Application of a change detection soil moisture retrieval algorithm to combined, semi-concurrent radiometer and radar observations

This paper extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to non-concurrent active and passive measurements from WindSat/AMSR2 and the Soil Moisture Active Passive radar, which was active from late April until mid-July on 2015. A time-series of L-band radar backscatter observations was used to populate an under-determined matrix equation whose optimal solution was derived via a bounded linear least squares estimator, and whose bounds were derived from a time-series of radiometer-derived soil moisture estimates (taken by either WindSat or AMSR2). Surface soil moisture estimates are compared with <italic>in-situ</italic> measurement probes, which were treated as ground truth. Error statistics and time-series results for the validation sites are presented here and conclusions derived therefrom. The overall RMSE and un-biased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 <inline-formula><tex-math notation="LaTeX">$\mathbf{m}^{3}/\mathbf{m}^{3}$</tex-math></inline-formula> and 0.067 <inline-formula><tex-math notation="LaTeX">$\mathbf{m}^{3}/\mathbf{m}^{3}$</tex-math></inline-formula> respectively, when using WindSat to constrain the algorithm. When using AMSR2 to constrain the algorithm, the RMSE and un-biased RMSE were 0.093 <inline-formula><tex-math notation="LaTeX">$\mathbf{m}^{3}/\mathbf{m}^{3}$</tex-math></inline-formula> and 0.090 <inline-formula><tex-math notation="LaTeX">$\mathbf{m}^{3}/\mathbf{m}^{3}$</tex-math></inline-formula> respectively.