Assorted GEE code copied from [email protected] account
This account may deprecate, so important code pieces are here
//var PR_USVI = ee.Geometry.Polygon( // [[[19.5, -68.5], // [19.5, -62.0], // [16.5, -62.0,], // [16.5, -68.5]]], null, false);
var PR_roi = ee.Geometry.Rectangle(-65.5,17.8,-67.3,18.5);
// Function to clip an image collection //function clipCollection(image){ // return image.clip(PR_roi); //}
// Load images for May/June/July 2020 - IFC var collection = ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') //Product ID Here .select('NO2_column_number_density') // Band Name Here .filterDate('2021-01-01', '2021-04-01') .filterBounds(PR_roi); // .map(clipCollection); // .median();
var list = collection.toList(100, 0) ; print(list)
// Select image from list using index var image= ee.Image(list.get(32));
var VizParams = {min:0.00001, max:0.0001,'palette':['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']}; Map.addLayer(image, VizParams); Map.setCenter(-66.12,18.45, 8);
// Create a dictionary of labels and visualizations. //var vis = { // 'December 2019': {bands: 'Dec', min: 0, max: 0.0004, palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']}, // 'January 2020': {bands: 'Jan', min: 0, max: 0.0004, palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']}, // 'February 2020': {bands: 'Feb', min: 0, max: 0.0004, palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']} //};
// Set up the maps. //var maps = []; //for (var label in vis) { // Create and set up each map. // var map = ui.Map(); // map.addLayer(img, vis[label]); // map.setControlVisibility(false); // map.add(ui.Label(label)); // Add the maps to the list. // maps.push(map); //}
// Link the maps. //var linker = ui.Map.Linker(maps);
// Because they are linked, they all respond to this event. //maps[0].setCenter(114.346, 30.553, 8);
// Set everything in the root panel to the list of widgets. //ui.root.widgets().reset(maps);
//---------------------------------END-----------------------------------------------//
// Atlantic ROI var TB_roi = ee.Geometry.Rectangle(-83.5,27.0,-81.5,28.5);
// Checking if geometry is ok //Map.addLayer(TB_roi,{},'TB_roi') // it is ok.
// Load images for May/June/July 2020 - IFC var collection = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') //Product ID Here .select('tropospheric_NO2_column_number_density') // Band Name Here .filterDate('2022-12-01', '2023-02-28') // .filterBounds(TB_roi); // .median();
// Function to clip an image collection //function clipCollection(collection){ //return collection.clip(TB_roi); // }
//var coll_clip = clipCollection(TB_roi);
// Add AOD collection //var collection_aerindex = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') //Product ID Here // .select('absorbing_aerosol_index') // Band Name Here // .filterDate('2020-06-01', '2020-06-30') // .filterBounds(TB_roi); // .median();
//var list = collection.toList(1000, 0) ; var list = collection.aggregate_array('system:index') // I like to use this way to see image's ID //var list_aod = collection_aerindex.aggregate_array('system:index') // I like to use this way to see image's ID print(list) //print(list_aod)
// Select image from list using index //var image= ee.Image(list.get(250)); // Filter a specific image in the collection: //var image = collection.filter(ee.Filter.eq('system:index',list.get(25))).first(); //print('Filtered Image:',image)
// Set vizualization parameters for TIRS Band 10 // var VizParams = {min:25000, max:31000, 'bands':'B10','palette':'000000, FF0000'}; var VizParams = {min:0, max:0.00007,'palette':['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']}; //var VizParams_aerindex = {min:-15, max:10,'palette':['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']};
// 'February 2020': {bands: 'Feb', min: 0, max: 0.0004, palette: ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']} Map.setCenter(-82.5,28, 9);
var img_disp = collection.mean(); var image_disp_clip = img_disp.clip(TB_roi);
// Clip raw image to SJB Region and add to map // var image_clip = image.clip(ATL_roi); //Map.addLayer(image, VizParams, 'Image'); // Not all the images have data.... //Map.addLayer(collection.mean(), VizParams, 'NO2 Mean'); //Better to use weekly or monthly means? //Map.addLayer(collection_aerindex.mean(), VizParams_aerindex, 'aerindex Mean'); //Better to use weekly or monthly means? Map.addLayer(image_disp_clip, VizParams, 'NO2 Mean');
//---------------------------------END-----------------------------------------------//
// SJB_LST_calc // Cloud-free daily images from Landsat-8 collection // Filter on BQA band and clip to San Juan Bay, PR // Compute surface radiance and surf temperature // Requires atm_corr coefficients // Outputs image as GEOTIFF
// Define boundary (SJB) var polygon = ee.Geometry.Polygon( [[[-66.25, 18.30], [-65.90, 18.30], [-65.90, 18.50], [-66.25, 18.50]]]);
// Convert bounds to JSON strings var regionJSON = JSON.stringify(polygon.getInfo());
// Cloud Index Values // 61440 = Cloudy (high probability), Cirrus* // 59242 = Cloudy (high probability), maybe Cirrus // 57344 = Cloudy (high probability), maybe Cirrus // 56320 = Cloudy (high probability), Non-Cirrus // 53248 = Cloudy (high probability) Non-Cirrus* // 39936 = Cloudy (low probability) Non-Cirrus // 36896 = Cloudy (low probability) Non-Cirrus // 36864 = Cloudy (low probability) Non-Cirrus // 28672 = Cloudy (low probability) Cirrus only*
// Set collection var startDate = new Date('07/01/2017'); var endDate = new Date('12/01/2017'); //var collection = ee.ImageCollection('LC8_L1T') var collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT')
.filterDate(startDate, endDate) // Date filter
// .filterMetadata('CLOUD_COVER',"not_greater_than",20) // Cloud cover filter // Path and row filters .filterMetadata('WRS_PATH',"equals",5) //.filterMetadata('WRS_ROW',"not_less_than",56) .filterMetadata('WRS_ROW',"equals",47)
// Define list of images from collection here var list = collection.toList(100, 0) ; print(list)
// Select image from list using index var image= ee.Image(list.get(5)); //print(image)
// TEST OF TOA alg. and CLOUD SCORE // Convert the raw data to top-of-atmosphere reflectance. var toa = ee.Algorithms.Landsat.TOA(raw);
// Load a cloudy Landsat scene and display it. var cloudy_scene = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140926'); Map.centerObject(cloudy_scene); Map.addLayer(cloudy_scene, {bands: ['B4', 'B3', 'B2'], max: 0.4}, 'TOA', false);
// Add a cloud score band. It is automatically called 'cloud'. var scored = ee.Algorithms.Landsat.simpleCloudScore(cloudy_scene);
// Create a mask from the cloud score and combine it with the image mask. var mask = scored.select(['cloud']).lte(20);
// Apply the mask to the image and display the result. var masked = cloudy_scene.updateMask(mask); Map.addLayer(masked, {bands: ['B4', 'B3', 'B2'], max: 0.4}, 'masked');
// Set cloud index values using BQA Band (Noel at Google) var quality = image.select( 'BQA' ); var cloud01 = quality.eq(61440); var cloud02 = quality.eq(59242); var cloud03 = quality.eq(57344); var cloud04 = quality.eq(56320); var cloud05 = quality.eq(53248); var cloud06 = quality.eq(39936); var cloud07 = quality.eq(36896); var cloud08 = quality.eq(36864); var cloud09 = quality.eq(28672); // Are all needed? How to avoid overmasking?
// Set vizualization parameters for TIRS Band 10 var VizParams = {min:25000, max:31000, 'bands':'B10','palette':'000000, FF0000'}; Map.setCenter(-66.12,18.4, 11);
// Clip raw image to SJB Region and add to map var image_raw = image.clip(polygon); //Map.addLayer(image_raw, VizParams, "Base Scene",false);
// Create cloud mask based on BQA Band var maskedimage = image.mask().and(cloud01.or(cloud02).or(cloud03).or(cloud04).or(cloud05).or(cloud06).or(cloud07).or(cloud08).or(cloud09).not()); //var maskedimage = image.mask().and(cloud01.or(cloud01).or(cloud09).not()); var image_masked_tmp = image.mask(maskedimage);
// Clip masked image to SJB Region and add to map var image_masked = image_masked_tmp.clip(polygon); Map.addLayer(image_masked, VizParams, "With Cloud Mask",true);
//print(image_raw.getInfo());
// Perform calculations on image2 (unmasked) // Extract coefficients from imput image //print(image_raw.getInfo()); var rad_mult = (image_raw.get('RADIANCE_MULT_BAND_10')); print(rad_mult) var rad_add = (image_raw.get('RADIANCE_ADD_BAND_10')); print(rad_add) var k1 = (image_raw.get('K1_CONSTANT_BAND_10')); print(k1) var k2 = (image_raw.get('K2_CONSTANT_BAND_10')); print(k2) var dt = (image_raw.get('DATE_ACQUIRED')); print(dt) var st = (image_raw.get('SCENE_CENTER_TIME')); print(st)
// Calculate Radiance at TOA // Coeffs. come from within image_raw (properties) var B10_rad_toa = image_raw.expression("((rad_mult * B10_dn_toa) + rad_add)", {B10_dn_toa: image_masked.select("B10"), rad_mult: 0.0003342, rad_add: 0.1 }); //Map.addLayer(B10_rad_toa);
// Calculate Radiance at surface (atm_corr) // Coeffs. come from URL:http://atmcorr.gsfc.nasa.gov/ var B10_rad_surf = image_raw.expression("(((B10_rad_toa - Lu)/(e*t))-((1-e)/e)*Ld)", {B10_rad_toa: B10_rad_toa, Lu: 4.12, // Upwelling radiance Ld: 6.08, // Downwelling radiance t: 0.50, // Transparency e: 0.95 // Emissivity }); //Map.addLayer(B10_rad_surf);
// Calculate Temp (in Deg C) at surface // Coeffs. come from within image_raw (properties) var LST_degC = image_raw.expression("(k2/log(k1/B10_rad_surf+1))-273.15", {B10_rad_surf: B10_rad_surf, k1: 774.89, k2: 1321.08 });
var LST_degC = LST_degC.float(); //print(LST_degC)
var LSTParams = {min:25, max:48,'palette':'000000, FF0000'};
// Create mask based on where LST < 25 DegC (cloud shadow) var cloud_thresh = LST_degC.lt(9.0);
// Define images for output and display
//
var LST_masked_tmp = LST_degC.mask().and(cloud_thresh.not());
var LST_masked = LST_degC.mask(LST_masked_tmp);
Map.addLayer(LST_masked,LSTParams,"LST");
// Add clipped RGB image to map (unmasked) Map.addLayer(image_raw, {'bands':['B4', 'B3', 'B2'], 'min': 5000, 'max': 18000},"RGB Comp.");
var RGBExport = image_raw.visualize(['B4', 'B3', 'B2']).toUint8();
// Export raw RGB image for comparison w/heat map Export.image(RGBExport, 'RGB', { region: regionJSON, crs: 'EPSG:4326', scale: 30 });
// Thumbnail preview //print("Download preview:", LST_degC.getThumbURL({ // region: regionJSON, // min:25, max:48, // palette:'000000, FF0000' //}))
// Export LST - GEOTIFF Format //Export.image(LST_masked, 'LST', { // region: regionJSON, // crs: 'EPSG:4326', // scale: 30 //});
//---------------------------------END-----------------------------------------------//
// SJB_mask_calc // Cloud-free composite (mosaic) for Landsat-8 collection // Or, single image w/cloud mask // Filter on BQA band and clip to San Juan Bay, PR // Compute surface radiance and surf temperature // Requires atm_corr coefficients // Outputs image (check format)
// Define boundary (SJB) var polygon = ee.Geometry.Polygon( [[[-66.25, 18.30], [-65.90, 18.30], [-65.90, 18.50], [-66.25, 18.50]]]);
// Convert bounds to JSON strings var regionJSON = JSON.stringify(polygon.getInfo());
// Cloud Index Values // 61440 = Cloudy (high probability), Cirrus* // 59242 = Cloudy (high probability), maybe Cirrus // 57344 = Cloudy (high probability), maybe Cirrus // 56320 = Cloudy (high probability), Non-Cirrus // 53248 = Cloudy (high probability) Non-Cirrus* // 39936 = Cloudy (low probability) Non-Cirrus // 36896 = Cloudy (low probability) Non-Cirrus // 36864 = Cloudy (low probability) Non-Cirrus // 28672 = Cloudy (low probability) Cirrus only*
// Set collection var startDate = new Date('9/01/2013'); var endDate = new Date('9/30/2013'); var collection = ee.ImageCollection('LC8_L1T') .filterDate(startDate, endDate).filterBounds(polygon)
// Select image from collection here var list = ee.List(collection.getInfo().features); //cast it expicitly as a list print(list)
// Select image from list using index var image2= ee.Image(list.get(22)); //print(image2)
// Choose individual image here // How to substitute using image2 here? var image = ee.Image('LC8_L1T/LC80050472013265LGN00')
// Set cloud index values using BQA Band (Noel at Google) var quality = image.select( 'BQA' ); var cloud01 = quality.eq(61440); var cloud02 = quality.eq(59242); var cloud03 = quality.eq(57344); var cloud04 = quality.eq(56320); var cloud05 = quality.eq(53248); var cloud06 = quality.eq(39936); var cloud07 = quality.eq(36896); var cloud08 = quality.eq(36864); var cloud09 = quality.eq(28672); // Are all needed? How to avoid overmasking?
// Set vizualization parameters for TIRS Band 10 var VizParams = {min:28000, max:32000, 'bands':'B10','palette':'000000, FF0000'}; Map.setCenter(-66.12,18.4, 11);
// Clip raw image to SJB Region and add to map var image_raw = image.clip(polygon); //Map.addLayer(image_raw, VizParams, "Base Scene",false);
// Create cloud mask based on BQA Band //var maskedimage = image.mask().and(cloud01.or(cloud02).or(cloud03).or(cloud04).or(cloud05).or(cloud06).or(cloud07).or(cloud08).or(cloud09).not()); var maskedimage = image.mask().and(cloud01.or(cloud01).or(cloud09).not()); var image_masked_tmp = image.mask(maskedimage);
// Clip masked image to SJB Region and add to map var image_masked = image_masked_tmp.clip(polygon); //Map.addLayer(image_masked, VizParams, "With Cloud Mask",true);
//print(image_raw.getInfo());
// Perform calculations on image2 (unmasked) // Extract coefficients from imput image //print(image_raw.getInfo()); var rad_mult = (image_raw.get('RADIANCE_MULT_BAND_10')); print(rad_mult) var rad_add = (image_raw.get('RADIANCE_ADD_BAND_10')); print(rad_add) var k1 = (image_raw.get('K1_CONSTANT_BAND_10')); print(k1) var k2 = (image_raw.get('K2_CONSTANT_BAND_10')); print(k2) var dt = (image_raw.get('DATE_ACQUIRED')); print(dt) var st = (image_raw.get('SCENE_CENTER_TIME')); print(st)
// Calculate Radiance at TOA // Coeffs. come from within image_raw (properties) var B10_rad_toa = image_raw.expression("((rad_mult * B10_dn_toa) + rad_add)", {B10_dn_toa: image_masked.select("B10"), rad_mult: 0.0003342, rad_add: 0.1 }); //Map.addLayer(B10_rad_toa);
// Calculate Radiance at surface (atm_corr) // Coeffs. come from URL:http://atmcorr.gsfc.nasa.gov/ var B10_rad_surf = image_raw.expression("(((B10_rad_toa - Lu)/(e*t))-((1-e)/e)*Ld)", {B10_rad_toa: B10_rad_toa, Lu: 3.76, // Upwelling radiance Ld: 5.68, // Downwelling radiance t: 0.57, // Transparency e: 0.95 // Emissivity }); //Map.addLayer(B10_rad_surf);
// Calculate Temp (in Deg C) at surface // Coeffs. come from within image_raw (properties) var LST_degC = image_raw.expression("(k2/log(k1/B10_rad_surf+1))-273.15", {B10_rad_surf: B10_rad_surf, k1: 774.89, k2: 1321.08 });
var LST_degC = LST_degC.float(); //print(LST_degC)
var LSTParams = {min:25, max:48,'palette':'000000, FF0000'};
// Create mask based on where LST < 25 Degc var cloud_thresh = LST_degC.lt(25.0);
// Define images for output and display
//
var LST_masked_tmp = LST_degC.mask().and(cloud_thresh.not());
var LST_masked = LST_degC.mask(LST_masked_tmp);
Map.addLayer(LST_masked,LSTParams,"LST");
// Add clipped RGB image to map (unmasked) Map.addLayer(image_raw, {'bands':['B4', 'B3', 'B2'], 'min': 5000, 'max': 18000},"RGB Comp.");
var RGBExport = image_raw.visualize(['B4', 'B3', 'B2']).toUint8();
// Export raw RGB image for comparison w/heat map Export.image(RGBExport, 'RGB', { region: regionJSON, crs: 'EPSG:4326', scale: 30 });
// Thumbnail preview //print("Download preview:", LST_degC.getThumbURL({ // region: regionJSON, // min:25, max:48, // palette:'000000, FF0000' //}))
// Export LST - GEOTIFF Format Export.image(LST_masked, 'LST', { region: regionJSON, crs: 'EPSG:4326', scale: 30 });
//---------------------------------END-----------------------------------------------//
var collection = sentinel2//.filter(ee.Filter.eq('MGRS_TILE','T17RNG')) .filterBounds(lake) //filter by geometry .filterDate('2020-01-01','2020-02-28') //filter by date .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',30)) //filter by cloud coverage
print(collection)
print(collection.aggregate_array('system:index'))
/// Load Cloud Mask function for Sentinel-2 Images var CloudMaskFunction = require('users/lizcanosandoval/public/:/CloudScore_S2'); var CloudMask = function(image){ var img = ee.Image(image); var mask = CloudMaskFunction.CloudScoreSR(img); return mask; };
// function for retrieving suspended particles // http://www.mdpi.com/2072-4292/9/7/761 var suspended_sediment = function(img){ var scale = img.select('B7').divide(10000) var power = scale.pow(1.357) var mult = power.multiply(2950) return ee.Image(mult) }
// APPLY CLOUD MASK FUNCTION var mask = collection.map(CloudMask)
// Apply sediment particle function var sp = collection.map(suspended_sediment)
var vis = {bands:['B4','B3','B2'],min:0,max:2500} var vis2 = {min:5,max:25, palette: [ '030d81', '0519ff', '05e8ff', '11ff01', 'fbff01', 'ff9901', 'ff0000', 'ad0000' ]}
Map.addLayer(collection.median(),vis,'image') Map.addLayer(mask.median(),vis,'image_masked') Map.addLayer(sp.median(),vis2,'suspended matter')
//---------------------------------END-----------------------------------------------//
var point = ee.Geometry.Point([-80.28, 25.43]);
var collection_S2 = ee.ImageCollection('COPERNICUS/S2_SR') // var collection_S3 = ee.ImageCollection('COPERNICUS/S3/OLCI') // var collectionLS8 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') // var collectionLS9 = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')
var collection = collection_S2 .filterBounds(point) //filter by geometry .filterDate('2021-01-01','2021-06-30') //filter by date // .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',50)) //filter by cloud coverage
// Tasks: // 1. Cloud screening // 2. Algorithm implementation // 3. Image generation
var list = collection.toList(100, 0) ; //var list = collection.aggregate_array('system:index') // I like to use this way to see image's ID
var list_ind=16;
// Select image from list using index var image= ee.Image(list.get(list_ind)); print(image) //print(list)
//print(collection)
/// Load Cloud Mask function for Sentinel-2 Images var CloudMaskFunction = require('users/lizcanosandoval/public/:/CloudScore_S2'); var CloudMask = function(image){ var img = ee.Image(image); var mask = CloudMaskFunction.CloudScoreSR(img); return mask; };
// APPLY CLOUD MASK FUNCTION var mask = collection.map(CloudMask) Map.setCenter(-80.28, 25.5, 10); var vis = {bands:['B4','B3','B2'],min:0,max:2500} //print(mask)
var mask_list = mask.toList(100, 0) ; print(mask_list) var mask_image = ee.Image(mask_list.get(list_ind));
print(mask_image)
// Define single image from collection // var disp_img = collection.first() // print(disp_img)
// Map // Map.addLayer(disp_img,vis,'image') // Map.addLayer(mask.first(),vis,'image_masked')
Map.addLayer(image,vis,'image') Map.addLayer(mask_image,vis,'masked image')
//---------------------------------END-----------------------------------------------//