I am attempting to generate some zonal statistics for a feature layer, approximately following the steps outlined in this tutorial. The ImageCollection I am using comes from the Awesome GEE Catalog and apparently does not contain any system:start_time properties for any items in the collection. When I try to call the zonal statistics function from the tutorial linked above, I get an error for Element (Error) Collection.first: Error in map(ID=Copernicus_DSM_COG_10_N00_00_E006_00_DEM): Image.date: Image 'Copernicus_DSM_COG_10_N00_00_E006_00_DEM' does not have a 'system:time_start' property. I've tried to manually set a system:time_start property here: //Bare Earth Elevation var fabdem = ee.ImageCollection("projects/sat-io/open-datasets/FABDEM") .filterBounds(fextent); print(fabdem.first()) var test = fabdem.mosaic(); var test2 = ee.ImageCollection(test) test2.set("system:time_start",'2023-02-13T19:33:07+00:00') print(test2) To simplify things, I took the original ImageCollection (which is not time sensitive) and flattened it to a mosaic, then recreated an ImageCollection as the zonalStats() function requires one. the output still does not contain a system:time_start property. Any suggestions?

Error: ERROR---Error: Image.clipToBoundsAndScale, argument 'input': Invalid type. Expected type: Image. Actual type: ImageCollection. (Error code: 3) THE SCRIPT: Map.addLayer(roi) Map.centerObject(roi, 10) var img = ee.ImageCollection("COPERNICUS/S2_SR") .filterDate('2021-10-01','2021-10-25') .filterBounds(roi); var calcLSWI = function(image) { var lswi = image.expression( '(NIR - SWIR) / (NIR+SWIR)', { 'NIR': image.select('B8').multiply(0.0001), 'SWIR': image.select('B11').multiply(0.0001), }).rename('LSWI'); return image.addBands(lswi); } var LSWI = img.map(calcLSWI).select('LSWI') print(LSWI) Map.addLayer(LSWI) var Chart = ui.Chart.image.seriesByRegion({ imageCollection: LSWI, regions: roi, reducer: ee.Reducer.mean(), //type of reduction. See ee.Reducers for other kinds of reductions seriesProperty: 'NAME' //property of roi to display in map }) .setOptions({ title: 'LSWI', vAxis: {title: 'LSWI', maxValue: 1, minValue: -1}, hAxis: {title: 'date'}, }) print(Chart) Export.image.toDrive({ image: LSWI, description: 'LSWI_VAISHALI', folder: 'classification', scale: 30, fileFormat: 'GeoTIFF', }); Link to code

I am trying to apply a mask to a imagecollection, I want to a apply this mask image a image, because if I apply the mask to the whole collection, I cant extract values by points. I am using a mask of fitoprincipe, https://github.com/fitoprincipe/geetools-code-editor/wiki/Cloud-Masks But when I apply this I can't get it to mask anything. Outline_Trigo is a perimeter shape Outline.shp points is a points shape points_all.shp How could I do the loop, I can't get it to work with either for or while, and I only find information about doing it with .map or .iterate. // Creamos la función que nos calcula el indice del NDVI function NDVI(entrada){ var ndvi = entrada.addBands(entrada.normalizedDifference(['B8','B4']).rename('NDVI')) return (ndvi) } var cloud_masks = require('users/fitoprincipe/geetools:cloud_masks'); var sentinel2function = cloud_masks.sentinel2(); // Creamos la coleccion inicial, filtramos por fechas y lugar, añadimos la banda del NDVI var coleccion_NDVI = ee.ImageCollection('COPERNICUS/S2_SR') .filterBounds(Outline_Trigo) .filterDate('2018-03-01','2018-03-15'); Map.centerObject(Outline_Trigo,15); Map.addLayer(Outline_Trigo,{color: '006600', strokeWidth: 8},"Outline_Trigo"); print(coleccion_NDVI, 'Colección NDVI'); var lista=coleccion_NDVI.toList(coleccion_NDVI.size()) var i=0; var imagen; var longitud= lista.size().getInfo() //var fecha; for(var i = 0; i

In the docs there are two versions of the errorMatrix function: ee.FeatureCollection.errorMatrix and ee.ImageCollection.errorMatrix I've been sampling points from my classified images to create error matrices using the former, which is working just fine, but I'm curious about when/how the ImageCollection version is used. I assumed ImageCollection.errorMatrix would implicitly convert pixels to points, then check if my classification band matched a reference band. It's not clear if the Image Collection should be a collection of images with two bands or two images with one band each (reference and classification), or something else. Any combination I try just gives me a 1*1 EM with a value of 0. The two arrangements that I thought might work: var classifiedImage = ee.Image(someClassifiedImage) //Some classified image with two bands: "reference" and "classification" ///Create image collection. Using one image now, but could probably do multiple var classifiedImageCollection = ee.ImageCollection.fromImages([classifiedImage]); var imageEM = classifiedImageCollection.errorMatrix("reference", "classification") Or var referenceImage= ee.Image(someReferenceImage) var classifiedImage = ee.Image(someClassifiedImage) var classifiedImageCollection= ee.ImageCollection.fromImages([referenceImage, classifiedImage]); var imageEM = classifiedImageCollection.errorMatrix("tailings", "classification") Both of these produce an error matrix of [[0]] To make it a little more confusing, the args for ImageCollection.errorMatrix are identical to those of ee.FeatureCollection.errorMatrix, including: this:collection (FeatureCollection): The input collection Again, I can just sample the image and get an EM from that, but I'm curious if there's a way to make this work.

I have this code, it generate N ImageCollection and for each image a chart from NDVI in function a the months This is the place of study: var areaEst = ee.FeatureCollection("users/aleszczuk/Trabajo_Final_Especializacion/TF_Mascara_Suelos") This is the collection: var NDVICollection=ee.ImageCollection('MODIS/MOD13A1') .filterDate('2000-01-01','2018-12-31'); a list var list=NDVICollection.toList(20); The loop a generator of charts: for (var i=2000;i

I am trying to extract pixel values by points and convert to a table in Google Earth Engine. I'd like to extract pixel values by lat,lon points I upload from a csv for each S2 image with a matching date. Before using the entire csv (3000+ points), I created two random points and am following the code in this post to extract points. However, the table it output looks like this. I'm not sure why only MSK_CLDPRB, MSK_SNWPRB, QA10, QA20, and QA60 are showing. I'd like all bands to be present. Can anyone steer me in the right direction? // random points to create a FeatureCollection var p1 = ee.Geometry.Point([-76.353, 39.094]) var p2 = ee.Geometry.Point([-76.133, 38.03]) var pts = ee.FeatureCollection(ee.List([ee.Feature(p1),ee.Feature(p2)])) print(pts) // Cloud Mask function function maskS2clouds(image) { var qa = image.select('QA60'); // Bits 10 and 11 are clouds and cirrus, respectively. var cloudBitMask = 1

Due to the necessity that I have to process the model in specific CRS (EPSG:3006), I try to reproject the ImageCollection after calculating all the vegetation indices I need. I use the code below to reproject that whole imagecollection so that I can continue the further analysis. var res2L2017c_Veg = s2L2017c_Veg.reduceRegion({ reducer: ee.Reducer.median(), geometry: ROI, scale: 10, // meters crs: 'EPSG:3006', //sweref99 tm }); print(res2L2017c_Veg) However, it shows the error: Dictionary (Error) Image.reduceRegion: Too many pixels in the region. Found 41709676124, but maxPixels allows only 10000000. Ensure that you are not aggregating at a higher resolution than you intended; that is a frequent cause of this error. If not, then you may set the 'maxPixels' argument to a limit suitable for your computation; set 'bestEffort' to true to aggregate at whatever scale results in 'maxPixels' total pixels; or both. Does it mean that it exceeds the computation affordability of GEE? How should I correct this error? This is the link to my code: https://code.earthengine.google.com/6a40e656de80a79aa4308c97f313b756 This is the geometry "ROI": https://code.earthengine.google.com/?asset=projects/ee-sweden2023study/assets/ROI

I want to merge two ImageCollection on GEE simply with merge() function . According to the document on GEE website (https://developers.google.com/earth-engine/apidocs/ee-featurecollection-merge), I only need to use the merge() function. However, my result returned Line 287: res2L2017c1_6_Veg.merge is not a function This is my GEE code. https://code.earthengine.google.com/c0bc5edacd177b0acaf0330670f759c7 I have no clue why it is not working, is it due to the reduceRegion part on line 138 and line 235 (for sentinel2 and sentinel1 individually)? Because the output of the reduceRegion is the input to merge(). Update: I have also tried to merge the ImageCollection that was produced before doing the reduceRegion, and it still filed to merge. var merge_s1s2 = s2L2017c1_6_Veg.merge(s1L20171_6_predb); print(merge_s1s2) //this is merging two imagecollection from line 121 and line 228 var merge_res1s2 = res2L2017c1_6_Veg.merge(res1L20171_6_predb); print(merge_res1s2) //this is merging two imagecollection from line 138 and line 235

I want to resample the mean of 48 images of GPM with 10000m to MODIS in GEE. My code works perfectly for one image but the result for the mean or median of 48 images (or any number) does not correct. The code: var GPM = ee.ImageCollection('NASA/GPM_L3/IMERG_V06').filterDate(startdate, enddate).filterBounds(roi).select('probabilityLiquidPrecipitation').median().clip(roi); var modis = ee.ImageCollection('MODIS/006/MOD10A1').filterDate(startdate, enddate).filterBounds(roi).first().select('NDSI').clip(roi); var modisProjection = ee.Image(modis).projection(); var crs=modisProjection.getInfo()['crs']; // 10,000m GPM resampled to modis 500m var GPM_500m=GPM.resample('bicubic').reproject({'crs':modisProjection,'scale':500}); Map.addLayer(GPM_500m.clip(roi).select('probabilityLiquidPrecipitation'),{min:50, max:95,palette:palette }, 'Resampeled GPM',true); How can I do this?

What are the min and max dates of any image in an image collection? As an example, I'm using 'GOOGLE/DYNAMICWORLD/V1' though I'd need this to work with any image collection. I've tried looking at the properties: var dataset_id = 'GOOGLE/DYNAMICWORLD/V1'; console.log(ee.data.getAsset(dataset_id)); // 'properties' is empty I've also tried asking the API itself what the min/max are, but it takes so long the browser window times out and I never get an answer: var collection = ee.ImageCollection(dataset_id); var start_millis = collection.aggregate_min('system:time_start').getInfo() var end_millis = collection.aggregate_max('system:time_start').getInfo() // takes so long to run the browser times out. I also can't statically set the min/max date because these change dynamically for many datasets that are updated daily, like the Google Dynamic World example.

I have climate indices values as lists and I tried to make the lists into an imageCollection. However, when I tried to use map function, var climate_indices = ee.ImageCollection.fromImages( years.map(function (y) { return months.map(function(m) { var monthID = m; var yearID = y; var dmiImage = ee.Image(dmi.get([yearID-2000,monthID-1])).rename('dmi').float(); var meiImage = ee.Image(mei.get([yearID-2000,monthID-1])).rename('mei').float(); var pdoImage = ee.Image(pdo.get([yearID-2000,monthID-1])).rename('pdo').float(); var w = dmiImage.addBands(meiImage).addBands(pdoImage); return w.set('year', y) .set('month', m) .set('system:time_start', ee.Date.fromYMD(y, m, 1)); }); }).flatten() ); print(climate_indices) The system report: "Array.get: Element 0 of list 'position' must not be null." It seems that the m or y here is seen as Null. Anyone could help with that? https://code.earthengine.google.com/41c17638e32ab0d2c922f46ffb215ef6

I am working with rainfall monthly totals for a 30-year period using CHIRPS pentad. //1.Reference Period var historical = ee.List.sequence(1991, 2020) var months = ee.List.sequence(1, 5) //print(historical) //2.Map over the years and create a monthly totals collection var monthlyImages = historical.map(function(year){ return months.map(function(month){ var filtered = chirps .filter(ee.Filter.calendarRange(year, year, 'year')) .filter(ee.Filter.calendarRange(month, month, 'month')) var monthly = filtered.sum(); return monthly.set({'month': month, 'year': year}) }) }).flatten() print(monthlyImages) //3. Create an Image Collection for each month of each year var monthlyCol = ee.ImageCollection.fromImages(monthlyImages) print(monthlyCol) //4. Convert to Feature Collection var montlyRainfall = ee.FeatureCollection(monthlyImages) // Export.table.toDrive({ // collection: monthlyRainfall, // fileNamePrefix: 'rainfallbymonth', // fileFormat: 'CSV'}) I have been able to get an Image Collection with 150 images for the months I require. I am looking to export each monthly totals(sum) to a table and obtain results from my geometry. Transforming my Image Collection to a Feature Collection and exporting to CSV has worked with the correct dates but it just doesn't contain the values from my 'precipitation' band which is the sum that I filtered in the first code of block. I understand I need to transform my image collection to a feature collection and tell it to assign the dictionary values to my table I just have not figured out how to do it. I am also not sure if I need to Reduce.sum again even though I have filtered my monthly images totals using .sum already.

I'm trying to export an Landsat image from GEE to a Drive, but the Export function that I applied gets 14 images (13 contain NoData and 1 the image). This is my code: function applyScaleFactors(image) { var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2); return image.addBands(opticalBands, null, true); } function maskClouds(image) { var cloudFree = bitwiseExtract(image.select('QA_PIXEL'), 0, 5).eq(0) return image .updateMask(cloudFree) } function bitwiseExtract(value, fromBit, toBit) { if (toBit === undefined) toBit = fromBit var maskSize = ee.Number(1).add(toBit).subtract(fromBit) var mask = ee.Number(1).leftShift(maskSize).subtract(1) return value.rightShift(fromBit).bitwiseAnd(mask) } var Addband_NDVI=function (image){ var NDVI=image.normalizedDifference(['SR_B4','SR_B3']).rename('NDVI') return image.addBands(NDVI);} var collection = ee.ImageCollection("LANDSAT/LE07/C02/T1_L2") .filterDate('2000-01-01', '2001-01-01') // End date is exclusive .filter(ee.Filter.metadata('WRS_PATH', "greater_than", 171)) .filter(ee.Filter.metadata('WRS_PATH', "less_than", 206)) .filter(ee.Filter.metadata('WRS_ROW', "greater_than", 27)) .filter(ee.Filter.metadata('WRS_ROW', "less_than", 37)) .map(applyScaleFactors) .map(Addband_NDVI) .select('NDVI') var pathRowCollection = ee.ImageCollection(ee.Join.saveAll('images') .apply({ primary: collection .distinct(['WRS_PATH', 'WRS_ROW']), secondary: collection, condition: ee.Filter.and( ee.Filter.equals({leftField: 'WRS_PATH', rightField: 'WRS_PATH'}), ee.Filter.equals({leftField: 'WRS_ROW', rightField: 'WRS_ROW'}) ) }) .map(function (image) { return ee.ImageCollection(ee.List(image.get('images'))) .median() // Include row/path for the composite .copyProperties(image, ['WRS_PATH', 'WRS_PATH']) }) ) var ten= pathRowCollection.limit(1); print(ten) Map.addLayer(ten) Export.image.toDrive({ image: ten, scale:30, description: 'prova2M', region: geometry, maxPixels:3e12, crs: 'EPSG:4326' }); My goal is export a single Landsat Image (as displayed on the map layer). Is there an other function that allows get this?

I'm trying to create an array from an EE image, following the accepted answer in this post. However, instead of grabbing a specific image, I start with an EE imageCollection, then reduce it to an image (taking the median across cloud free pixels). When grabbing a specific image, I get the expected shape of the np array; however, I'm not able to get the expected shape when starting with an imageCollection. (This is a similar issue as described in this post, although I'm not sure of the solution in this context. In addition, this post gets close to what I'm looking for -- but returns a 1d array, not a 2d array). #### Set up import ee import numpy as np import geetools from geetools import ui, cloud_mask ee.Authenticate() ee.Initialize() #### Create AOI aoi = ee.Geometry.Polygon( [[[-110.8, 44.7], [-110.8, 44.6], [-110.6, 44.6], [-110.6, 44.7]]], None, False) #### Create np array, starting from image # This works! img = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810') band_arrs = img.sampleRectangle(region=aoi) band_arr_b1 = band_arrs.get('B1') np_arr_b1 = np.array(band_arr_b1.getInfo()) np_arr_b1.shape # Returns --> (373, 531) #### Create np array, starting from imageCollection # This doesn't seem to work mask_l8SR_all = cloud_mask.landsatSR() img = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')\ .filterDate('2017-01-01', '2020-12-31')\ .map(mask_l8SR_all)\ .median()\ .multiply(0.0001) band_arrs = img.sampleRectangle(region=aoi) band_arr_b1 = band_arrs.get('B1') np_arr_b1 = np.array(band_arr_b1.getInfo()) np_arr_b1.shape # Returns --> (1, 1) Google Colab link to the above code

I was trying to retrieving chlorophyll-a levels using Landsat9. After processed it using this function : //Clorophyll-a Levels // Coefficients var a0= 0.341 var a1= -3.001 var a2= 2.811 var a3= -2.041 var a4= 0.0400 function R(img){ return img.expression( 'log10(blue/green)', { 'blue': img.select('B2'), 'green': img.select('B3') }); } var Ratio = citra.map(R) function chlor(img){ return img.expression( '10**(a0+a1*R+a2*R**2+a3*R**3)+a4', { 'a0': a0, 'a1': a1, 'a2': a2, 'a3': a3, 'a4': a4, 'R': Ratio }) } var chl = citra.map(chlor) var ChlVisPar = {min:0.4,max:1.5,palette:['blue','lime','yellow','orange','red']} Map.addLayer(chl,ChlVisPar,'Chlor-a') I cant visualize using the Map.addLayer(chla,ChlVisPar,'Chlor-a') and I got messages: Layer error: Image.pow, argument 'image1': Invalid type. Expected type: Image. Actual type: ImageCollection. How do I solve this?

I aim to create an Landsat8 image for bare-soil (no vegetation). To do this, I plan to derive the quantile of the bare soil index for each pixel and mask out pixels where the bare soil index has a value lower than the 95th quantile. I got until the point where I added the bare soil index to the imagecollection, and created an image of the 95th quantile of the bare soil index for each pixel. But how can I then use this as a mask? //Imports (country borders and landsat8 collection) var countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017"), L8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2"); // Define a function to add BSI (bare soil index) var addBSI = function(image){ var bsi = image.expression( '((SWIR+RED)-(NIR+BLUE)) / ((SWIR+RED) + (NIR + BLUE))', { 'RED': image.select('SR_B4'), 'SWIR': image.select('SR_B7'), 'NIR': image.select('SR_B5'), 'BLUE': image.select('SR_B2') }).rename('BSI'); return image.addBands(bsi); }; // Create 'collection', filtering landsat collection and adding bare soil index var collection = ee.ImageCollection(L8 // filter images within range (2014-2022) .filterDate("2014-01-01", "2022-12-31") // incldue geographic filter on created shape .filterBounds(netherlands) //filter away images with many clouds .filterMetadata("CLOUD_COVER", "less_than", 10) //add bare soil index to collection .map(addBSI)); //Create image of 95th quantile of bare soil index var bsi95 = collection.select('BSI').reduce(ee.Reducer.percentile([95])) //Mask out pixels where BSI is lower than the pixel-specific 95th quantile of the BSI //?

I am using the Sentinel-2 time series to identify the timing of forest disturbances using Google Earth Engine. I calculated the Normalized Burned Ratio (NBR) Index and I filled in the gaps in the time series. As can be seen in the following image there is a disturbance (breakpoint) at 2022-08-18. To identify these breakpoints my goal is: for each NBR image in the collection, subtract the value of the previous NBR image in the collection. I am trying to do this using the .iterate() function but I don't know where my error is. Complette script here: https://code.earthengine.google.com/473b9f15fee6c2c21f162c3265912495 My failed code here: // Breakpoint detection var time0 = nbrCol.first().get('system:time_start'); var first = ee.List([ ee.Image(0).set('system:time_start', time0).select([0], ['nbr']) ]); function breakPoint(image, list){ var previous = ee.Image(ee.List(list).get(-1)); var difference = image.subtract(previous).set('system:time_start', image.get('system:time_start')); return difference; } var diffCol = ee.ImageCollection(ee.List(nbrCol.iterate(breakPoint, first))); print(diffCol); Error: ImageCollection (Error) List.get, argument 'list': Invalid type. Expected type: List. Actual type: Image.

I am trying to apply Otsu's threshold to remove water from my NDVI image. But when running the code I get the error: ImageCollection (Error) Error in map(ID=1_2_LE07_020039_20220510): Dictionary.get: Dictionary does not contain key: bucketMeans. The image shows nothing when I try to display the image with Map.addLayer, so they probably have null. I am wondering if there's anyway to filter them out so that I don't get the error. Or any other idea would be helpful. var PEI = ee.Geometry.Polygon( [[[-88.64042172041307, 30.446701603950796], [-88.64042172041307, 30.32290398952697], [-88.49004635420214, 30.32290398952697], [-88.49004635420214, 30.446701603950796]]], null, false); Map.centerObject(PEI, 15) var otsu = function(histogram) { var counts = ee.Array(ee.Dictionary(histogram).get('histogram')); var means = ee.Array(ee.Dictionary(histogram).get('bucketMeans')); var size = means.length().get([0]); var total = counts.reduce(ee.Reducer.sum(), [0]).get([0]); var sum = means.multiply(counts).reduce(ee.Reducer.sum(), [0]).get([0]); var mean = sum.divide(total); var indices = ee.List.sequence(1, size); // Compute between sum of squares, where each mean partitions the data. var bss = indices.map(function(i) { var aCounts = counts.slice(0, 0, i); var aCount = aCounts.reduce(ee.Reducer.sum(), [0]).get([0]); var aMeans = means.slice(0, 0, i); var aMean = aMeans.multiply(aCounts) .reduce(ee.Reducer.sum(), [0]).get([0]) .divide(aCount); var bCount = total.subtract(aCount); var bMean = sum.subtract(aCount.multiply(aMean)).divide(bCount); return aCount.multiply(aMean.subtract(mean).pow(2)).add( bCount.multiply(bMean.subtract(mean).pow(2))); }); //print(ui.Chart.array.values(ee.Array(bss), 0, means)); // Return the mean value corresponding to the maximum BSS. return means.sort(bss).get([-1]); }; var threshold_func = function(image) { image = ee.Image(image).select('NDVI') var histogram = image.reduceRegion({ reducer: ee.Reducer.histogram() .combine('mean', null, true) .combine('variance', null, true), geometry: PEI, scale: 10, bestEffort: true }); var threshold = otsu(histogram.get('NDVI_histogram')); var water_removed = image.gt(threshold).selfMask(); return water_removed; }; var addNDVI = function(img) { var ndvi = img.normalizedDifference(['B5','B4']).rename('NDVI') return img.addBands(ndvi) } var addNDVIpre8 = function(img) { var ndvi = img.normalizedDifference(['B4','B3']).rename('NDVI') return img.addBands(ndvi) } var landsat5 = ee.ImageCollection("LANDSAT/LT05/C02/T1_TOA").filterBounds(PEI) .filterMetadata('CLOUD_COVER', 'less_than', 20) .select(['B5', 'B4', 'B3', 'QA_PIXEL']) .map(addNDVIpre8) print('l5', landsat5) var landsat7 = ee.ImageCollection("LANDSAT/LE07/C02/T1_TOA").filterBounds(PEI) .filterMetadata('CLOUD_COVER', 'less_than', 20) .select(['B5', 'B4', 'B3', 'QA_PIXEL']) .map(addNDVIpre8) print('l7', landsat7) var landsat8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterBounds(PEI) .filterMetadata('CLOUD_COVER', 'less_than',20) .select(['B5', 'B4', 'B3', 'QA_PIXEL']) .map(addNDVI) print('l8', landsat8) var landsat = landsat5.merge(landsat7).merge(landsat8) print('total', landsat) //var landsatlist = landsat.toList(landsat.size()); var landsat_otsu = landsat.map(threshold_func) print(landsat_otsu) Link: https://code.earthengine.google.com/46a1c8ed3b8ae773fea005a65ac87576