Integrated assessment of multi-sensor gridded data products for agricultural and hydrological applications

Abstract

New remote sensing and gridded reanalysis data products from sources including the NASA Soil Moisture Active Passive (SMAP) Mission, Global Precipitation Measurement (GPM) Mission, North American Land Data Assimilation System (NLDAS), Parameter-elevation Relationships on Independent Slopes Model (PRISM), and others provide unprecedented fine resolution characterization of near-surface atmospheric variables (e.g. air temperature, precipitation, downwelling solar radiation, etc.) and surface-to-root-zone hydrologic variables (e.g. soil moisture, hydraulic conductivity, soil composition, etc.) with national to global coverage. When integrated with state-of-the-science process models, these novel data products have the potential to provide useful information for applications in agriculture management, drought assessment, irrigation planning, and hydrological (e.g. streamflow) assessments. This study investigates the value of integrating these new multi-sensor gridded data products for hindcasting and prediction of regional-scale crop yield, irrigation demand, monitoring of agricultural drought, and hydrological flows.