Uncertainty in Soil Moisture Retrievals Using the SMAP Combined Active-Passive Algorithm for Growing Sweet Corn

The baseline active and passive (AP) algorithm of the NASA Soil Moisture Active Passive (SMAP) mission disaggregates the brightness temperature (T-B) from a spatial resolution of 36 km to 9 km for the soil moisture (SM) using the radar backscattering coefficient (sigma(0)) at 3 km. This algorithm was derived based upon an assumption of a linear relationship between T-B and sigma(0). In this study, we investigated the robustness of this assumption with plot-scale AP measurements obtained under different conditions of surface roughness and stages of growing sweet corn. The uncertainties in the estimated T-B at 9 km and, hence, the retrieved SM, due to uncertainties in the algorithm parameters, beta and Gamma, were assessed under different landcover heterogeneities. Overall, the linear regression was robust, with r(2) > 0.75 under bare soil conditions when surface scattering is dominant and > 0.52 during the growing season. The uncertainties in beta and Gamma due to AP observations result in uncertainties in retrieved SM < 0.04 m(3)/m(3) for most conditions of heterogeneity. The differences in T-B at 9 km, obtained when using beta derived from vegetation water content (VWC) and using those from radar vegetation index, were also assessed. The errors in retrieved SM could reach as high as 0.5 m(3)/m(3) for the worst-case scenario, when an intermediate scale contains high VWC, but the coarse scale region has low averaged VWC. These results suggest that determination of growth stages using a biophysical parameter is essential for beta estimations, particularly for highly heterogeneous landcovers.