North America Soil Moisture Dataset at 250-Meter Resolution

NASMo-TiAM provides gridded estimates of surface soil moisture (0-5 cm depth) at a 250-meter spatial resolution over 16-day intervals from mid-2002 to December 2020 for North America. The dataset was generated by NASA using Random Forests to downscale coarse-resolution European Space Agency Climate Change Initiative (ESA CCI) data, validated against ground-truth records from the North American Soil Moisture Database. Data files are provided in cloud-optimized GeoTIFF format.

Use Cases

• Analyzing soil moisture trends for drought assessment using the 16-day interval time series.
• Validating hydrological or land surface models with spatially detailed 250-meter resolution gridded estimates.
• Studying the correlation between soil moisture and dynamic covariates like Normalized Difference Vegetation Index (NDVI) and land surface temperature.
• Assessing agricultural water stress by mapping surface soil moisture (0-5 cm depth) across croplands.
• Downscaling regional climate data using the machine learning methodology and static terrain parameter covariates.

Strengths

• Dataset provides high spatial detail at a 250-meter resolution, a significant downscaling from the source 0.25-degree ESA CCI data.
• Model predictions were validated through both cross-validation with ESA CCI reference data and independent ground-truth validation using the North American Soil Moisture Database (NASMD).
• Offers a long-term, consistent time series with 16-day interval estimates covering over 18 years from mid-2002 to December 2020.

Limitations

• Conflicting last updated dates exist between platforms (datagov: 2026-03-12; nasa_earthdata: 2020-12-31), casting uncertainty on dataset maintenance.
• Specific column names and the exact dataset size (row count, file size) are not provided in the available metadata.
• The 'other-license-specified' license on datagov requires users to locate and review the specific license terms before use.

Provenance

Source

National Aeronautics and Space Administration (NASA)

Collection Method

Generated using Random Forests machine learning models to downscale European Space Agency Climate Change Initiative (ESA CCI) soil moisture estimates with static and dynamic covariates.

Time Range

mid-2002 to December 2020

Freshness

2026-03-12 23:53:20.561689

Geography

North America