
var geometry = ee.Geometry.Point([75.71168046831512, 13.30751919691132]);
Map.centerObject(geometry, 10)
var s2 = ee.ImageCollection("COPERNICUS/S2_SR");

var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};


// Function to remove cloud and snow pixels from Sentinel-2 SR image
function maskCloudAndShadowsSR(image) {
  var cloudProb = image.select('MSK_CLDPRB');
  var snowProb = image.select('MSK_SNWPRB');
  var cloud = cloudProb.lt(5);
  var snow = snowProb.lt(5);
  var scl = image.select('SCL'); 
  var shadow = scl.eq(3); // 3 = cloud shadow
  var cirrus = scl.eq(10); // 10 = cirrus
  // Cloud probability less than 5% or cloud shadow classification
  var mask = cloud.and(cirrus.neq(1)).and(shadow.neq(1));
  return image.updateMask(mask);
}


// Write a function that computes NDVI for an image and adds it as a band
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
}

var s2Filtered = s2
  .filter(ee.Filter.date('2020-01-01', '2021-01-01'))
  .filter(ee.Filter.bounds(geometry))
  .map(maskCloudAndShadowsSR)
  .map(addNDVI)


var composite = s2Filtered.median()
Map.addLayer(composite, rgbVis, 'Composite')  



// Rainfall
var chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/PENTAD");
var chirpsFiltered = chirps
  .filter(ee.Filter.date('2020-01-01', '2021-01-01'))


// Monsoon months
var months = ee.List.sequence(1, 11)

// Monthly Images
var byMonth = months.map(function(month) {
    var monthlyRain = chirpsFiltered
      .filter(ee.Filter.calendarRange(month, month, 'month'))
    var totalRain = monthlyRain.sum()

    var nextMonth = ee.Number(month).add(1)
    var monthly = s2Filtered
      .filter(ee.Filter.calendarRange(nextMonth, nextMonth, 'month'))
    var medianComposite = monthly.median()
  

    return totalRain.addBands(medianComposite).set({'month': month})
})
var monthlyCol = ee.ImageCollection.fromImages(byMonth)

// Display Composites
var julImage = ee.Image(monthlyCol.filter(ee.Filter.eq('month', 7)).first())
var augImage = ee.Image(monthlyCol.filter(ee.Filter.eq('month', 8)).first())
var sepImage = ee.Image(monthlyCol.filter(ee.Filter.eq('month', 9)).first())

Map.addLayer(julImage, rgbVis, 'July', false)
Map.addLayer(augImage, rgbVis, 'August', false)
Map.addLayer(sepImage, rgbVis, 'September', false)


var correlationCol = monthlyCol.select(['precipitation', 'ndvi'])

var correlation = correlationCol.reduce(ee.Reducer.pearsonsCorrelation());

var positive = correlation.select('correlation').gt(0.5)

Map.addLayer(correlation.select('correlation'), 
  {min:-1, max:1, palette: ['red', 'white', 'green']}, 'Correlation');
Map.addLayer(positive.selfMask(), 
  {palette: ['yellow']}, 'Positive Correlation', false);   

Calculating Band Correlation Matrix

When selecting features for your machine learning model, it is important to have features which are not correlated with each other. Correlated features makes it difficult for machine learning models to discover the interactions between different features. A commonly used technique to aid in removing redundant variables is to create a Correlation Matrix. In Earth Engine, you can take a multi-band image and calculate pair-wise correlation between the bands using either ee.Reducer.pearsonsCorrelation() or ee.Reducer.spearmansCorrelation(). The correlation matrix helps you identify variables that are redundant and can be removed. The code below also shows how to export the table of features that can be used in other software to compute correlation.

Correlation Matrix created in Python using data exported from GEE

Open in Code Editor ↗

// Calculate Pair-wise Correlation Between Bands of an Image

// We take a multi-band composite image created in the previous sections
var composite = ee.Image('users/ujavalgandhi/e2e/arkavathy_multiband_composite');
var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
Map.addLayer(composite, visParams, 'RGB');

var basin = ee.FeatureCollection('WWF/HydroSHEDS/v1/Basins/hybas_7');
var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640));
var geometry = arkavathy.geometry();
Map.centerObject(geometry);

// This image has 18 bands and we want to compute correlation between them.
// Get the band names
// These bands will be the input variables to the model
var bands = composite.bandNames();
print(bands);


// Generate random points to sample from the image
var numPoints = 5000
var samples = composite.sample({ 
  region: geometry,
  scale: 10, 
  numPixels: numPoints,
  tileScale: 16
});
print(samples.first());

// Calculate pairwise-correlation between each pair of bands
// Use ee.Reducer.pearsonsCorrelation() for Pearson's Correlation
// Use ee.Reducer.spearmansCorrelation() for Spearman's Correlation
var pairwiseCorr = ee.FeatureCollection(bands.map(function(i){
  return bands.map(function(j){
    var stats = samples.reduceColumns({
      reducer: ee.Reducer.pearsonsCorrelation(),
      selectors: [i,j]
    });
    var bandNames = ee.String(i).cat('_').cat(j);
    return ee.Feature(null, {'correlation': stats.get('correlation'), 'band': bandNames});  
  });
}).flatten());

// Export the table as a CSV file
Export.table.toDrive({
  collection: pairwiseCorr,
  description: 'Pairwise_Correlation',
  folder: 'earthengine',
  fileNamePrefix: 'pairwise_correlation',
  fileFormat: 'CSV',
});

// You can also export the sampled points and calculate correlation
// in Python or R. Reference Python implementation is at
// https://courses.spatialthoughts.com/python-dataviz.html#feature-correlation-matrix
Export.table.toDrive({
  collection: samples,
  description: 'Feature_Sample_Data',
  folder: 'earthengine',
  fileNamePrefix: 'feature_sample_data',
  fileFormat: 'CSV',
  selectors: bands.getInfo()
});

Calculating Area by Class

This code snippet shows how to use a Grouped Reducer to calculate area covered by each class in a classified image. It also shows how to use the ui.Chart.image.byClass() function to create a chart showing the area for each class.

Open in Code Editor ↗

var classified = ee.Image("users/ujavalgandhi/e2e/bangalore_classified");
var bangalore = ee.FeatureCollection("users/ujavalgandhi/public/bangalore_boundary");

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

// Create a 2 band image with the area image and the classified image
// Divide the area image by 1e6 so area results are in Sq Km
var areaImage = ee.Image.pixelArea().divide(1e6).addBands(classified);

// Calculate Area by Class
// Using a Grouped Reducer
var areas = areaImage.reduceRegion({
      reducer: ee.Reducer.sum().group({
      groupField: 1,
      groupName: 'classification',
    }),
    geometry: bangalore,
    scale: 100,
    tileScale: 4,
    maxPixels: 1e10
    }); 

var classAreas = ee.List(areas.get('groups'))
print(classAreas)

var areaChart = ui.Chart.image.byClass({
  image: areaImage,
  classBand: 'classification', 
  region: bangalore,
  scale: 100,
  reducer: ee.Reducer.sum(),
  classLabels: ['urban', 'bare', 'water', 'vegetation'],
}).setOptions({
  hAxis: {title: 'Classes'},
  vAxis: {title: 'Area Km^2'},
  title: 'Area by class',
  series: {
    0: { color: 'gray' },
    1: { color: 'brown' },
    2: { color: 'blue' },
    3: { color: 'green' }
  }
});
print(areaChart); 

Spectral Signature Plots

For supervised classification, it is useful to visualize average spectral responses for each band for each class. Such charts are called Spectral Response Curves or Spectral Signatures. Such charts helps determine separability of classes. If classes have very different signatures, a classifier will be able to separate them well.

We can also plot spectral signatures of all training samples for a class and check the quality of the training dataset. If all training samples show similar signatures - it indicates that you have done a good job of collecting appropriate samples. You can also catch potential outliers from these plots.

These charts provide a qualitative and visual methods for checking separability of classes. For quantitative methods, one can apply measures such as Spectral Distance, Mahalanobis distance, Bhattacharyya distance , Jeffreys-Matusita (JM) distance etc. You can find the code for these in this Stack Exchange answer.

Mean Signatures for All Classes

Spectral Signatures for All Training Points by Class

Open in Code Editor ↗

var gcps = ee.FeatureCollection("users/ujavalgandhi/e2e/bangalore_gcps");
var composite = ee.Image('users/ujavalgandhi/e2e/bangalore_composite');

// Overlay the point on the image to get bands data.
var training = composite.sampleRegions({
  collection: gcps, 
  properties: ['landcover'], 
  scale: 10
});


// We will create a chart of spectral signature for all classes

// We have multiple GCPs for each class
// Use a grouped reducer to calculate the average reflectance
// for each band for each class

// We have 12 bands so need to repeat the reducer 12 times
// We also need to group the results by class
// So we find the index of the landcover property and use it
// to group the results
var bands = composite.bandNames()
var numBands = bands.length()
var bandsWithClass = bands.add('landcover')
var classIndex = bandsWithClass.indexOf('landcover')

// Use .combine() to get a reducer capable of 
// computing multiple stats on the input
var combinedReducer = ee.Reducer.mean().combine({
  reducer2: ee.Reducer.stdDev(),
  sharedInputs: true})

// Use .repeat() to get a reducer for each band
// We then use .group() to get stats by class
var repeatedReducer = combinedReducer.repeat(numBands).group(classIndex)

var gcpStats = training.reduceColumns({
    selectors: bands.add('landcover'),
    reducer: repeatedReducer,
})

// Result is a dictionary, we do some post-processing to
// extract the results
var groups = ee.List(gcpStats.get('groups'))

var classNames = ee.List(['urban', 'bare', 'water', 'vegetation'])

var fc = ee.FeatureCollection(groups.map(function(item) {
  // Extract the means
  var values = ee.Dictionary(item).get('mean')
  var groupNumber = ee.Dictionary(item).get('group')
  var properties = ee.Dictionary.fromLists(bands, values)
  var withClass = properties.set('class', classNames.get(groupNumber))
  return ee.Feature(null, withClass)
}))

// Chart spectral signatures of training data
var options = {
  title: 'Average Spectral Signatures',
  hAxis: {title: 'Bands'},
  vAxis: {title: 'Reflectance', 
    viewWindowMode:'explicit',
    viewWindow: {
        max:0.6,
        min:0
    }},
  lineWidth: 1,
  pointSize: 4,
  series: {
    0: {color: 'grey'}, 
    1: {color: 'brown'}, 
    2: {color: 'blue'}, 
    3: {color: 'green'},
}};

// Default band names don't sort propertly
// Instead, we can give a dictionary with
// labels for each band in the X-Axis
var bandDescriptions = {
  'B2': 'B02/Blue',
  'B3': 'B03/Green',
  'B4': 'B04/Red',
  'B8': 'B08/NIR',
  'B11': 'B11/SWIR-1',
  'B12': 'B12/SWIR-2'
}
// Create the chart and set options.
var chart = ui.Chart.feature.byProperty({
  features: fc,
  xProperties: bandDescriptions,
  seriesProperty: 'class'
})
.setChartType('ScatterChart')
.setOptions(options);

print(chart)

var classChart = function(landcover, label, color) {
  var options = {
  title: 'Spectral Signatures for ' + label + ' Class',
  hAxis: {title: 'Bands'},
  vAxis: {title: 'Reflectance', 
    viewWindowMode:'explicit',
    viewWindow: {
        max:0.6,
        min:0
    }},
  lineWidth: 1,
  pointSize: 4,
  };

  var fc = training.filter(ee.Filter.eq('landcover', landcover))
  var chart = ui.Chart.feature.byProperty({
  features: fc,
  xProperties: bandDescriptions,
  })
.setChartType('ScatterChart')
.setOptions(options);

print(chart)
}
classChart(0, 'Urban')
classChart(1, 'Bare')
classChart(2, 'Water')
classChart(3, 'Vegetation')

Identify Misclassified GCPs

While doing accuracy assessment, you will see the validation features that were not classified correctly. It is useful to visually see the points that were misclassified. We can use ee.Filter.eq() and ee.Filter.neq() filters to filter the features where the actual and predicted classes were different. The code below shows how to implement this and also use the style() function visualize them effectively.

Open in Code Editor ↗

// Script that shows how to apply filters to identify
// validation points that were misclassified
var s2 = ee.ImageCollection("COPERNICUS/S2_SR");
var basin = ee.FeatureCollection("WWF/HydroSHEDS/v1/Basins/hybas_7");
var gcp = ee.FeatureCollection("users/ujavalgandhi/e2e/arkavathy_gcps");

Map.centerObject(gcp)
var arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))
var boundary = arkavathy.geometry()
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
 
var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(boundary))
  .select('B.*')

var composite = filtered.median().clip(boundary) 

// Display the input composite.
Map.addLayer(composite, rgbVis, 'image');


// Add a random column and split the GCPs into training and validation set
var gcp = gcp.randomColumn()
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));

// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: trainingGcp,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16,
  geometries:true
});
// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50)
.train({
  features: training,  
  classProperty: 'landcover',
  inputProperties: composite.bandNames()
});

// Classify the image.
var classified = composite.classify(classifier);

Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

var test = classified.sampleRegions({
  collection: validationGcp,
  properties: ['landcover'],
  tileScale: 16,
  scale: 10,
  geometries:true
});

var testConfusionMatrix = test.errorMatrix('landcover', 'classification')
print('Confusion Matrix', testConfusionMatrix);

// Let's apply filters to find misclassified points
// We can find all points which are labeled landcover=0 (urban) 
// but were not classified correctly

// We use ee.Filter.and() function to create a combined filter
var combinedFilter = ee.Filter.and(
  ee.Filter.eq('landcover', 0), ee.Filter.neq('classification', 0))
var urbanMisclassified = test.filter(combinedFilter)
print('Urban Misclassified Points', urbanMisclassified)

// We can also apply a filter to select all misclassified points
// Since we are comparing 2 properties agaist each-other,
// we need to use a binary filter
var misClassified = test.filter(ee.Filter.notEquals({
  leftField:'classification', rightField:'landcover'}))
  
print('All Misclassified Points', misClassified)

// Display the misclassified points by styling them
var landcover = ee.List([0, 1, 2, 3])
var palette = ee.List(['gray','brown','blue','green'])
var misclassStyled = ee.FeatureCollection(
  landcover.map(function(lc){
    var feature = misClassified.filter(ee.Filter.eq('landcover', lc))
    var color = palette.get(landcover.indexOf(lc));
    var markerStyle = {color:color}
    return feature.map(function(point){
       return point.set('style', markerStyle)
       })
    })).flatten();
      
Map.addLayer(misclassStyled.style({styleProperty:"style"}), {}, 'Misclassified Points')

Image Normalization and Standardization

For machine learning, it is a recommended practice to either normalize or standardize your features. The code below shows how to implement these feature scaling techniques.

Open in Code Editor ↗

var image = ee.Image("users/ujavalgandhi/e2e/arkavathy_2019_composite");
var boundary = ee.FeatureCollection("users/ujavalgandhi/e2e/arkavathy_boundary")
var geometry = boundary.geometry()

//************************************************************************** 
// Function to Normalize Image
// Pixel Values should be between 0 and 1
// Formula is (x - xmin) / (xmax - xmin)
//************************************************************************** 
function normalize(image){
  var bandNames = image.bandNames();
  // Compute min and max of the image
  var minDict = image.reduceRegion({
    reducer: ee.Reducer.min(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var maxDict = image.reduceRegion({
    reducer: ee.Reducer.max(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var mins = ee.Image.constant(minDict.values(bandNames));
  var maxs = ee.Image.constant(maxDict.values(bandNames));

  var normalized = image.subtract(mins).divide(maxs.subtract(mins))
  return normalized
}

//************************************************************************** 
// Function to Standardize Image
// (Mean Centered Imagery with Unit Standard Deviation)
// https://365datascience.com/tutorials/statistics-tutorials/standardization/
//************************************************************************** 
function standardize(image){
  var bandNames = image.bandNames();
  // Mean center the data to enable a faster covariance reducer
  // and an SD stretch of the principal components.
  var meanDict = image.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var means = ee.Image.constant(meanDict.values(bandNames));
  var centered = image.subtract(means)
  
  var stdDevDict = image.reduceRegion({
    reducer: ee.Reducer.stdDev(),
    geometry: geometry,
    scale: 10,
    maxPixels: 1e9,
    bestEffort: true,
    tileScale: 16
  });
  var stddevs = ee.Image.constant(stdDevDict.values(bandNames));

  var standardized = centered.divide(stddevs);
   
  return standardized
}

var standardizedImage = standardize(image)
var normalizedImage = normalize(image)


Map.addLayer(image, 
  {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3, gamma: 1.2}, 'Original Image');
Map.addLayer(normalizedImage,
  {bands: ['B4', 'B3', 'B2'], min: 0, max: 1, gamma: 1.2}, 'Normalized Image');
Map.addLayer(standardizedImage,
  {bands: ['B4', 'B3', 'B2'], min: -1, max: 2, gamma: 1.2}, 'Standarized Image');
Map.centerObject(geometry)

// Verify Normalization

var beforeDict = image.reduceRegion({
  reducer: ee.Reducer.minMax(),
  geometry: geometry,
  scale: 10,
  maxPixels: 1e9,
  bestEffort: true,
  tileScale: 16
});

var afterDict = normalizedImage.reduceRegion({
  reducer: ee.Reducer.minMax(),
  geometry: geometry,
  scale: 10,
  maxPixels: 1e9,
  bestEffort: true,
  tileScale: 16
});

print('Original Image Min/Max', beforeDict)
print('Normalized Image Min/Max', afterDict)

// Verify Standadization
// Verify that the means are 0 and standard deviations are 1
var beforeDict = image.reduceRegion({
  reducer: ee.Reducer.mean().combine({
      reducer2: ee.Reducer.stdDev(), sharedInputs: true}),
  geometry: geometry,
  scale: 10,
  maxPixels: 1e9,
  bestEffort: true,
  tileScale: 16
});

var resultDict = standardizedImage.reduceRegion({
  reducer: ee.Reducer.mean().combine({
      reducer2: ee.Reducer.stdDev(), sharedInputs: true}),
  geometry: geometry,
  scale: 10,
  maxPixels: 1e9,
  bestEffort: true,
  tileScale: 16
});
// Means are very small franctions close to 0
// Round them off to 2 decimals
var afterDict = resultDict.map(function(key, value) {
  return ee.Number(value).format('%.2f')
})

print('Original Image Mean/StdDev', beforeDict)
print('Standadized Image Mean/StdDev', afterDict)

Calculate Feature Importance

Many classifiers in GEE have a explain() method that calculates feature importances. The classifier will assign a score to each input variable on how useful they were at predicting the correct value. The script below shows how to extract the feature importance and create a chart to visualize it.

Relative Feature Importance

Open in Code Editor ↗

var bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary')
var s2 = ee.ImageCollection('COPERNICUS/S2_SR')
var gcps = ee.FeatureCollection('users/ujavalgandhi/e2e/bangalore_gcps')

var filtered = s2
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
  .filter(ee.Filter.bounds(bangalore))
  .select('B.*')

var composite = filtered.median().clip(bangalore) 


var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B11', 'B8']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B11']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B11'), //swir1
        'Y': image.select('B4'),  //red
        'A': image.select('B8'), // nir
        'B': image.select('B2'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi)
}

composite = addIndices(composite)


// Display the input composite.
var rgbVis = {
  min: 0.0,
  max: 3000,
  bands: ['B4', 'B3', 'B2'],
};
Map.addLayer(composite, rgbVis, 'image');


// Overlay the point on the image to get training data.
var training = composite.sampleRegions({
  collection: gcps, 
  properties: ['landcover'], 
  scale: 10
});


// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(50).train({
  features: training,  
  classProperty: 'landcover', 
  inputProperties: composite.bandNames()
});
// // Classify the image.
var classified = composite.classify(classifier);
Map.addLayer(classified, {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019'); 


//************************************************************************** 
// Calculate Feature Importance
//************************************************************************** 
    
// Run .explain() to see what the classifer looks like
print(classifier.explain())

// Calculate variable importance
var importance = ee.Dictionary(classifier.explain().get('importance'))


// Calculate relative importance
var sum = importance.values().reduce(ee.Reducer.sum())

var relativeImportance = importance.map(function(key, val) {
   return (ee.Number(val).multiply(100)).divide(sum)
  })
print(relativeImportance)

// Create a FeatureCollection so we can chart it
var importanceFc = ee.FeatureCollection([
  ee.Feature(null, relativeImportance)
])

var chart = ui.Chart.feature.byProperty({
  features: importanceFc
}).setOptions({
      title: 'Feature Importance',
      vAxis: {title: 'Importance'},
      hAxis: {title: 'Feature'},
      legend: {position: 'none'}
  })
print(chart)

Time-Series Smoothing and Gap-filling

Moving Window Smoothing

A technique applied to a time series for removal of the fine-grained variation between time steps is known as Smoothing. This example shows how a moving-window smoothing algorithm can be applied in Earth Engine. Using a Save-all Join, the collection is joined with itself and all images that fall within the temporal-window are added as a property of each image. Next, a mean reducer is applied on all the images, resulting in the average value of the pixel within the time-frame. The resulting time-series reduces the sharp peaks and valleys - and is more robust against outliers (such as cloudy pixels)

Moving Window Average Smoothing

Open in Code Editor ↗

// Moving-Window Temporal Smoothing 
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var geometry = ee.Geometry.Point([74.80368345518073, 30.391793042969]);

var startDate = ee.Date.fromYMD(2019, 1, 1);
var endDate = ee.Date.fromYMD(2021, 1, 1);

// Function to add a NDVI band to an image
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
} 

// Function to mask clouds
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask).divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

var originalCollection = s2
  .filter(ee.Filter.date(startDate, endDate))
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .map(addNDVI);

// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: originalCollection.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Original NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },

    })
print(chart);

// Moving-Window Smoothing

// Specify the time-window
var days = 15;

// Convert to milliseconds 
var millis = ee.Number(days).multiply(1000*60*60*24);

// We use a 'save-all join' to find all images 
// that are within the time-window

// The join will add all matching images into a
// new property called 'images'
var join = ee.Join.saveAll({
  matchesKey: 'images'
});

// This filter will match all images that are captured
// within the specified day of the source image
var diffFilter = ee.Filter.maxDifference({
  difference: millis,
  leftField: 'system:time_start', 
  rightField: 'system:time_start'
});


var joinedCollection = join.apply({
  primary: originalCollection, 
  secondary: originalCollection, 
  condition: diffFilter
});

print('Joined Collection', joinedCollection);

// Each image in the joined collection will contain
// matching images in the 'images' property
// Extract and return the mean of matched images
var extractAndComputeMean = function(image) {
  var matchingImages = ee.ImageCollection.fromImages(image.get('images'));
  var meanImage = matchingImages.reduce(
    ee.Reducer.mean().setOutputs(['moving_average']))
  return ee.Image(image).addBands(meanImage)
}

var smoothedCollection = ee.ImageCollection(
  joinedCollection.map(extractAndComputeMean));

print('Smoothed Collection', smoothedCollection)

// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: smoothedCollection.select(['ndvi', 'ndvi_moving_average']),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#66c2a4', lineDashStyle: [1, 1], pointSize: 2}, // Original NDVI
        1: {color: '#238b45', lineWidth: 2 }, // Smoothed NDVI
      },

    })
print(chart);

// Let's export the NDVI time-series as a video
var palette = ['#d73027','#f46d43','#fdae61','#fee08b',
  '#ffffbf','#d9ef8b','#a6d96a','#66bd63','#1a9850'];
var ndviVis = {min:-0.2, max: 0.8,  palette: palette}

Map.centerObject(geometry, 16);
var bbox = Map.getBounds({asGeoJSON: true});

var visualizeImage = function(image) {
  return image.visualize(ndviVis).clip(bbox).selfMask()
}

var visCollectionOriginal = originalCollection.select('ndvi')
  .map(visualizeImage)


var visCollectionSmoothed = smoothedCollection.select('ndvi_moving_average')
  .map(visualizeImage)


Export.video.toDrive({
  collection: visCollectionOriginal,
  description: 'Original_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'original',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox})
  
Export.video.toDrive({
  collection: visCollectionSmoothed,
  description: 'Smoothed_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'smoothed',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox})

Temporal Interpolation

The code below shows how to do temporal gap-filling of time-series data. A detailed explanation of the code and other examples are described in our blog post Temporal Gap-Filling with Linear Interpolation in GEE.

Open in Code Editor ↗

// Temporal Interpolation (Gap-Filling Masked Pixels)
var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var geometry = ee.Geometry.Point([74.80368345518073, 30.391793042969]);

var startDate = ee.Date.fromYMD(2019, 1, 1);
var endDate = ee.Date.fromYMD(2021, 1, 1);

// Function to add a NDVI band to an image
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
} 

// Function to mask clouds
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask).divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

var originalCollection = s2
  .filter(ee.Filter.date(startDate, endDate))
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .map(addNDVI);


// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: originalCollection.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Original NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },

    })
print(chart);

// Gap-filling

// Add a band containing timestamp to each image
// This will be used to do pixel-wise interpolation later
var originalCollection = originalCollection.map(function(image) {
  var timeImage = image.metadata('system:time_start').rename('timestamp')
  // The time image doesn't have a mask. 
  // We set the mask of the time band to be the same as the first band of the image
  var timeImageMasked = timeImage.updateMask(image.mask().select(0))
  return image.addBands(timeImageMasked).toFloat();
})

// For each image in the collection, we need to find all images
// before and after the specified time-window

// This is accomplished using Joins
// We need to do 2 joins
// Join 1: Join the collection with itself to find all images before each image
// Join 2: Join the collection with itself to find all images after each image

// We first define the filters needed for the join

// Define a maxDifference filter to find all images within the specified days
// The filter needs the time difference in milliseconds
// Convert days to milliseconds

// Specify the time-window to look for unmasked pixel
var days = 45;
var millis = ee.Number(days).multiply(1000*60*60*24)

var maxDiffFilter = ee.Filter.maxDifference({
  difference: millis,
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})

// We need a lessThanOrEquals filter to find all images after a given image
// This will compare the given image's timestamp against other images' timestamps
var lessEqFilter = ee.Filter.lessThanOrEquals({
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})

// We need a greaterThanOrEquals filter to find all images before a given image
// This will compare the given image's timestamp against other images' timestamps
var greaterEqFilter = ee.Filter.greaterThanOrEquals({
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})


// Apply the joins

// For the first join, we need to match all images that are after the given image.
// To do this we need to match 2 conditions
// 1. The resulting images must be within the specified time-window of target image
// 2. The target image's timestamp must be lesser than the timestamp of resulting images
// Combine two filters to match both these conditions
var filter1 = ee.Filter.and(maxDiffFilter, lessEqFilter)
// This join will find all images after, sorted in descending order
// This will gives us images so that closest is last
var join1 = ee.Join.saveAll({
  matchesKey: 'after',
  ordering: 'system:time_start',
  ascending: false})
  
var join1Result = join1.apply({
  primary: originalCollection,
  secondary: originalCollection,
  condition: filter1
})
// Each image now as a property called 'after' containing
// all images that come after it within the time-window
print(join1Result.first())

// Do the second join now to match all images within the time-window
// that come before each image
var filter2 = ee.Filter.and(maxDiffFilter, greaterEqFilter)
// This join will find all images before, sorted in ascending order
// This will gives us images so that closest is last
var join2 = ee.Join.saveAll({
  matchesKey: 'before',
  ordering: 'system:time_start',
  ascending: true})
  
var join2Result = join2.apply({
  primary: join1Result,
  secondary: join1Result,
  condition: filter2
})

var joinedCol = join2Result;

// Each image now as a property called 'before' containing
// all images that come after it within the time-window
print(joinedCol.first())
// Do the gap-filling

// We now write a function that will be used to interpolate all images
// This function takes an image and replaces the masked pixels
// with the interpolated value from before and after images.

var interpolateImages = function(image) {
  var image = ee.Image(image);
  // We get the list of before and after images from the image property
  // Mosaic the images so we a before and after image with the closest unmasked pixel
  var beforeImages = ee.List(image.get('before'))
  var beforeMosaic = ee.ImageCollection.fromImages(beforeImages).mosaic()
  var afterImages = ee.List(image.get('after'))
  var afterMosaic = ee.ImageCollection.fromImages(afterImages).mosaic()

  // Interpolation formula
  // y = y1 + (y2-y1)*((t – t1) / (t2 – t1))
  // y = interpolated image
  // y1 = before image
  // y2 = after image
  // t = interpolation timestamp
  // t1 = before image timestamp
  // t2 = after image timestamp
  
  // We first compute the ratio (t – t1) / (t2 – t1)

  // Get image with before and after times
  var t1 = beforeMosaic.select('timestamp').rename('t1')
  var t2 = afterMosaic.select('timestamp').rename('t2')

  var t = image.metadata('system:time_start').rename('t')

  var timeImage = ee.Image.cat([t1, t2, t])

  var timeRatio = timeImage.expression('(t - t1) / (t2 - t1)', {
    't': timeImage.select('t'),
    't1': timeImage.select('t1'),
    't2': timeImage.select('t2'),
  })
  // You can replace timeRatio with a constant value 0.5
  // if you wanted a simple average
  
  // Compute an image with the interpolated image y
  var interpolated = beforeMosaic
    .add((afterMosaic.subtract(beforeMosaic).multiply(timeRatio)))
  // Replace the masked pixels in the current image with the average value
  var result = image.unmask(interpolated)
  return result.copyProperties(image, ['system:time_start'])
}

// map() the function to gap-fill all images in the collection
var gapFilledCol = ee.ImageCollection(joinedCol.map(interpolateImages))
  
// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: gapFilledCol.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Gap-Filled NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },
    })
print(chart);

// Let's visualize the NDVI time-series
Map.centerObject(geometry, 16);
var bbox = Map.getBounds({asGeoJSON: true});

var palette = ['#d73027','#f46d43','#fdae61','#fee08b','#ffffbf','#d9ef8b','#a6d96a','#66bd63','#1a9850'];
var ndviVis = {min:-0.2, max: 0.8,  palette: palette}

var visualizeImage = function(image) {
  return image.visualize(ndviVis).clip(bbox).selfMask()
}

var visCollectionOriginal = originalCollection.select('ndvi')
  .map(visualizeImage)

var visualizeIGapFilled = gapFilledCol.select('ndvi')
  .map(visualizeImage)


Export.video.toDrive({
  collection: visCollectionOriginal,
  description: 'Original_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'original',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox})

Export.video.toDrive({
  collection: visualizeIGapFilled,
  description: 'Gap_Filled_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'gap_filled',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox}) 

Savitzky-Golay Smoothing

The Savitzky–Golay filter fits a polynomial to a set of data points in a time-series. The Open Earth Engine Library (OEEL) provides an efficient implementation of this filter that can be applied on an ImageCollection. However, the time-series must be pre-processed so there are images at a regular interval. We use the interpolation technique described in the previous section and prepare a continous time-series without any masked pixels. The result is a new ImageCollection containing images at a regular interval (5-day) and with pixel values smoothed using the Savitzky–Golay filter.

Savitzky-Golay Smoothing

Open in Code Editor ↗

// Aplying Savitzky-Golay Filter on a NDVI Time-Series
// This script uses the OEEL library to apply a 
// Savitzky-Golay filter on a imagecollection

// We require a regularly-spaced time-series without
// any masked pixels. So this script applies
// linear interpolation to created regularly spaced images
// from the original time-series

// Step-1: Prepare a NDVI Time-Series
// Step-2: Create an empty Time-Series with images at n days
// Step-3: Use Joins to find before/after images
// Step-4: Apply linear interpolation to fill each image
// Step-5: Apply Savitzky-Golay filter
// Step-6: Visualize the results

var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
var geometry = ee.Geometry.Point([74.80368345518073, 30.391793042969]);

var startDate = ee.Date.fromYMD(2019, 1, 1);
var endDate = ee.Date.fromYMD(2021, 1, 1);

// Function to add a NDVI band to an image
function addNDVI(image) {
  var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
} 

// Function to mask clouds
function maskS2clouds(image) {
  var qa = image.select('QA60')
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0))
  return image.updateMask(mask).divide(10000)
      .select("B.*")
      .copyProperties(image, ["system:time_start"])
}

var originalCollection = s2
  .filter(ee.Filter.date(startDate, endDate))
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
  .filter(ee.Filter.bounds(geometry))
  .map(maskS2clouds)
  .map(addNDVI);


// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: originalCollection.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Original NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },

    })
print(chart);

// Prepare a regularly-spaced Time-Series

// Generate an empty multi-band image matching the bands
// in the original collection
var bandNames = ee.Image(originalCollection.first()).bandNames();
var numBands = bandNames.size();
var initBands = ee.List.repeat(ee.Image(), numBands);
var initImage = ee.ImageCollection(initBands).toBands().rename(bandNames)

// Select the interval. We will have 1 image every n days
var n = 5;
var firstImage = ee.Image(originalCollection.sort('system:time_start').first())
var lastImage = ee.Image(originalCollection.sort('system:time_start', false).first())
var timeStart = ee.Date(firstImage.get('system:time_start'))
var timeEnd = ee.Date(lastImage.get('system:time_start'))

var totalDays = timeEnd.difference(timeStart, 'day');
var daysToInterpolate = ee.List.sequence(0, totalDays, n)

var initImages = daysToInterpolate.map(function(day) {
  var image = initImage.set({
    'system:index': ee.Number(day).format('%d'),
    'system:time_start': timeStart.advance(day, 'day').millis(),
    // Set a property so we can identify interpolated images
    'type': 'interpolated'
  })
  return image
})

var initCol = ee.ImageCollection.fromImages(initImages)
print('Empty Collection', initCol)

// Merge original and empty collections
var originalCollection = originalCollection.merge(initCol)

// Interpolation

// Add a band containing timestamp to each image
// This will be used to do pixel-wise interpolation later
var originalCollection = originalCollection.map(function(image) {
  var timeImage = image.metadata('system:time_start').rename('timestamp')
  // The time image doesn't have a mask. 
  // We set the mask of the time band to be the same as the first band of the image
  var timeImageMasked = timeImage.updateMask(image.mask().select(0))
  return image.addBands(timeImageMasked).toFloat();
})

// For each image in the collection, we need to find all images
// before and after the specified time-window

// This is accomplished using Joins
// We need to do 2 joins
// Join 1: Join the collection with itself to find all images before each image
// Join 2: Join the collection with itself to find all images after each image

// We first define the filters needed for the join

// Define a maxDifference filter to find all images within the specified days
// The filter needs the time difference in milliseconds
// Convert days to milliseconds

// Specify the time-window to look for unmasked pixel
var days = 45;
var millis = ee.Number(days).multiply(1000*60*60*24)

var maxDiffFilter = ee.Filter.maxDifference({
  difference: millis,
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})

// We need a lessThanOrEquals filter to find all images after a given image
// This will compare the given image's timestamp against other images' timestamps
var lessEqFilter = ee.Filter.lessThanOrEquals({
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})

// We need a greaterThanOrEquals filter to find all images before a given image
// This will compare the given image's timestamp against other images' timestamps
var greaterEqFilter = ee.Filter.greaterThanOrEquals({
  leftField: 'system:time_start',
  rightField: 'system:time_start'
})


// Apply the joins

// For the first join, we need to match all images that are after the given image.
// To do this we need to match 2 conditions
// 1. The resulting images must be within the specified time-window of target image
// 2. The target image's timestamp must be lesser than the timestamp of resulting images
// Combine two filters to match both these conditions
var filter1 = ee.Filter.and(maxDiffFilter, lessEqFilter)
// This join will find all images after, sorted in descending order
// This will gives us images so that closest is last
var join1 = ee.Join.saveAll({
  matchesKey: 'after',
  ordering: 'system:time_start',
  ascending: false})
  
var join1Result = join1.apply({
  primary: originalCollection,
  secondary: originalCollection,
  condition: filter1
})
// Each image now as a property called 'after' containing
// all images that come after it within the time-window
print(join1Result.first())

// Do the second join now to match all images within the time-window
// that come before each image
var filter2 = ee.Filter.and(maxDiffFilter, greaterEqFilter)
// This join will find all images before, sorted in ascending order
// This will gives us images so that closest is last
var join2 = ee.Join.saveAll({
  matchesKey: 'before',
  ordering: 'system:time_start',
  ascending: true})
  
var join2Result = join2.apply({
  primary: join1Result,
  secondary: join1Result,
  condition: filter2
})

// Each image now as a property called 'before' containing
// all images that come after it within the time-window
print(join2Result.first())

var joinedCol = join2Result;

// Do the interpolation

// We now write a function that will be used to interpolate all images
// This function takes an image and replaces the masked pixels
// with the interpolated value from before and after images.

var interpolateImages = function(image) {
  var image = ee.Image(image);
  // We get the list of before and after images from the image property
  // Mosaic the images so we a before and after image with the closest unmasked pixel
  var beforeImages = ee.List(image.get('before'))
  var beforeMosaic = ee.ImageCollection.fromImages(beforeImages).mosaic()
  var afterImages = ee.List(image.get('after'))
  var afterMosaic = ee.ImageCollection.fromImages(afterImages).mosaic()

  // Interpolation formula
  // y = y1 + (y2-y1)*((t – t1) / (t2 – t1))
  // y = interpolated image
  // y1 = before image
  // y2 = after image
  // t = interpolation timestamp
  // t1 = before image timestamp
  // t2 = after image timestamp
  
  // We first compute the ratio (t – t1) / (t2 – t1)

  // Get image with before and after times
  var t1 = beforeMosaic.select('timestamp').rename('t1')
  var t2 = afterMosaic.select('timestamp').rename('t2')

  var t = image.metadata('system:time_start').rename('t')

  var timeImage = ee.Image.cat([t1, t2, t])

  var timeRatio = timeImage.expression('(t - t1) / (t2 - t1)', {
    't': timeImage.select('t'),
    't1': timeImage.select('t1'),
    't2': timeImage.select('t2'),
  })
  // You can replace timeRatio with a constant value 0.5
  // if you wanted a simple average
  
  // Compute an image with the interpolated image y
  var interpolated = beforeMosaic
    .add((afterMosaic.subtract(beforeMosaic).multiply(timeRatio)))
  // Replace the masked pixels in the current image with the average value
  var result = image.unmask(interpolated)
  return result.copyProperties(image, ['system:time_start'])
}

// map() the function to interpolate all images in the collection
var interpolatedCol = ee.ImageCollection(joinedCol.map(interpolateImages))

// Once the interpolation are done, remove original images
// We keep only the generated interpolated images
var regularCol = interpolatedCol.filter(ee.Filter.eq('type', 'interpolated'))


// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: regularCol.select('ndvi'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      title: 'Regular NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#238b45'},
      },
    })
print(chart);


// SavatskyGolayFilter
// https://www.open-geocomputing.org/OpenEarthEngineLibrary/#.ImageCollection.SavatskyGolayFilter

// Use the default distanceFunction
var distanceFunction = function(infromedImage, estimationImage) {
  return ee.Image.constant(
      ee.Number(infromedImage.get('system:time_start'))
      .subtract(
        ee.Number(estimationImage.get('system:time_start')))
        );
  }

// Apply smoothing

var oeel=require('users/OEEL/lib:loadAll');

var order = 3;

var sgFilteredCol = oeel.ImageCollection.SavatskyGolayFilter(
  regularCol, 
  maxDiffFilter,
  distanceFunction,
  order)

print(sgFilteredCol.first())
// Display a time-series chart
var chart = ui.Chart.image.series({
  imageCollection: sgFilteredCol.select(['ndvi', 'd_0_ndvi'], ['ndvi', 'ndvi_sg']),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 20
}).setOptions({
      lineWidth: 1,
      title: 'NDVI Time Series',
      interpolateNulls: false,
      vAxis: {title: 'NDVI', viewWindow: {min: 0, max: 1}},
      hAxis: {title: '', format: 'YYYY-MM'},
      lineWidth: 1,
      pointSize: 4,
      series: {
        0: {color: '#66c2a4', lineDashStyle: [1, 1], pointSize: 2}, // Original NDVI
        1: {color: '#238b45', lineWidth: 2 }, // Smoothed NDVI
      },

    })
print(chart);


// Let's visualize the NDVI time-series
Map.centerObject(geometry, 16);
var bbox = Map.getBounds({asGeoJSON: true});

var palette = ['#d73027','#f46d43','#fdae61','#fee08b','#ffffbf','#d9ef8b','#a6d96a','#66bd63','#1a9850'];
var ndviVis = {min:-0.2, max: 0.8,  palette: palette}

var visualizeImage = function(image) {
  return image.visualize(ndviVis).clip(bbox).selfMask()
}

var visCollectionRegular = regularCol.select('ndvi')
  .map(visualizeImage)

var visualizeSgFiltered = sgFilteredCol.select('d_0_ndvi')
  .map(visualizeImage)


Export.video.toDrive({
  collection: visCollectionRegular,
  description: 'Regular_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'regular',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox})
  
Export.video.toDrive({
  collection: visualizeSgFiltered,
  description: 'Filtered_Time_Series',
  folder: 'earthengine',
  fileNamePrefix: 'sg_filtered',
  framesPerSecond: 2,
  dimensions: 800,
  region: bbox})

User Interface Templates

Adding a Discrete Legend

You may want to add a legend for a classified image to your map visualization in your App. Here’s a code snippet that shows how you can build it using the UI Widgets.

Creating a Discrete Map Legend

Open in Code Editor ↗

var classified = ee.Image("users/ujavalgandhi/e2e/bangalore_classified")
Map.centerObject(classified)
Map.addLayer(classified,
  {min: 0, max: 3, palette: ['gray', 'brown', 'blue', 'green']}, '2019');

var legend = ui.Panel({style: {position: 'middle-right', padding: '8px 15px'}});

var makeRow = function(color, name) {
  var colorBox = ui.Label({
    style: {color: '#ffffff',
      backgroundColor: color,
      padding: '10px',
      margin: '0 0 4px 0',
    }
  });
  var description = ui.Label({
    value: name,
    style: {
      margin: '0px 0 4px 6px',
    }
  }); 
  return ui.Panel({
    widgets: [colorBox, description],
    layout: ui.Panel.Layout.Flow('horizontal')}
)};

var title = ui.Label({
  value: 'Legend',
  style: {fontWeight: 'bold',
    fontSize: '16px',
    margin: '0px 0 4px 0px'}});
    
legend.add(title);
legend.add(makeRow('gray','Built-up'))
legend.add(makeRow('brown','Bare Earth'))
legend.add(makeRow('blue','Water'))
legend.add(makeRow('green','Vegetation'))

Map.add(legend);

Adding a Continous Legend

If you are displaying a raster layer in your app with a color palette, you can use the following technique to add a colorbar using the snipet below.

Creating a Continuous Raster Legend