High Spatio-Temporal Root Zone Soil Moisture for Agriculture using Data Assimilation in the Land Information System Framework Case Study in Mississippi River Basin

Soil moisture in the root zone (RZSM) is one of the critical geophysical variables that governs water and energy transport between land and atmosphere. In agriculture, accurate RZSM estimates provide guidelines for field management practices such as planting, irrigation scheduling, and harvesting, which impact crop yields. Knowledge of RZSM during the growing season is critical for crop production forecasting. In situ observations of RZSM are generally made at small scales only. For large study domains, land surface models (LSM) are used to estimate SM content at various depths including root zone, but the estimates may diverge from reality over time due to error sources in the forcing data, initial conditions, and model parameters. Incorporating auxiliary observations into such dynamic models through data assimilation may improve RZSM estimates. Presently, satellite-based remote sensing missions, such as Soil Moisture Active Passive (SMAP), provide SM estimates in the near-surface (0-5 cm) at their baseline spatial resolutions of 36 km with a repeat cycle of 2-3 days. Surface SM observations can be assimilated in the LSM by updating the surface information to improve RZSM estimates. Although the use of SMAP data may meet the temporal requirement of agricultural applications, its spatial resolution (~36 km) may not be adequate for agriculture due to high heterogeneity in agricultural regions. The goal of this project is to implement assimilation of the high spatial resolution SM product at 1 km created from thermal hydraulic disaggregation of soil moisture (THySM) into the Noah-MP model in the Land Information System framework. THySM was developed by combining a thermal flux approach with a hydraulic-based approach to enhance spatial resolution of surface soil moisture from SMAP Enhanced products. The data assimilation is conducted at the scale of 1 km daily in the Mississippi River basin to enhance the spatio-temporal resolution of RZSM estimates. The RZSM estimates are validated using in situ measurements and will be investigated for their potential impacts on agriculture. Particularly, the consequence of extreme hydrologic events, such as flooding, on crop production will be explored in the study.