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You will need to rework the data calculated in ui.Chart.image.series() into a feature collection. Then you will add a property for each different month and you can plot each month using ui.Chart.feature.groups(). // put the data similar as presented in the chart above in a featureCollection var featsPrecipitation = ee.FeatureCollection(precipitation.map(...


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I've been working with the Python API for long, and tried to use the mapclient module it has, but Google Developers did that first approach and then dropped it (I could find some links related to that if you need). I don't think there is a way you can just "insert the map". You'll have to code it yourself. You could start with mapclient.py, but I can ensure ...


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You can retrieve the info using getInfo and save it to local file # put centroids in a list centroids_list = centroids.toList(centroids.size()).map(lambda f: ee.Feature(f).geometry().centroid()).getInfo() # get only coordinates data = [p['coordinates'] for p in centroids_list] # file name filename = 'test.txt' # write file with open(filename, 'w+') as ...


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To answer your questions: As long as you have authenticated your system for using Earth Engine (i.e. earthengine authenticate) it should run. I am not sure how it behaves with a cron job though...you may want to set up a service account and use that to authenticate at the beginning of your script as described in the docs. The Earth Engine Python API comes ...


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The error “Remote request too large …” is an unfortunate limitation — unfortunate in that it really shouldn't exist, and it's also not worded very well. What it actually means is that you're somehow trying to compute an image that's too big (number of pixels times number of bytes needed to store each pixel). Most likely, this is due to your reprojection and ...


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The image you are looking for is not currently available in the Google Earth Engine repository. If you know that the image exists, you should report it. The best way to know if an image is in the repository is creating a collection and then printing it. For example, if you want to check the images of the tile T15SXD in june 2018 write the following: // ...


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The issue is in the visualization parameters. If you see the documentation for ee.data.getMapId you can see in the accepted parameters: bands (not band), and it must be a list of bands. So var viz = { bands: ['VV'], min: -50.0, max: -10, };


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You can do it easily by converting the timestamp of the image to a date object and then just requesting the "year" property. Modify your code as follows. // This function adds a time band to the image. var createTimeBand = function(image) { // get the system:time_start property and extract the year var timestamp = ee.Date(image.get('system:time_start')...


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The full dataset is for continental US. A bit much to turn into points. If you zoom in further to your image, you can see that it isn't individual pixels, but actually pixel groups. It is easy to vectorise this with a reduceToVectors() function, but without knowing what the aim of the Feature Collection, it is difficult to tell you which direction to take....


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The instrumentMode for HH-HV or HV is EW and not IW. Also, there is no 10m resolution available for these polarizations. To make sure you pick your geometry right: HH-HV or HH polarization for the monitoring of polar environments, sea-ice zones VV-VH or VV polarization for all other observation zones (with an exception for the Baltic Sea observed ...


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I believe the issue is in your feature collection. I can't access it as you don't have it shared, so can't investigate what is going on there. But I have tested the script with a random geometry placed on the map, and had no issues. Check that your feature collection doesn't have too many features to it. ================================== EDIT Thanks ...


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ee.Image.reduceToVectors converts an image to features, so its return value is a FeatureCollection. You cannot export a FeatureCollection using Export.image because a FeatureCollection is not an Image. If you want an image where the features have been drawn, then use ee.Image.paint after reduceToVectors. (It requires an image, which is the "background" for ...


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Just similar as you retrieve the image from the imageSelect function, you can get the image inside the polySelect function. Note that I changed the Map.addLayer to Map.Layers.set(), so the image is overwritten and not added,which is probably what you want making this kind of UI tools. var reduceTheRegion = function(){ var poly = polySelect.getValue(); ...


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Note that the new, better way to do this is with imageCollection.toBands().


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The issue is that you are creating the collection based on the date, and then clipping every image with your function. (even those that don't contain your geometry .... the whole globe) Change your code with a .filterBounds(geometry) before doing the clipping. // Load the Sentinel-1 ImageCollection. var sentinel= ee.ImageCollection('COPERNICUS/S1_GRD'); //...


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I think this might be what you are looking for? Adapted from this post. Apologies is it's a bit cumbersome. //get projection and scale var proj = sst.projection(); var scale = proj.nominalScale() // get coordinates image var latlon = ee.Image.pixelLonLat().reproject(proj); //Create a geometry object at the true center of the pixel var coords = latlon....


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Reduceregions should be applied to single images, instead of an image collection. Therefore, you shoul dmap over the image collection: // map over the collection (reduceRegions works on single imagery) var nl = nighttimeLights.map(function(image){ var year = image.date().format('YYYY'); var feats = image.reduceRegions({ collection: shp, reducer:...


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You are mixing client-side with server-side code. See the GEE help for that. Hopefully, this will show you how to work with a client-side for loop to export multiple featureCollections. As your asset is not shared, I am unsure if the code will work for the buffer you're trying to make. // get the list of features (client side) var Tables = ee.data.getList({...


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You can map a function that operates on an image over an entire imageCollection before reducing. For example, I use snippets from your code above to generate a cloud-free composite of Ghana by defining the masking functions first, then applying them to the imageCollection. Note that I expanded the date range because it looks like 2013 may have some ...


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A common technique in Earth Engine to export things like lists, values etc which are too large is to first create a feature collection from them and then export them. The feature collection need not have any geometry, so you can create features with empty geometry with the values of interest as properties. In your case, the following snippet does the trick ...


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For your particular example, you can do this: // map over the dictionary var mappedDictionary = dictionary.map(function(key, val){ return 'noData'; }); If you have non-null data in the dictionary, you could also use the val argument, see the example in the link. Link code


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You will have to make a long addition to your script to extract not from the mosaic, but from a collection for each of your indices. Then you reduce the images with .reduceregion() and add it as a property to a feature. After that, you create a collection of the feature so that it can be exported to a csv. ////////////////// // Export index results as a ...


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I would check out the tutorial done by Assoc. Prof. Shaun Levick at the GEARS Lab, Darwin Australia University. It covers, polarization and acquisition modes etc. https://youtu.be/129K0saWPu8 and the associated text tutorial at: https://github.com/geospatialeco/GEARS/blob/master/Intro_RS_Lab8.md You will notice in the tutorial that you can adjust the min ...


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I personally solved this issue after an aid from a friend of mine. The problem is not due to Python itself but by its interpreter. So, if you're using Eclipse follow this procedure: Window > Preferences > PyDev > Editor > Code Analysis > Undefined > Undefined variable from import > Ignore This is also quite useful if you want to not show those boring ...


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Here is an alternative approach that avoids nested map() calls, ee.Image.clip(), ee.Algorithms.If(), and accumulating results with a dictionary. Modify the cloud masking function to add a band cloud_flag that indicates whether or not there is a cloud. // Copyright 2019 Google LLC. // SPDX-License-Identifier: Apache-2.0 var cloudMaskL457 = function(image) {...


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the functionget of a list returns an object of type Object (or computed object). But Map.addLayer takes either Collection/Feature/Image/MapId. Since your computed object is an image, all you need to do is cast it to an image object. var Mar = ee.Image(listOfImages.get(2));


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I believe you are looking for vectorization: a transformation of the raster image into vector areas. You can probably use the rasterio.features.shapes() function from the Python's rasterio package, which gives out GeoJSON-like coordinates of contiguous areas of the same value from the raster, and then potentially use shapely to convert the areas to ...


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