I'm trying to download a 5 year timeseries for Canada of Sentinel 3 SLSTR LST data. Most of the data is stored 'offline' and has to be restored/ordered. However, it is limiting me to 20 scenes or so, whilst I'm looking to access thousands. Has anyone found a way to access offline Sentinel files and work around the limit?

I am trying to convert Sentinel-3 SLSTR S6 radiance data to a Geotiff file. However, I am finding converting the Sentinel-3 data which is in NCDF4 format to Geotiff format correctly very difficult. I am trying to use snappy API from Python. Since the lat-long information is provided in a separate file, my main problem is how to overlay geo-coordinates and radiance values from S6 and write it in a tiff file with the correct projection and resolution. The code that I am trying before now is below. Can somebody guide me in the process? rad13 = p.getBand(‘S6_radiance_ao’) w = rad13.getRasterWidth() h = rad13.getRasterHeight() rad13_data = np.zeros(w * h, np.float32) # numpy array of longitude rad13.readPixels(0, 0, w, h, rad13_data) lat = p.getBand(‘x_ao’) long = p.getBand(‘y_ao’) w1 = lat.getRasterWidth() h1=long.getRasterHeight() lat_data = np.zeros(w1 * h1, np.float32) # numpy array of latitude lat.readPixels(0, 0, w, h, lat_data) long_data = np.zeros(w1 * h1, np.float32) long.readPixels(0, 0, w, h, long_data) # numpy array of longitude ###After I got the separate numpy arrays of lat-long and radiance values I have been trying to convert it into a geotiff file xmin,ymin,xmax,ymax = [long_data.min(),lat_data.min(),long_data.max(),lat_data.max() nrows,ncols = np.shape(rad13_data) xres = (xmax-xmin)/float(ncols) yres = (ymax-ymin)/float(nrows) geotransform=(xmin,xres,0,ymax,0, -yres) output_raster = gdal.GetDriverByName(‘GTiff’).Create(‘myraster.tif’,ncols, nrows, 1 ,gdal.GDT_Float32) output_raster.SetGeoTransform(geotransform) ####I am getting a 0 value here so not able to proceed srs = osr.SpatialReference() srs.ImportFromEPSG(4326) ###getting a 0 value here output_raster.SetProjection( srs.ExportToWkt()) output_raster.GetRasterBand(1).WriteArray(rad_array)

dask.array Coordinates: crs object +proj=utm +datum=WGS84 +ellps=WGS84 +no_defs +units=m +zone=33.0 +type=crs * y (y) float64 4.006e+06 4.006e+06 4.005e+06 ... 3.951e+06 3.95e+06 * x (x) float64 4.094e+05 4.104e+05 4.114e+05 ... 4.832e+05 4.842e+05 Attributes: sensor: slstr view: n polarization: None standard_name: toa_brightness_temperature coordinates: ('longitude_in', 'latitude_in') resolution: 1000 wavelength: (10.4, 10.85, 11.3) file_type: esa_l1b_ntir long_name: Gridded pixel brightness temperature for channel S8... units: K level: None calibration: brightness_temperature name: S8_in platform_name: Sentinel-3A modifiers: () start_time: 2018-07-20 09:12:25.111891 end_time: 2018-07-20 09:15:24.803164 area: Area ID: LS8_duplicate\nDescription: Patch subset i... ancillary_variables: [] I have this Xarray which resulted from reading and resampling a NetCDF file using satpy. newscn = scn.resample(area_def, resampler='nearest') Printing the newscn results in an xarray. How can I extract the (76, 76) array with float32 values as a numpy array?

I'm trying to download a 5 year timeseries for Canada of Sentinel 3 SLSTR LST data. Most of the data is stored 'offline' and has to be restored/ordered. However, it is limiting me to 20 scenes or so, whilst I'm looking to access thousands. Has anyone found a way to access offline Sentinel files and work around the limit?

I am trying to convert Sentinel-3 SLSTR S6 radiance data to a Geotiff file. However, I am finding converting the Sentinel-3 data which is in NCDF4 format to Geotiff format correctly very difficult. I am trying to use snappy API from Python. Since the lat-long information is provided in a separate file, my main problem is how to overlay geo-coordinates and radiance values from S6 and write it in a tiff file with the correct projection and resolution. The code that I am trying before now is below. Can somebody guide me in the process? rad13 = p.getBand(‘S6_radiance_ao’) w = rad13.getRasterWidth() h = rad13.getRasterHeight() rad13_data = np.zeros(w * h, np.float32) # numpy array of longitude rad13.readPixels(0, 0, w, h, rad13_data) lat = p.getBand(‘x_ao’) long = p.getBand(‘y_ao’) w1 = lat.getRasterWidth() h1=long.getRasterHeight() lat_data = np.zeros(w1 * h1, np.float32) # numpy array of latitude lat.readPixels(0, 0, w, h, lat_data) long_data = np.zeros(w1 * h1, np.float32) long.readPixels(0, 0, w, h, long_data) # numpy array of longitude ###After I got the separate numpy arrays of lat-long and radiance values I have been trying to convert it into a geotiff file xmin,ymin,xmax,ymax = [long_data.min(),lat_data.min(),long_data.max(),lat_data.max() nrows,ncols = np.shape(rad13_data) xres = (xmax-xmin)/float(ncols) yres = (ymax-ymin)/float(nrows) geotransform=(xmin,xres,0,ymax,0, -yres) output_raster = gdal.GetDriverByName(‘GTiff’).Create(‘myraster.tif’,ncols, nrows, 1 ,gdal.GDT_Float32) output_raster.SetGeoTransform(geotransform) ####I am getting a 0 value here so not able to proceed srs = osr.SpatialReference() srs.ImportFromEPSG(4326) ###getting a 0 value here output_raster.SetProjection( srs.ExportToWkt()) output_raster.GetRasterBand(1).WriteArray(rad_array)

I'm trying to download a 5 year timeseries for Canada of Sentinel 3 SLSTR LST data. Most of the data is stored 'offline' and has to be restored/ordered. However, it is limiting me to 20 scenes or so, whilst I'm looking to access thousands. Has anyone found a way to access offline Sentinel files and work around the limit?