Assignment A: Remote Sensing Theory - Earth Engine

Investigating Landsat Data

Purpose: The purpose of this lab is to enable you to search, find and visualize remotely sensed imagery in Google Earth Engine.  At completion, you should be able to understand the difference between radiance and reflectance, load imagery with the units of interest (radiance or reflectance, for example), make true color and false color composites and identify land cover types based on spectral characteristics.

Searching (and finding) Landsat imagery

Landsat is a NASA program that has launched a sequence of Earth observation satellites, named Landsat 1, 2,... etc.  We will discuss the Landsat program in much more detail later this semester, but we will start here by using some of the data.

Note that the Landsat program has resulted in the longest continuous observation of the Earth's surface.  In this exercise, you will load a Landsat scene over your area of interest, inspect the units and make a plot of radiance.   Specifically, use imagery from the Landsat 8, the most recent of the sequence of Landsat satellites.  To inspect a Landsat 8 image (also called a scene) in your region of interest (ROI), define your ROI as a point, filter the image collection to get a scene with few clouds, display some information about the image in the console.

Landsat 8 measures different ranges of wavelengths along the electromagnetic spectrum. Each of these ranges in known as a band and in total Landsat 8 has 11 bands. The first 7 of these bands are in the visible and infrared part of the spectrum and are commonly known as the "reflective bands" and are captured by the Operational Land Imager (OLI) on board Landsat 8. In addition to the 7 bands, there is also a panchromatic or black-and-white band (Band 8) and a cirrus cloud band (Band 9) that is used to detect cirrus clouds. Landsat 8 also has a Thermal Infrared Sensor (TIRS) which collects data in two thermal infrared bands. 

• Search for 'Oklahoma City' in the playground search bar and click the result to pan and zoom the map to Oklahoma City.  
• Use the geometry tools (Links to an external site.) (top left above the map) to make a point in Oklahoma City (Exit the drawing tool when you're finished).  Name the resultant import (Links to an external site.) 'point' by clicking on the import name ('geometry' by default).

• Search for 'landsat 8 raw' and import the 'USGS Landsat 8 Collection 1 Tier 1 Raw Scenes' ImageCollection.  Name the import 'landsat'.
• Filter the ImageCollection by date and location, sort by a metadata property called 'CLOUD_COVER' and get the first image out of this sorted collection:

// Note that we need to cast the result of first() to Image.
var image = ee.Image(landsat
// Filter to get only images in the specified range. 
.filterDate('2014-01-01', '2014-12-31')
// Filter to get only images at the location of the point.
.filterBounds(point) 
// Sort the collection by a metadata property. 
.sort('CLOUD_COVER')
// Get the first image out of this collection.
.first()); 

• The variable image now stores a reference to an object of type ee.Image.  Display a human-readable representation of the image by printing it to the console:

// Print the image to the console.
print('A Landsat scene:', image);