The Gridded National Soil Survey Geographic Database & Microsoft's Planetary Computer (part 1)

The Gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database containing soils information for all areas of the US and Island Territories. It combines data from three sources in the USDA’s Ag Data Commons:

SURRGO – Soil Survey Geographic Database
STATSGO2 - State Soil Geographic Database
RSS – Raster Soil Survey

Microsoft’s Planetary Computer

Microsoft’s Planetary Computer, available for preview by request, provides access to a diverse collection of public earth-science data via the Planetary Computer Hub. The hub provides developer workspaces pre-configured with API’s, compute & storage resources to access and process public earth science data. Workspace language preferences are available for Python, R, Tensorflow & QGIS. I used used the Python notebook workspace to create the following notebook that lists tables and columns in the gNATSGO collection.

gNATSGO Data Dictionary (Python Notebook Code)

Github Repository



  # Import required libraries
      import planetary_computer  

      import numpy as np  

      import pandas as pd    

      import rioxarray  

      import xarray  

      import pystac_client  



   # Create planetary computer client
      catalog = pystac_client.Client.open(
          “https://planetarycomputer.microsoft.com/api/stac/v1",
          modifier=planetary_computer.sign_inplace,
      )
   # Empty Dataframe
      df_dictionary = pd.DataFrame(columns = [‘name’,’type’,’description’,’table_name’])
  # Search catalog for gnatsgo table collection
      search = catalog.search(
          collections=[“gnatsgo-tables”]
      )
  # Get a list of tables
      collection_tables = list(search.get_items())
      df_dictionary = pd.DataFrame(columns = [‘name’,’type’,’description’,’table_name’])
  # Populate dataframe with table meta-data
      for i in collection_tables:
          df_i = pd.DataFrame(i.properties[“table:columns”])
          df_i[“table_name”] = i.id
          df_dictionary = pd.concat([df_dictionary, df_i], ignore_index=True, sort=False)
          pd.set_option(‘display.max_colwidth’, 1000)
  display(df_dictionary.style.set_properties(**{'text-align': 'left'}))

# Save Data Frame as .csv
      df_dictionary.to_csv(‘gNATSGO Data Dictionary.csv’)


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 Download resulting meta-data file: gNATSGO Data Dictionary.csv

Planetary Computer gNATSGO Table Collection

gNatsgo Tabular Data: List of tables in the collection
gNatsgo Rasters: Description of Raster Data in the colleciton


Next up
Part 2: Exporting Table Data from the gNATSGO Collection and defining table relationships
Part 3: Blackland Prairie Crop Yields (Power BI Visualizations)

  Microsoft Planetary Computer, Soil Survey, gNATSGO — Dec 7, 2022