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1

You are treating the data as if it were a regular raster, but it is not. See: library(ncdf4) nc_data <- nc_open("T2020165182000.L2_LAC_OC.nc") chlor <- ncvar_get(nc_data, "geophysical_data/chlor_a") lat <- ncvar_get(nc_data, "navigation_data/latitude") long <- ncvar_get(nc_data, "navigation_data/longitude") ...


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Some of your inputs don't exist on all days. (Specifically, there is no LST on day 42 and others). You can't train a classifier with missing points in your data, so the classifiers for those days fail. print(ee.Image(datasetMap.toList(1, 42).get(0)) .reduceRegion(ee.Reducer.count(), table.geometry())) That said, Creating a new classifier for each day ...


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You can explicitly cast each image inside scaledlst1 and scaledlst2 to float. That ensures that they have the same type: var scaledlst1 = lst.map(function (lst) { return lst.expression( '(lst/30)',{ 'lst': lst.select('LST_Day_1km') }).float() }) var scaledlst2 = lst.map(function (lst) { return lst.expression( '(lst * (-0.05)) + 2.5',{ ...


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If anyone (like me) can't get the Oracle official download to work, i was able to find a direct link on GitHub, searching for the filename "jre-9.0.4_windows-x64_bin.exe" the link is: https://download.oracle.com/otn/java/jdk/9.0.4+11/c2514751926b4512b076cc82f959763f/jre-9.0.4_windows-x64_bin.exe?AuthParam=1622138782_bbc2c982f49c7b24512f46bf602da91b ...


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There are a couple of strange things in your code, explaining why it doesn't run at all: There's no band called Optical_Depth_055y You're calling map(maskedImage). map() takes a function as argument, while maskedImage is an ee.Image. To do the cloud masking, you will need to determine the mask for the individual images inside your collection. So the QA ...


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It seems that the "constant" band name arises because the second expression begins with a constant, which is treated as a single-band constant image. Note that with a small adjustment to your code, the resulting band names in scaledls2 is the expected "LST_Day_1km": // Select a geometry for reproducibility var geometry = ee....


3

I didn't understand what you want to achieve, but if you change min and max value in visParams something appears on the map. var bp = ee.FeatureCollection("users/miaohu828/bankment_polygon"); var modisLandcover = ee.ImageCollection("MODIS/006/MCD12Q1"); var filtered2018 = modisLandcover.filter(ee.Filter.date('2018-01-01', '2018-12-31')...


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For Python users, I have written a short article and Gist explaining how to perform this translation in GDAL and Rasterio. from osgeo import gdal in_file = f"./modis.hdf" # raw MODIS HDF in sinusoid projection out_file = f"./modis.tif" # open dataset dataset = gdal.Open(in_file,gdal.GA_ReadOnly) subdataset = gdal.Open(dataset....


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Something like this should work. I don't have your imagery, so I wasn't able to test it all the way through. If you're not using tidyverse, you can probably rewrite the date extraction in base R. library(tidyverse) library(lubridate) library(raster) #Simple example filenames <- data.frame(name = c("MOD13A3.A2000122.h20v09.006.2015137094939.tif",...


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