Cai et al. (2025) High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-12-06
• Authors: Shuohao Cai, Yijia Xu, Zhengwei Yang, Wade T. Crow, Zhou Zhang, Jiali Shang, Jiangui Liu, Peter La Follette, Chris Reberg‐Horton, Harry H. Schomberg, Steven B. Mirsky, Brian Davis, Sarah Seehaver, Alexis Correira, Andrea Basche, Ashley Waggoner, Charles N. Ellis, Dara M. Park, Danielle Treadwell, David Campbell, DeAnn Presley, Esleyther L. Henriquez Inoa, Heather Darby, J. Adam, Jarrod O. Miller, Joseph Haymaker, John R. Wallace, Julia W. Gaskin, Kipling S. Balkcom, Lindsey Ruhl, Mark S. Reiter, Matthew D. Ruark, Michael F. Flessner, Cynthia Sias, Payton Davis, Peter Tomlinson, Richard G. Smith, Nicholas Warren, Ryan Dierking, Shalamar D. Armstrong, Traci L. Almeida, Jingyi Huang
• DOI: 10.1016/j.rse.2025.115167

Research Groups

• Department of Soil and Environmental Sciences, University of Wisconsin-Madison, USA
• Department of Biological Systems Engineering, University of Wisconsin-Madison, USA
• Research and Development Division, National Agricultural Statistics Service, United States Department of Agriculture, USA
• USDA ARS Hydrology and Remote Sensing Laboratory, USA
• Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Canada
• Lynker, USA
• Department of Crop and Soil Sciences, North Carolina State University, USA
• USDA-ARS Sustainable Agricultural Systems Laboratory, USA
• Department of Agronomy, Kansas State University, USA
• Department of Agronomy and Horticulture, University of Nebraska-Lincoln, USA
• University of Missouri Extension, USA
• Department of Plant and Environmental Sciences, Clemson University, USA
• Department of Horticultural Sciences, University of Florida, USA
• Northwest Crops and Soil Program, University of Vermont Extension, USA
• College of Agriculture, Land Resources and Environmental Sciences, Montana State University, USA
• Department of Plant and Soil Sciences, University of Delaware, USA
• Eastern Shore Agricultural Research and Extension Center, Virginia Tech, USA
• Plant Science Department, The Pennsylvania State University, USA
• Department of Crop and Soil Sciences, University of Georgia, USA
• USDA-ARS National Soil Dynamics Laboratory, USA
• School of Plant and Environmental Sciences, Virginia Tech, USA
• Department of Natural Resources and the Environment, University of New Hampshire, USA
• EarthOptics formally Indigo Ag, USA
• School of Agronomy, Purdue University, USA

Short Summary

This paper introduces a novel framework for monitoring high-resolution surface and rootzone soil moisture over US cropland by assimilating multi-source remote sensing data, machine learning, and a hydrological model. The objective is to provide accurate soil moisture data crucial for water resource management, drought forecasting, and nutrient transport estimation at the field scale.

Objective

• To develop a novel framework for accurate and high spatiotemporal resolution monitoring of surface and rootzone soil moisture in US cropland.

Study Configuration

• Spatial Scale: US cropland, with a focus on field-scale resolution.
• Temporal Scale: High spatiotemporal resolution monitoring (specific frequency not detailed in provided text).

Methodology and Data

• Models used: Layered Green and Ampt Infiltration with Redistribution model, Machine Learning.
• Data sources: Multi-source remote sensing data.

Main Results

• The provided text does not contain the main results of the study.

Contributions

• Presents a novel framework that integrates multi-source remote sensing data, machine learning, and a hydrological model to achieve high-resolution soil moisture monitoring.
• Aims to improve soil moisture data crucial for sustainable agriculture, water resource management, drought forecasting, and nutrient transport estimation at the field scale.

Funding

• The provided text does not contain funding information.

Citation

@article{Cai2025Highresolution,
  author = {Cai, Shuohao and Xu, Yijia and Yang, Zhengwei and Crow, Wade T. and Zhang, Zhou and Shang, Jiali and Liu, Jiangui and Follette, Peter La and Reberg‐Horton, Chris and Schomberg, Harry H. and Mirsky, Steven B. and Davis, Brian and Seehaver, Sarah and Correira, Alexis and Basche, Andrea and Waggoner, Ashley and Ellis, Charles N. and Park, Dara M. and Treadwell, Danielle and Campbell, David and Presley, DeAnn and Inoa, Esleyther L. Henriquez and Darby, Heather and Adam, J. and Miller, Jarrod O. and Haymaker, Joseph and Wallace, John R. and Gaskin, Julia W. and Balkcom, Kipling S. and Ruhl, Lindsey and Reiter, Mark S. and Ruark, Matthew D. and Flessner, Michael F. and Sias, Cynthia and Davis, Payton and Tomlinson, Peter and Smith, Richard G. and Warren, Nicholas and Dierking, Ryan and Armstrong, Shalamar D. and Almeida, Traci L. and Huang, Jingyi},
  title = {High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115167},
  url = {https://doi.org/10.1016/j.rse.2025.115167}
}