[Dataset Title/Name]: NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset

### Contact Details samapriya@gmail.com ### Dataset 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. **Source:** https://www.earthdata.nasa.gov/data/catalog/ornl-cloud-nasmo-tiam-250m-2326-1 **Provider:** Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) **DOI:** https://doi.org/10.3334/ORNLDAAC/2326 **Citation:** Llamas, R., Olaya, P., Taufer, M., & Vargas, R. (2024). NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2326 **Spatial Coverage:** North America **Temporal Range:** 2002-06-26/2020-12-31 **Format:** GeoTIFF **Resolution:** 250m **CRS:** CARTESIAN **Dataset Size:** 538.587 GB **Update Frequency:** static **License:** CC0-1.0 **Data Availability:** Data can be accessed via the User Guide and documentation provided by ORNL DAAC. See the 'Documents' section for 'USER'S GUIDE ORNL DAAC Data Set Documentation' and 'NASMo-TiAM 250m 16-day North America Surface Soil Moisture Dataset: NASMo_TiAM_250m.pdf'. ### Earth Engine Snippet if dataset already in GEE null ### Enter license information CC0-1.0 ### Keywords Soil Moisture/Water Content, Soil Moisture, North America, surface soil moisture, machine learning, GeoTIFF, remote sensing, downscaling, land surface temperature, NDVI ### Code of Conduct - [x] I agree to follow this project's Code of Conduct