Multifrequency, Multiscale Active and Passive Microwave Synthetic Dataset for Validating Soil Moisture Algorithms in Agricultural Regions

Estimation of soil moisture (SM) plays a key role in management of water resources, hydrology, drought assessment and crop yield. Changes in near-surface SM are highly sensitive to L-band active and passive (AP) microwave (MW) observations. Currently, the global SM information is provided by passive L-band missions, such as NASA soil moisture active passive (SMAP) and ESA soil moisture and ocean salinity (SMOS) at the temporal resolutions of 2-3 days, and spatial scales of 36 km and 25 km, respectively. However, such spatial resolutions are too coarse to provide relevant SM estimates in agricultural regions which are highly heterogeneous. Several approaches, such as thermal inertia based, smoothing filter intensity modulation (SFIM), principle of relevant information (PRI), universal triangle (UT), and bagged regression trees (BRT) have been used to downscale satellite SM observations from passive systems to finer spatial scales. Active systems, like upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) will provide L and S-band, and ESA BIOMASS will provide first P-band observations at finer spatial resolutions. In this study, a synthetic dataset is developed to validate sc aling algorithms and understand synergies between multifrequency AP MW observations at various spatial scales. We extend our previous study developed at L-band for the state of IOWA, one of the most important agricultural regions in the USA for two years, 2012 and 2015. It consists of 162 SMAP-like pixels of corn, soybean and bare soil. Calibrated crop growth model is used to obtain soil and vegetation conditions, and microwave emission and backscattering models are used to obtain MW signatures for growing corn and soybean at L, S, C and P-bands at 250m, 500m, 1km, 10km, 25km and 50km. This AP MW synthetic dataset will be used to evaluate SM algorithms in the agricultural regions.