Loading...
GLDAS provides datasets on land surface conditions, including soil moisture, temperature, precipitation and other hydrological variables, derived from satellite and ground-based observations.
GLDAS data is in NetCDF-4 or HDF5 format. Engineers use `xarray.open_dataset()` to load multi-dimensional grids with lat/lon/time dimensions, selecting spatial subsets with `.sel()` and computing regional averages with `.mean()`. NASA Earthdata login is required.
GLDAS land surface data trains AI models for drought prediction, crop yield forecasting, and water resource management. The global soil moisture and evapotranspiration grids are powerful features for climate-aware AI systems that predict agricultural conditions and water stress at continental scale.
# pip install xarray netCDF4 matplotlib
import xarray as xr
# Download GLDAS NetCDF from NASA Earthdata (requires login)
ds = xr.open_dataset("GLDAS_NOAH025_M.A202301.021.nc4")
soil_moisture = ds["SoilMoi0_10cm_inst"]
print(soil_moisture.mean(dim=["lat", "lon"]).values, "kg/m²")
soil_moisture.mean(dim="time").plot()Official dataset source
More datasets used by Python data engineers.
EOSDIS provides access to a wide range of Earth observation datasets, including satellite imagery, climate data, land cover, oceanography and atmospheric data.
NOAA platform provides access to a vast collection of climate-related datasets, including historical weather data, climate observations, satellite imagery and climate model outputs.
The NCEI, part of NOAA, provides access to a wide range of environmental datasets, including climate data, weather observations, oceanographic data and geophysical data.