H046-03
High-Resolution Soil Moisture using Thermal Hydraulic Disaggregation for Agricultural Applications
Tuesday, 8 December 2020: 16:08
Virtual
Pang-Wei Liu1, Rajat Bindlish1, Peggy E O'Neill2, Zhengwei Yang3, Venkataraman (Venkat) Lakshmi4, Bin Fang5 and Michael H Cosh6, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard SFC, Greenbelt, MD, United States, (3)USDA National Agricultural Statistics Service, Research and Development Division, Washington, DC, United States, (4)University of Virginia, Engineering Systems and Environment, Charlottesville, VA, United States, (5)University of South Carolina, School of Earth Ocean and Environment, Columbia, SC, United States, (6)U. S. Dept. of Agriculture, Beltsville, MD, United States
Abstract:
Soil moisture (SM) is a critical variable in terrestrial hydrology that controls water and heat fluxes at the near surface. Accurate SM would enhance field management activities (such as planting, fertilizing, and irrigation schedules) and provides valuable information for yield forecasting. Although SMAP meets the temporal revisit requirements for agricultural applications, its spatial resolution (~36 km) does not meet the field scale requirements in areas with high spatial heterogeneity. Various methodologies have been proposed to enhance the SMAP radiometric SM products. One of the methodologies uses thermal flux variation to disaggregate coarse resolution SM utilizing higher resolution land surface temperature (LST) from thermal sensors. The thermal flux-based approach was typically developed based upon the triangle method, which correlates a tripartite relationship between LST, vegetation, and SM at fine scale (1km), to estimate relative wetness. However, the thermal sensors suffer from missing observations in the presence of cloud cover. The soil hydraulic-based disaggregation approach uses water retention curve and field capacity information based upon water drainage processes to allocate water content distribution in the near-surface soil. Although such an approach is independent of the cloud cover effect and can provide data filling, it may underestimate impacts of thermal fluxes and vegetation on SM estimates.
This project improves the disaggregated SM algorithm by combining a thermal flux approach with a soil hydraulic-based approach to improve the spatio-temporal resolution of SM. The two disaggregation approaches are combined based on energy balance components and soil water capacity rate. During the strong heat transport seasons relative wetness from the thermal flux approach is weighted higher, while during the winter, wetness from the soil hydraulic approach is weighted higher. The new model, called Thermal Hydraulic disaggregation of Soil Moisture (THySM), is validated using in situ measurements of SM. High resolution SM from THySM was ingested in USDA's Crop Condition and Soil Moisture Analytics (Crop CASMA) system to help provide guidance on agricultural conditions and in making agricultural yield forecasts for food security (https://cloud.csiss.gmu.edu/Crop-CASMA/).
