Overview
| DOI | https://doi.org/10.3334/ORNLDAAC/2326 |
|---|---|
| Version | 1 |
| Project | |
| Published | 2024-03-21 |
| Updated | 2024-03-21 |
| Usage | 27 downloads |
Description
This NASMo-TiAM (North America Soil Moisture Dataset Derived from Time-Specific Adaptable Machine Learning Models) dataset holds gridded estimates of surface soil moisture (0-5 cm depth) at a spatial resolution of 250 meters over 16-day intervals from mid-2002 to December 2020 for North America. The model employed Random Forests to downscale coarse-resolution soil moisture estimates (0.25 deg) from the European Space Agency Climate Change Initiative (ESA CCI) based on their correlation with a set of static (terrain parameters, bulk density) and dynamic covariates (Normalized Difference Vegetation Index, land surface temperature). NASMo-TiAM 250m predictions were evaluated through cross-validation with ESA CCI reference data and independent ground-truth validation using North American Soil Moisture Database (NASMD) records. The data are provided in cloud optimized GeoTIFF format.
Science Keywords
- LAND SURFACE
- SOILS
- SOIL MOISTURE/WATER CONTENT
- CLIMATE INDICATORS
- LAND SURFACE/AGRICULTURE INDICATORS
- SOIL MOISTURE
Data Use and Citation
This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy. See our Data Use and Citation Policy for more information.
Data Files
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Companion Files
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Dataset Companion Files
Dataset has 1 companion files.
- NASMo_TiAM_250m.pdf