The Javascript code below will calculate Land Surface Temperature (LST) from landsat 8 imagery.
Adopted from this medium post: Analyzing Land Surface Temperature (LST) with Landsat 8 Data in Google Earth Engine - https://medium.com/@ridhomuh002/analyzing-land-surface-temperature-lst-with-landsat-8-data-in-google-earth-engine-f4dd7ca28e70// Load AOI or ROI
var aoi = ee.FeatureCollection("users/forum2k9/Karu"); // Karu polygon - change if you need different
Map.addLayer(aoi, {}, 'AOI - Karu');
Map.centerObject(aoi, 10);
// Define data retrieval Start and End Date
var startDate = '2023-03-01';
var endDate = '2023-10-31';
// Applies scaling factors. Recalculating from K to °C
function applyScaleFactors(image) {
// Scale and offset values for optical bands
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
// Scale and offset values for thermal bands
var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0).subtract(273.15); // I'm used to the °C, In case of K delete the subtract part
// Add scaled bands to the original image
return image.addBands(opticalBands, null, true)
.addBands(thermalBands, null, true);
}
// Function to Mask Clouds and Cloud Shadows in Landsat 8 Imagery
function cloudMask(image) {
// Define cloud shadow and cloud bitmasks
var cloudShadowBitmask = (1 << 4);
var cloudBitmask = (1 << 3);
var cirrus = (1 << 2); // I add also mask for cirrus. Suprisely use of Bit 6 (Clear of any clouds) is not working - anybody know why?
// Select the Quality Assessment (QA) band for pixel quality information
var qa = image.select('QA_PIXEL');
// Create a binary mask to identify clear conditions (both cloud and cloud shadow bits set to 0)
var mask = qa.bitwiseAnd(cloudShadowBitmask).eq(0)
.and(qa.bitwiseAnd(cloudBitmask).eq(0))
.and(qa.bitwiseAnd(cirrus).eq(0));
// Update the original image, masking out cloud and cloud shadow-affected pixels
return image.updateMask(mask);
}
// Import and preprocess Landsat 8 imagery
var image = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
.filterBounds(aoi)
.filterDate(startDate, endDate)
.map(applyScaleFactors)
.map(cloudMask)
.median()
.clip(aoi);
// Define visualization parameters for True Color imagery (bands 4, 3, and 2)
var visualization = {
bands: ['SR_B4', 'SR_B3', 'SR_B2'],
min: 0.0,
max: 0.2,
};
// Add the processed image to the map with the specified visualization
Map.addLayer(image, visualization, 'True Color 432');
print(image);
// Add the LST Layer to the Map with Custom Visualization Parameters
Map.addLayer(image.select('ST_B10'), {
min: 19, // Minimum LST value
max: 43, // Maximum LST value
palette: [
'040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
'0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
'3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
'ff0000', 'de0101', 'c21301', 'a71001', '911003'
]}, 'Land Surface Temperature');
// // Calculate Normalized Difference Vegetation Index (NDVI)
// var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI');
// // Define NDVI Visualization Parameters
// var ndviPalette = {
// min: -1,
// max: 1,
// palette: ['blue', 'white', 'green']
// };
// Map.addLayer(ndvi, ndviPalette, 'NDVI Karu')
// Adopted from: https://github.com/panmoravec/GEE/blob/main/LST%20Landsat8
Export.image.toDrive({
image: image.select('ST_B10'),
description: 'LST_2023',
scale: 30,
region: aoi,
fileFormat: 'GeoTIFF',
maxPixels: 3100100100, // set bigger in case of need
});// Another version for.... Land surface temperature (LST) Monitoring using Landsat imagery using Google Earth Engine
1) https://code.earthengine.google.com/525ebe138c6d2198a0fd464f7cfc967a
var LLST = require('users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js')
var Lan8 = LLST.collection('L8', '2021-1-01', '2021-12-31', geometry, true).select('LST').map(function(image){
return image.subtract(273.15).clip(geometry).set('system:time_start',image.get('system:time_start'))
})
print("chart");
// print(Lan8)
print(
ui.Chart.image.series({
imageCollection: Lan8,
region: geometry,
reducer: ee.Reducer.mean(),
scale: 30,
xProperty: 'system:time_start'
}));
Map.centerObject(geometry,10)
Map.addLayer(geometry)
Map.addLayer(Lan8.mean().clip(geometry), imageVisParam4 ,'Dhaka City LST')
====================================================================================
2) https://code.earthengine.google.com/3fdeff0b82b6fc98022a11facd30d269
// Land surface temperature
var landsat8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterBounds(ROI)
.filterDate('2020-01-01','2022-12-31')
//.filterMetadata('CLOUD_COVER', 'less_than', 1)
.median()
.clip(ROI)
print(landsat8)
Map.addLayer(landsat8, imageVisParam3, 'Satelliteimage with clouds')
Map.centerObject(landsat8,8)
// CLOUD MASK
//Bits 3 and 5 are cloud shadow and cloud, respectively
var maskL8sr = function(col){
var cloudShadowBitMask = (1 << 3)
var cloudsBitMask = (1 << 5)
// Get the piixel QA band
var qa = col.select('pixel_qa')
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
.and(qa.bitwiseAnd(cloudsBitMask).eq(0))
return col.updateMask(mask)
}
// visualization params
var vizParams = {
bands: ['B5', 'B6', 'B4'],
min: 0,
max: 4000,
gamma: [1, 0.9, 1.1]
};
var vizParams2 = {
bands: ['B4', 'B3', 'B2'],
min: 0,
max: 3000,
gamma: 1.4,
};
//load the collection
{
var col = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.map(maskL8sr)
.filterBounds(ROI)
.filterDate('2020-01-01','2022-12-31')
.mean()
.clip(ROI)
}
print(col)
Map.addLayer(col, imageVisParam2, 'Cloud_Free')
// NDVI CALCULATION
var NDVI = col.normalizedDifference(['B5','B4'])
Map.addLayer(NDVI, {min: -1, max: 0.7, palette: ['blue','white','yellow','red']}, 'Created NDVI')
Map.centerObject(NDVI, 8)
print(NDVI)
// selecting Band10 which is the surface temperature
// thermal band 10
var thermalband = col.select('B10').multiply(0.1)
Map.addLayer(thermalband, {min:294.388, max:300.514, palette:['yellow','green','blue']}, 'Thermal band')
//finding minimum and maximum NDVI
var min = ee.Number(NDVI.reduceRegion({ //reduce region is used to calculate a statistcal function for each region
reducer: ee.Reducer.min(),
geometry: ROI,
scale: 30,
maxPixels: 1e9
}).values().get(0));
print(min, 'min');
var max = ee.Number(NDVI.reduceRegion({
reducer: ee.Reducer.max(),
geometry: ROI,
scale: 30,
maxPixels: 1e9
}).values().get(0));
print(max, 'max')
//fractional vegetation
var fv = (NDVI.subtract(min).divide(max.subtract(min))).pow(ee.Number(2))
print(fv,'fv');
Map.addLayer(fv, {},'Fractional Vegetation');
//Emissivity
// you will have to find the emissivity of natural and town
var a = ee.Number(0.004)
var b = ee.Number(0.986)
var EM = fv.multiply(a).add(b).rename('EMM')
Map.addLayer(EM, imageVisParam, 'Emissivity')
//FINDING LAND SURFACE TEMPERATURE
//LST in celsius degree bring -273.15
//NB: In kelvin don't bring -273.15
var LST = thermalband.expression(
'(Tb/(1 + (0.00115*(Tb/1.438))*log(Ep)))-273.15',{
'Tb':thermalband.select('B10'),
'Ep':EM.select('EMM')
}).rename('Land surface Temperature');
var viz = {min: 22.298638087799763, max:29.22115281845691, palette: [
'040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
'0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
'3be285', '3ff38f', '86e26f', '3ae237', 'b5e22e', 'd6e21f',
'fff705', 'ffd611', 'ffb613', 'ff8b13', 'ff6e08', 'ff500d',
'ff0000', 'de0101', 'c21301', 'a71001', '911003'
]}
Map.addLayer(LST, viz, 'LST')
//TIMESSERIES
//print(image_drycollection)
//print(ui.Chart.image.series(LST, ROI, ee.Reducer.mean(), 1000, 'system:time_start'))
// Create the title label.
var title = ui.Label('Shama LandSurface Temperature Map Year of 2022 ');
title.style().set('position', 'top-center');
Map.add(title);
var title = ui.Label('Prepared by Kangah Desmond');
title.style().set('position', 'bottom-center');
Map.add(title);
// set position of panel
var legend = ui.Panel({
style: {
position: 'bottom-left',
padding: '8px 15px'
}
});
// Create the title label.
//var title = ui.Label('Dhaka City LandSurface Temperature Map Year of 2020 ');
//title.style().set('position', 'top-center');
//Map.add(title);
// Create legend title
var legendTitle = ui.Label({
value: 'LandSurface Temperature (degC)',
style: {
fontWeight: 'bold',
fontSize: '18px',
margin: '0 0 4px 0',
padding: '0'
}
});
// Add the title to the panel
legend.add(legendTitle);
// create the legend image
var lon = ee.Image.pixelLonLat().select('latitude');
var gradient = lon.multiply((viz.max-viz.min)/100.0).add(viz.min);
var legendImage = gradient.visualize(viz);
// create text on top of legend
var panel = ui.Panel({
widgets: [
ui.Label(viz['max'])
],
});
legend.add(panel);
// create thumbnail from the image
var thumbnail = ui.Thumbnail({
image: legendImage,
params: {bbox:'0,0,10,100', dimensions:'10x200'},
style: {padding: '1px', position: 'bottom-center'}
});
// add the thumbnail to the legend
legend.add(thumbnail);
// create text on top of legend
var panel = ui.Panel({
widgets: [
ui.Label(viz['min'])
],
});
legend.add(panel);
Map.add(legend);
// Create the title label.
//var title = ui.Label('Prepared By: Mijanur Rahman');
//title.style().set('position', 'bottom-right');
//Map.add(title);Also Mirza Waleed has a great publication on LST and YouTube explanation on same publication that you should checkout.
Thank you for reading.
