Google Earth Engine provides cloud-computing platform for Remote Sensing analysis. There are many datasets available. The following images are mostly used images.
- Landsat 8
Landsat 8 is multi-spectral image. Check how to make image composite here. The script below will show the collection of Landsat 8 image in the whole year of 2014. Landsat 8 is available starting from February 2013.
// Composite an image collection and clip it to a boundary.
var i;
var j;
// set date 2015 - 2019
var tahun = [2014];
var bulan = [1,1,2,2,3,3, 4,4,5,5,6,6, 7,7,8,8,9,9, 10,10,11,11,12,12];
var tgl1 = [1,16,1,16,1,16, 1,16,1,16,1,16, 1,16,1,16,1,16, 1,16,1,16,1,16];
var tgl2 = [15,31,15,28,15,31, 15,30,15,31,15,30, 15,31,15,31,15,30, 15,31,15,30,15,31];
// Load every Landsat 8 raw imagery in 2014.
for (i = 0; i<tahun.length;i++){
for(j = 0; j<bulan.length;j++){
var composite = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterDate( tahun[i] + "-" + bulan[j] + "-" + tgl1[j], tahun[i] + "-" + bulan[j] + "-" + tgl2[j])
.median();
// Clip to the output image to the geometry boundary.
var clipped = composite.clip(geometry);
Map.addLayer(clipped , {bands: ['B6', 'B5', 'B4']}, "date " + tahun[i] + "-" + bulan[j] + "-" + tgl1[j], false );
}
}
// Display the result.
Map.setCenter(113.517, -8.1631, 9);
Map.addLayer(clipped, {color: 'FFFFFF'}, 'boundary', false);
//print('Polygon area: ', geometry.area().divide(100 * 100));
// Export the FeatureCollection to a SHP file.
Export.table.toDrive({
collection: clipped,
description:'Mount Bromo',
fileFormat: 'SHP'
});
- Landsat 7.
Landsat 7 was launched in April 1999. The script below show Landsat images for the 1 hole year. The latest Landsat 7 images have interrupting black stripes.
// Composite an image collection and clip it to a boundary.
var i;
var j;
// set year 2011
var tahun = [2011];
var bulan = [1,1,2,2,3,3, 4,4,5,5,6,6, 7,7,8,8,9,9, 10,10,11,11,12,12];
var tgl1 = [1,16,1,16,1,16, 1,16,1,16,1,16, 1,16,1,16,1,16, 1,16,1,16,1,16];
var tgl2 = [15,31,15,28,15,31, 15,30,15,31,15,30, 15,31,15,31,15,30, 15,31,15,30,15,31];
// Load every Landsat 7 raw imagery in 2011.
for (i = 0; i<tahun.length;i++){
for(j = 0; j<bulan.length;j++){
var composite = ee.ImageCollection('LANDSAT/LE07/C01/T1')
.filterDate( tahun[i] + "-" + bulan[j] + "-" + tgl1[j], tahun[i] + "-" + bulan[j] + "-" + tgl2[j])
.median();
// Clip to the output image to the geometry boundary.
var clipped = composite.clip(geometry);
Map.addLayer(clipped , {bands: ['B5', 'B4', 'B3']}, "date " + tahun[i] + "-" + bulan[j] + "-" + tgl1[j], false );
}
}
// Display the result.
Map.setCenter(113.517, -8.1631, 9);
Map.addLayer(clipped, {color: 'FFFFFF'}, 'boundary', false);
// Export the FeatureCollection to a SHP file.
Export.table.toDrive({
collection: clipped,
description:'Mount Bromo',
fileFormat: 'SHP'
});
3. Sentinel-2
The script here shows Sentinel-2 image in 2020 with cloud mask.
// This example uses the Sentinel-2 QA band to cloud mask
// the collection. The Sentinel-2 cloud flags are less
// selective, so the collection is also pre-filtered by the
// CLOUDY_PIXEL_PERCENTAGE flag, to use only relatively
// cloud-free granule.
// Function to mask clouds using the Sentinel-2 QA band.
function maskS2clouds(image) {
var qa = image.select('QA60')
// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
qa.bitwiseAnd(cirrusBitMask).eq(0))
// Return the masked and scaled data, without the QA bands.
return image.updateMask(mask).divide(10000)
.select("B.*")
.copyProperties(image, ["system:time_start"])
}
// Map the function over one year of data and take the median.
// Load Sentinel-2 TOA reflectance data.
var collection = ee.ImageCollection('COPERNICUS/S2')
.filterDate('2020-01-01', '2020-7-31')
// Pre-filter to get less cloudy granules.
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
.map(maskS2clouds)
var composite = collection.median()
// Clip to the output image to the geometry boundary.
var clipped = composite.clip(geometry);
Map.setCenter(113.517, -8.1631, 9);
// Display the results.
Map.addLayer(clipped, {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3}, 'RGB 432')
Map.addLayer(clipped, {bands: ['B8', 'B4', 'B3'], min: 0, max: 0.3}, 'Composite 843')
4. Sentinel-1 SAR
// Load the Sentinel-1 ImageCollection.
var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD');
// Filter by metadata properties.
var vh = sentinel1
// Filter to get images with VV and VH dual polarization.
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
// Filter to get images collected in interferometric wide swath mode.
.filter(ee.Filter.eq('instrumentMode', 'IW'));
// Filter to get images from different look angles.
var vhAscending = vh.filter(ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));
var vhDescending = vh.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));
// Create a composite from means at different polarizations and look angles.
var composite = ee.Image.cat([
vhAscending.select('VH').mean(),
ee.ImageCollection(vhAscending.select('VV').merge(vhDescending.select('VV'))).mean(),
vhDescending.select('VH').mean()
]).focal_median();
// clip image
var clipped = composite.clip(geometry);
// Display as a composite of polarization and backscattering characteristics.
Map.setCenter(113.517, -8.1631, 9);
Map.addLayer(clipped, {min: [-25, -20, -25], max: [0, 10, 0]}, 'local');
Map.addLayer(composite, {min: [-25, -20, -25], max: [0, 10, 0]}, 'global', false);
5. SRTM (Shuttle Radar Topography Mission)
SRTM shows Digital Surface Model (DSM) in color grading. High altitude to lo altitude is represented in colors from green, yelloe, orange, and red.
// Display SRTM image.
var image = ee.Image('CGIAR/SRTM90_V4');
// Clip to the output image to the geometry boundary.
var clipped = image.clip(geometry);
// Center the Map.
Map.setCenter(113.517, -8.1631, 9);
// Make a palette: a list of hex strings.
var palette = ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
'74A901', '66A000', '529400', '3E8601', '207401', '056201',
'004C00', '023B01', '012E01', '011D01', '011301'];
// Display the image.
Map.addLayer(clipped, {min: 0, max: 3000, palette:palette}, 'SRTM');
6. MODIS (Moderate Resolution Imaging Spectroradiometer)
// Compute Enhanced Vegetation Index (EVI) over the MODIS MOD09GA product
// using an expression.
// Load a MODIS image and apply the scaling factor.
var img = ee.Image('MODIS/006/MOD09GA/2012_03_09').multiply(0.0001);
// Compute EVI using an expression. The second argument is a map from
// variable name to band name in the input image.
var evi = img.expression(
'2.5 * (nir - red) / (nir + 6 * red - 7.5 * blue + 1)',
{
red: img.select('sur_refl_b01'), // 620-670nm, RED
nir: img.select('sur_refl_b02'), // 841-876nm, NIR
blue: img.select('sur_refl_b03') // 459-479nm, BLUE
});
// Center the map.
Map.setCenter(113.517, -8.1631, 9);
// Display the input image and the EVI computed from it.
Map.addLayer(img.select(['sur_refl_b01', 'sur_refl_b04', 'sur_refl_b03']),
{min: 0, max: 0.2}, 'MODIS bands 1/4/3');
Map.addLayer(evi, {min: 0, max: 1}, 'EVI');
7. NOAA (National Oceanic and Atmospheric Administration)
// Compute the trend of nighttime lights from DMSP.
// Add a band containing image date as years since 1990.
function createTimeBand(img) {
var year = img.date().difference(ee.Date('2020-08-01'), 'year');
return ee.Image(year).float().addBands(img);
}
// Fit a linear trend to the nighttime lights collection.
var collection = ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4')
.select('avg_vis')
.map(createTimeBand);
var fit = collection.reduce(ee.Reducer.linearFit());
// Display a single image
Map.addLayer(ee.Image(collection.select('avg_vis').first()),
{min: 0, max: 63},
'stable lights first asset');
// Display trend in red/blue, brightness in green.
Map.setCenter(107.404, -3.463, 5);
Map.addLayer(fit,
{min: 0, max: [0.18, 20, -0.18], bands: ['scale', 'offset', 'scale']},
'stable lights trend');
Data Science and GIS 