Root-Zone Soil Moisture Estimation Using In-Situ Measurements and Remote Sensing Data

Root-zone soil moisture (RZSM) is a critical parameter for agricultural management, drought monitoring and weather prediction. However, large-scale and accurate daily RZSM are not available. This paper will propose an operational and robust approach for estimating RZSM using in-situ measurements and remote sensing products. Exponential filter will be used to generate RZSM from both soil moisture measurement from North American Soil Moisture Database (NASMD) and the observed soil moisture from satellite (SMAP and SMOS). Regression Kriging will be adopted with ancillary data, such as soil properties, vegetation index and land cover to interpolate the station-based RZSM to a 1 km gridded product. Finally, triple collocation will be applied to assess the errors from the three independent sources of soil moisture data (interpolated RZSM from NASMD; SMAP-based RZSM, and SMOS soil moisture). The errors will be used as weighting factors to combine the remote sensing observations and in-situ measurements. The new RZSM product will be generated at daily scale and 1 km spatial resolution. Finally, the hybrid RZSM product will be validated using in-situ measurements from NASMD.