@mitchbon,
I'm not sure if I have an exact answer for you, but here are a few thoughts to consider.
- The MSLSP30NA data product is currently archived and distributed from the LP DAAC on-prem archive. In order to read these data remotely, you’ll need to pass your Earthdata Login credentials to authenticate and access these data. I have done this using Earthaccess and its use of fsspec. However, it does seem like remotely accessing these data is slow. You may be better off downloading the data first before working with them in Python/Xarray.
- pystac_client can certainly be used to find assets, but I'm uncertain if stackstac can read netCDF4 files as STAC assets. I’m also uncertain whether stackstac can pass authentication credentials via anything other than GDAL env variables.
- Finally, the MSLSP30NA data product is in a Universal Transverse Mercator (UTM) projection on the Military Grid Reference System (MGRS). When querying for granules using CMR and a region of interest, you may end up with granules that are from two different UTM zones. It’s recommended to only mosaic granules that are within a common zone. Mosaicking across zones will likely have negative impacts on your analysis. So, when trying to read granules into a Python object (i.e., xarray dataset) be sure that those granules are from within the same UTM zone. If you’re intent is to have a final mosaiced map, do the mosaicing after you perform your analysis by zone.
I'm not sure if I have an exact answer for you, but here are a few thoughts to consider.
- The MSLSP30NA data product is currently archived and distributed from the LP DAAC on-prem archive. In order to read these data remotely, you’ll need to pass your Earthdata Login credentials to authenticate and access these data. I have done this using Earthaccess and its use of fsspec. However, it does seem like remotely accessing these data is slow. You may be better off downloading the data first before working with them in Python/Xarray.
- pystac_client can certainly be used to find assets, but I'm uncertain if stackstac can read netCDF4 files as STAC assets. I’m also uncertain whether stackstac can pass authentication credentials via anything other than GDAL env variables.
- Finally, the MSLSP30NA data product is in a Universal Transverse Mercator (UTM) projection on the Military Grid Reference System (MGRS). When querying for granules using CMR and a region of interest, you may end up with granules that are from two different UTM zones. It’s recommended to only mosaic granules that are within a common zone. Mosaicking across zones will likely have negative impacts on your analysis. So, when trying to read granules into a Python object (i.e., xarray dataset) be sure that those granules are from within the same UTM zone. If you’re intent is to have a final mosaiced map, do the mosaicing after you perform your analysis by zone.
Statistics: Posted by LP DAAC - afriesz — Wed Jan 24, 2024 3:59 pm America/New_York