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PreLab - Intro to GEE


This is a pre-lab designed to introduce the functionality and structure of Google Earth Engine (GEE) before we get into the practical labs. The topcis we will cover are:

  • Brief introduction to the GEE JavaScript interface (the code editor)
  • GEE resources at your disposal
  • GEE Python Environment
  • Geospatial packages within your Python Environment

By the end of this lab, you should be able to access GEE imagery, build some basic visualizations and be comfortable working with both the code editor and Python. This lab contains many different outside sources - spending the time now getting associated with the tools we'll be using will save you significant time as we delve into more complex topics.

The JavaScript code editor is easier to get up and running, while the Python has more extensive data and analytical tools available, and is more commonly used throughout the Remote Sensing industry. Both are important and when working with GEE, you will spend significant amounts of time using the JavaScript. Each code block within these notebooks is in Python, but some parts of this lab references JavaScript (mainly outputs and things printed in the console). In later labs, we will focus on Python.

Learning Outcomes

  • Navigate and find what you need within the Google Earth Engine infrastructure
  • Describe and understand the major GEE data types and their associated methods
  • Build custom vector data within GEE

Video Introduction to GEE

Getting Set Up

In addition to the petabytes of satellite imagery and products that GEE has available, it allows you to incorporate your own raster, vector, and relational data into your analysis. Note that when using the code editor this process is automatically linked to the same Google Drive account that signed up for GEE. Using Python has more flexibility and is easier to incorporate outside information in a variety of formats.

If you are not familiar with Google Drive, the Getting Started Guide reviews the basics of maintaining resources within your Google Drive account. Although Google Cloud Platform Storage is beyond the scope of this course, it's an option and discussed in the documentation. We will go more in-depth on working with external data later, but below are some good resources to peruse.

Gecomputation with GEE: Server vs. Client

Understanding how Google Earth Engine works is critical for its effective use. The Developer's overview provides much more detail on the intricacies of how GEE processes data on the Google Cloud Platform, but in the simplest terms, there are two sides to the process - the client side and server side.

When you run code in a code block or the code editor, that is considered the client side. You can write code in the editor or your notebook and the code will be processed completely within your browser or local machine. The code chunk below simply creates variables x and y, adds them together as the variable z and prints the result. Google Earth Engine plays no role in the execution of the code.

var x = 1; var y = 2;
var z = x + y;

To begin using the cloud computing resources of GEE, we have to call upon the server side. Let's say we want to import an image collection. In the snippet below, there is an ee before the ImageCollection constructor. In simple terms, this signals to Earth Engine that we will be using its resources. Without that indicator, GEE does not play a role in operations.

var sentinelCollection = ee.ImageCollection('COPERNICUS/S2_SR');

Over time, you will gain experience understanding the role of working with client side and the server side operations, but the main point in this section is that when programming, we will be building 'packages' that draw upon GEE resources to complete their operations.

An extension of this topic is listed here, along with discussions of some specific programming topics (mapping instead of looping) - it might be advanced, but the bottom line is understand that the client and server work together to create an output.


The intent of this course is not to teach the intricacies of programming within JavaScript. JavaScript is the core language for web development and many of the tutorials and resources you might find online will not be directly relevant to the type of JavaScript that you will need to work in Earth Engine (e.g., React, JQuery, dynamic app development). JavaScript was chosen because it is an extremely popular language (~97% of websites use it in some fashion) and as an object-oriented language, it is well-suited to pair objects (in this case, imagery provided by Google Earth Engine) with methods (such as using the reduce() function to summarize the analytical information from a processed image).

Several excellent resources exist that can help you in working with JavaScript. One such resource is, which provides a thorough overview of working with JavaScript. In this tutorial, focus on part I, as part II and III are directed towards web development and not relevant for this purpose.

W3Schools provides good information on each individual component of working with JavaScript. For instance, if you see the word var and wanted more information on it, W3Schools has helpful definitions and code snippets that will be of use.

Finally, JavaScript & JQuery is an excellent, well-designed book that goes through the fundamentals of working with JavaScript and provides helpful illustrations and use cases. The second half of the book is outside the scope of this course, but if you did want to extend your skill-set, this book is a great starting point.

Bottom line: The code editor uses JavaScript, and you are more than welcome to learn more - but for the purposes of this course, a basic understanding the language is adequate.

Data and Methods

Working with data structures and their associated methods is essential to understanding Google Earth Engine.

Most Google Earth Engine tutorials begin with an introduction to the data you will be working with and the operations you can use to analyze this data. Each bullet point below contains a link to the GEE documentation - it is well worth your time to read through this as thoroughly as possible and get familar with these key terms.

Intro to Data

  • Image
    • A singe raster image consisting of values and their associated values
  • ImageCollection
    • A "stack" or sequence of images with the same attributes
  • Geometry
    • Vector data either built within Earth Engine or imported
    • Points, lines, polygons
  • Feature
    • geometry with associated attributes
    • An example would be a geometric point associated with the city of Paris
  • FeatureCollection
    • A set of features that share a theme
    • An example is a list of geometric points that describe all the capitals of the world
  • Reducer
    • A method used to compute statistics or perform aggregations on data over space, time, bands, arrays, and other data structures
    • An example - aggregate the mean pixel value from an image for each neighborhood polygon
  • Join
    • A method to combine datasets (Image or Feature collections) based on time, location, or specified attribute
  • Array
    • A flexible (albeit sometimes inefficient) data structure that can be used for multi-dimensional analyses.


Below is a simple flow chart of how the raster and vector data works together. Throughout this course we will expand upon this and go into how this all works together to extract data.

Sensed Versus Derived

Images and Image Collections


Images are Raster objects composed of:

  • Bands, or layers with a unique:

    • Name
    • Data type
    • Scale
    • Mask
    • Projection
  • Metadata, stored as a set of properties for that band.

You can create images from constants, lists, or other objects. In the code editor 'docs', you'll find numerous processes you can apply to images. Ensure that you do not confuse an individual image with an image collection, which is a set of images grouped together, most often as a time series, (also known as a stack).

Image Collections

Let's analyze the code below (Python), which extracts one individual image from an image collection.

On the first line, we see that we are creating a variable named image, and then using ee in front of ImageCollection, which signifies we are requesting information from GEE. The data we are importing ('COPERNICUS/S2_SR') is the Sentinel-2 MSI: MultiSpectral Instrument, Level-2A, with more information found in the dataset documentation.

The next four steps refine the extraction of an image from an image collection.

  1. .filterBounds filters data to the area specified, in this case a geometry Point that was created within GEE.

  2. .filterDate filters between the two dates specified (filtering down to images collected in 2019)

  3. .sort organizes the image collection in descending order based upon the percentage of cloudy pixels

    1. This is an attribute of the image, which can be found in the 'Image Properties' tab in the dataset documentation
  4. .first is an earth engine method of choosing the first image from the list of sorted images

As a result, we can now use the variable 'first' to visualize the image.

Map.centerObject() centers the map on the image, and the number is the amount of zoom. The higher that value is, the more zoomed in the image is - you'll likely have to adjust via trial-and-error to find the best fit.

Map.build_map() adds the visualization layer to the map. Images and image collections each have a unique naming convention of their bands, so you will have to reference the documentation for each one you use. GEE uses Red-Green-Blue ordering (as opposed to the popular Computer Vision framework, OpenCV, which uses a Blue-Green-Red convention). min and max are the values that normalize the value of each pixel to the conventional 0-255 color scale. In this case, although the maximum value of a pixel in all three of those bands is 2000, for visualization purposes GEE will normalize that to 255, the max value in a standard 8-bit image.

There is a comprehensive guide to working on visualization with different types of imagery that goes quite in-depth. It is a worthwhile read and covers some interesting topics such as false-color composites, mosaicking and single-band visualization. Work with some of the code-snippets to understand how to build visualizations for different sets of imagery.

// Code Chunk 1
var lat = 13.7; var lon = 2.54; var zoom = 11
var first = ee.ImageCollection('COPERNICUS/S2_SR')
.filterBounds(ee.Geometry.Point(2.54, 13.7))
.filterDate('2019-01-01', '2019-12-31')
// Define a map centered in Niger
Map.centerObject(first, zoom);
Map.addLayer(first, {bands: ['B4', 'B3', 'B2'], min: 0, max: 3300}, 'first');

map of Niger

Sensed versus Derived Imagery

One additional note: GEE provides a rich suite of datasets, and while many of them are traditional sensed imagery (shows reality as it is), others are derived datasets. For instance, the Global Map of Oil Palm Plantations dataset is derived from analysis using the Sentinel composite imagery. If you look at the bands, there are only three values, which refer to categories of palm plantations (industrical Palm Oil Plantation, small farm Palm Oil Plantation or not palm oil). Datasets such as these will have different methods for visualizing the data. As you can see below, this derived dataset is different than typical satellite imagery - the intent is to classify each 10m pixel value as one of the above categories.

Sensed Versus Derived

National Cropland Data Layer

Another common one is the National Cropland Data Layer - each pixel has 30m resolution, and defines the cropland type for the United States. Not all derived datasets are available all over the world, being that many are sponsored by government agencies acting in the purview of their own country. Explore the map below and match the code to the cropland type.

// Code Chunk 2
var lat = 40.71; var lon = -100.55; var zoom = 11
var image = (ee.ImageCollection('USDA/NASS/CDL')
.filter('2018-01-01', '2019-12-31'))
.filterBounds(ee.Geometry.Point(lon, lat))
var image ='cropland')
Map.centerObject(image, zoom);
Map.addLayer(image, {}, 'NLCD');



Google Earth Engine handles vector data with the geometry data structure. Traditionally, this follows the basics of vector data, broadly:

  • Point
  • Line
  • Polygon

However, GEE has several different nuances.

  • Point
  • LineString
    • List of points that do not start and end at the same location
  • LinearRing
    • LineString which starts and ends at the same location
  • Polygon
    • List of LinearRing's - first item of the list is the outer shell and other components of the list are interior shells

GEE also recognizes MultiPoint, MultiLineString and MultiPolygon, which are simply collections of more than one element. Additionally, you can combine any of these together to form a MultiGeometry. Here is a quick video of working with the geometry tools within GEE.

Once you have a set of geometries, there are geometric operations you can use for analysis, such as building buffer zones, area analysis, rasterization, etc. The documentation contains examples to show you how to get started, and all of the functions are listed under the 'docs' tab in the Code Editor.

Features and Feature Collections


A Feature in GEE is an object which stores a geometry (Point, Line, Polygon) along with its associated properties. GEE uses the GeoJSON data format to store and transmit these features. In the previous video, we saw how to build geometries within Google Earth Engine, while a feature adds meaningful information to it. This would be a good section to review working with dictionaries within JavaScript.

Let's say we created an individual point, which we want to associate with data that we collected. The first line establishes the variable point, which is then used as the geometry to create a feature. The curly braces represent a dictionary, which creates Key:Value pairs, which in our case is the type of tree and a measurement of the size. This new variable, treeFeature, now contains geographic information along with attribute data about that point.

// Earth Engine Geometry
var point = ee.Geometry.Point([-79.68, 42.06]);
// Create a Feature from the geometry
var treeFeature = ee.Feature(point, {type: 'Pine', size: 15});

Obviously this is just one point, but JavaScript and GEE engine provide functionality for bringing different data sources together and automatically associating geometries with attribute data. This can be done within GEE or outside, depending on your preferences.

Feature Collections

Just like the relationship between images and image collections, feature collections are features that can be grouped together for ease of use and analysis. They can be different types and combinations of geometry, as well as associated tabular data. The code segment from the documentation consolidates the operations discussed earlier. Each line has an interior layer which creates the geometry, which is then associated with attribute data (information within the {} ) and then converted to a Feature. This variable is a list, which contains three separate, individual features. This is then converted to a feature collection with the command ee.FeatureCollection(features)

// Make a list of Features.
var features = [
ee.Feature(ee.Geometry.Rectangle(30.01, 59.80, 30.59, 60.15), {name: 'Voronoi'}),
ee.Feature(ee.Geometry.Point(-73.96, 40.781), {name: 'Thiessen'}),
ee.Feature(ee.Geometry.Point(6.4806, 50.8012), {name: 'Dirichlet'})

// Create a FeatureCollection from the list and print it.
var fromList = ee.FeatureCollection(features);

If run this code block in GEE code editor, you can see the information that is contained within the Feature Collection - three elements (features) and two columns (the index and the properties). By clicking on the dropdown next to each one, you can see that the first feature is a Polygon that has the name of 'Voronoi'.

Feature Collection Information

Once you have information in a Feature Collection, you can filter it to find specific information, such as the name of an object or based on the size of a polygon, or provide aggregated analysis. The documentation on working with Feature Collections is comprehensive and provides many ideas on how to use them efficiently in in your analysis.

Methods: Reducers

Up until now, we have focused on objects: Images, Features, and Geometries. Reducers are a method of aggregating data for analysis. For instance, we could take an Image Collection and use reducer to find the average value of the magnitude of each pixel across all the images of the collection, simplifying the data into a single layer. Or we could reduce an image to a set of regions, grouping similar data together to create an aggregated map. The applications of Reducer are endless, and can be applied to both Images and Features. There are different functions for different object types, and Reducer can be combined and sequenced to create a chain of analysis. From the documentation, the code chunk below creates the variable max which is the maximum elevation (in meters) of the imagery within our bounding box.

// Code Chunk 3A
var lat = 13.7; var lon = 2.54; var zoom = 9
// The input image to reduce, in this case an SRTM elevation map.
var image = ee.Image('CGIAR/SRTM90_V4');
Map.centerObject(ee.Geometry.Point(lon, lat), zoom);
Map.addLayer(image, {'min':0, 'max':800}, 'Shuttle Radar Topography Mission (SRTM)')

Elevation Map

// Code Chunk 3B
// Build a polygon within the country of Niger in GEE Code Editor
var poly = ee.Geometry.Polygon(
[[[1.0381928005666774, 23.471775399486358],
[1.0381928005666774, 12.477838833503146],
[15.825790456816677, 12.477838833503146],
[15.825790456816677, 23.471775399486358]]]

//Reduce the image within the given region, using a reducer that
// computes the max pixel value. We also specify the spatial
// resolution at which to perform the computation, in this case 200
// meters.
var max = image.reduceRegion({
reducer: ee.Reducer.max(),
geometry: poly,
maxPixels: 1e10,
scale: 200
// Print the result (a Dictionary) to the console.

We have successfully calculated the maximum elevation in an area about the size of Niger in under 1 second! In future projects, we will calculate a wide range of usable values incredibly quickly.

There are hundreds of different operations for using Reducer, with the functions listed on the left hand table under 'Docs'. Certain functions will only work with specific object types, but follow along with the Reducer documentation to get a better understanding of how to aggregate data and extract meaningful results. Getting familiar with Reducer is an essential component to working with Google Earth Engine.

Joins and Arrays


If you have programmed in the past, joining data together is a familiar concept. This process associates information from different dataset together. Let's say you have an Image Collection of Landsat data that is filtered to the first six months of the year 2016 and a bounding box of your area of study. You also have a table of Redwood tree locations that is filtered to the same area of study, although it contains information over the past decade. You can use a Join to associate information about the trees from the Feature Collection and include it in the Image Collection, keeping only the relevant data that falls within that timeframe. You now have a consolidated dataset with useful information from both the Image Collection and Feature Collection. Although there are different types of joins, the process brings information together, keeping only relevant information. The documentation on Joins goes over specific examples and concepts, but a crucial component is understanding the type of join you need the three most prominent within GEE are:

  • Left Join
    • Keeps all the information from the primary dataset, and only information that joins from the secondary dataset
  • Inner Join
    • Keeps only the information where the primary and secondary data match
  • Spatial Join
    • A join based on spatial location (e.g., keep only the geometry points that fall within a polygon)

GEE provides some unique types of joins, including 'Save-All', 'Save-Best' and 'Save-First', which are useful if you want to look at a specific area.


Arrays are a collection of data where information is stored contiguously - matrices are a multi-dimensional array. For instance, an image might have 1024 rows and 1024 columns. Each row is an array, each column is an array, and taken together, you have a 2-dimensional array, also known as a matrix. If the image has three separate color channels, then that is a 3-dimensional array. Some of the terminology changes depending on discipline (ie, physics vs. computer science), but if you are familiar with working with matrices and arrays in programming languages such as Matlab, NumPY or OpenCV, it is important to understand the role of arrays within GEE.

In fact, Google Earth Engine states that working with arrays outside of the established functions that they have built is not recommended, as GEE is not specifically designed for array-based math, and will lead to unoptimized performance.

There is a very informative video that delves into the engineering behind Google Earth Engine, but in this course we will only be doing a limited amount with array transformations and Eigen Analysis. In many cases, you will probably be better off aggregating the specific data and then conducting array mathematics with programming languages more geared for this (Python, R, MatLab).