Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Vegetation water content (VWC) is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms. Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i.e., "agricultural crops," based on climatological databases. Determining crop type-specific equations for water content could lead to improvements in the soil moisture retrievals. Data to address this issue are lacking, and as a part of the calibration and validation program for NASA's Soil Moisture Active Passive (SMAP) Mission, field experiments are conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP soil moisture products for these intensive agricultural regions. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of 2016 that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation. In Iowa, regression equations for corn and soybean are developed that provided VWC with root mean square error (RMSE) values of 1.37 and 1.10 kg/m(2), respectively. In Manitoba, corn and soybean equations are developed with RMSE values of 0.55 and 0.25 kg/m(2). Additional crop-specific equations are developed for winter wheat (RMSE of 0.07 kg/m(2)), canola (RMSE of 0.90 kg/m(2)), oats (RMSE of 0.74 kg/m(2)), and black beans (RMSE of 0.31 kg/m(2)). Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.