Sahaar et al. (2024) Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning

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Identification

• Journal: Remote Sensing
• Year: 2024
• Date: 2024-10-04
• Authors: Shukran A. Sahaar, Jeffrey D. Niemann
• DOI: 10.3390/rs16193699

Research Groups

Not specified in the provided text.

Short Summary

This study investigates the application of machine learning, specifically XGBoost and other algorithms, to estimate soil moisture at multiple depths across the coterminous United States, integrating various satellite and observational data sources.

Objective

• To estimate soil moisture at multiple depths across the coterminous United States using machine learning techniques.

Study Configuration

• Spatial Scale: Coterminous United States
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: XGBoost and other machine learning algorithms.
• Data sources: SMAP (Soil Moisture Active Passive), GPM (Global Precipitation Measurement), ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), MODIS (Moderate Resolution Imaging Spectroradiometer), and other data sources (satellite, observation).

Main Results

Not specified in the provided text.

Contributions

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Funding

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Citation

@article{Sahaar2024Estimating,
  author = {Sahaar, Shukran A. and Niemann, Jeffrey D.},
  title = {Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning},
  journal = {Remote Sensing},
  year = {2024},
  doi = {10.3390/rs16193699},
  url = {https://doi.org/10.3390/rs16193699}
}