How to download SMAP data in GeoTiff and HDF formats from the NSIDC (

[@samestate11]

Thank you! You’re a lifesaver

[@akashsenthilkumaran9610]

Thank you so much. It was very helpful. Expecting more videos from you.

[@geospatialcamp5618]

Thank you!

[@heatherlovesyou11]

thank you, this was very helpful

[@senpaiyajuu2914]

looks like all smap soil Moisture h5 file are globe size. is there any way we can select a single area to download? globe is too big

[@jhoreenejulian8767]

Thank you for the very helpful tutorial! But may I know how to download and extract large data for a month-long period.

[@geospatialcamp5618]

You should select the period of interest and it downloads all data at once. There will be one raster file for each 3-hourly soil moisture data that I then summarized by averaging through Python codes.

[@habtamumulugeta2298]

thank you for your excellent explanation, but still I could not get the exact measurements of the soil moisture, which means how can I read it in Arc map gis? thank you so much

[@geospatialcamp5618]

Did you download the GeoTiff format? You can add the GeoTiff file directly into ArcMAP (same as a raster file). Clicking on each pixel (grid cell) using ArcMAP "Identify" tool, you can know the exact values of soil moisture. If you have downloaded large data (for a long period) let me know and I can show you how to extract data for your study area.

[@raymondariho2613]

@@geospatialcamp5618 I downloaded numerous Geotiff files for three days. How do I get exact soil moisture data using python

[@majilybalo5100]

Hello, ma'am. Do you have a video tutorial on extracting monthly averaged soil moisture data with a resolution of 30m or less?

[@amenehtavakol8587]

It should be the same! At the beginning choose the monthly data and the rest is the same!

[@max477]

after downloading 3 hourly data for 1 day How to convert that data into 1 day data what method or logic you use.
Thank you for sharing.

[@geospatialcamp5618]

I wrote a script in python (arcpy package) and averaged 3-hourly data into daily data (specified the date based on the file names)

[@geospatialcamp5618]

Python Script:
import arcpy
import os from shutil
import copy2
arcpy.CheckOutExtension("Spatial")
from collections import defaultdict
def main():
raster_dir = r"D:\NASA\Data\USA\SMAP_L4\Processed-Daily"
arcpy.env.workspace = raster_dir
raster_days = defaultdict(list)
for r in arcpy.ListRasters():
#To calculate Daily Average
#raster_days[r[15:23]].append(r)
# To calculate Monthly Average
raster_days[r[0:6]].append(r)
if not os.path.exists(raster_dir+"\Processed-Monthly"):
os.makedirs(raster_dir+"\Processed-Monthly")
for date, rasters in raster_days.items():
print(date)
outCellStatistics = arcpy.sa.CellStatistics(rasters, "MEAN")
outCellStatistics.save(raster_dir+"\Processed-Monthly\{}.tif".format(date))
if _name_ == '__main__':
main()

[@max477]

@@geospatialcamp5618 Thanks you so much...!
what kind of jobs profile available for these skills..? can you suggest any well-paying remote jobs related to these skills.?
Need video related to this.
Thanks in advance

[@Home-bh6rf]

Great tutorial. After downloading, as these are 3 hourly data, could you please share some way to format them daily and then monthly? Thank you

[@geospatialcamp5618]

I wrote a python script to summarize data into daily and monthly raster files. IF you leave an email, I can send the script to you.

[@geospatialcamp5618]

Check this: import arcpy
import os from shutil
import copy2
arcpy.CheckOutExtension("Spatial")
from collections import defaultdict
def main():
raster_dir = r"D:\NASA\Data\USA\SMAP_L4\Processed-Daily"
arcpy.env.workspace = raster_dir
raster_days = defaultdict(list)
for r in arcpy.ListRasters():
#To calculate Daily Average
#raster_days[r[15:23]].append(r)
# To calculate Monthly Average
raster_days[r[0:6]].append(r)
if not os.path.exists(raster_dir+"\Processed-Monthly"):
os.makedirs(raster_dir+"\Processed-Monthly")
for date, rasters in raster_days.items():
print(date)
outCellStatistics = arcpy.sa.CellStatistics(rasters, "MEAN")
outCellStatistics.save(raster_dir+"\Processed-Monthly\{}.tif".format(date))
if __name__ == '__main__':
main()

[@Home-bh6rf]

@@geospatialcamp5618 thank you so much

[@abrahamalvarez2730]

hello the website was way more simpler, no it is not like this video and it is so difficult to extract the data.