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.
Resources
| Name | Format | Description | Link |
|---|---|---|---|
| Get a related visualization | JPG | Random Forests predictions of mean volumetric soil moisture values for North America derived from 426 biweekly periods from 2002 to 2020. | Open resource |
| View documentation related to this dataset | HTML | ORNL DAAC Data Set Documentation | Open resource |
| Download this dataset | HTML | This link allows direct data access via Earthdata login | Open resource |
| View documentation related to this dataset | NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset: NASMo_TiAM_250m.pdf | Open resource | |
| This dataset's landing page | HTML | Data set Landing Page DOI URL | Open resource |
| Google Scholar search results | HTML | Search results for publications that cite this dataset by its DOI. | Open resource |