Jingyi Huang, an assistant professor of soil science, received USDA-NIFA funding for his project ML-HRSM: Machine Learning High-Resolution Soil Moisture product development in support of USDA NASS crop monitoring through NIFA’s Data Science for Food and Agricultural Systems program. It was among 13 projects sharing more than $7 million in funding.

Project summary (from CRIS website): National Agricultural Statistics Service (NASS) conducts weekly surveys of crop and soil moisture conditions for U.S. cropland and provides coarse-resolution satellite soil moisture and vegetation conditions via a web application Crop-CASMA. However, its coarse-resolution maps are unable to capture field/subfield level soil moisture variations. It is urgently needed to develop field/subfield-level soil moisture maps for NASS and the agricultural community to monitor crop growth conditions and assess drought or flood impact. This project will establish a partnership between US and Canadian institutes to develop new Machine Learning High-Resolution SoilMoisture (ML-HRSM2.0) products in support of NASS crop monitoring and assessment. In situ networks, satellite imagery and model-derived weather, soil moisture, terrain, and soil maps will be combined to predict daily soil water content (SWC) and plant available water storage (PAWS) at 100-m at the surface and root zone since 2016. We will combine ML models with a process model via data assimilation to develop crop soil moisture condition maps and NASS weekly soil moisture condition reports and disseminate all maps over Crop-CASMA for enhancing NASS soil moisture condition monitoring operation and for free public use. It is expected that using ML-HRSM2.0 product will help NASS and the Agricultural community improve crop condition monitoring, disaster assessment, and operational decision makings.