Application of a Change Detection Soil Moisture Retrieval Algorithm to Combined, Semiconcurrent Radiometer, and Radar Observations

Abstract

This article extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to nonconcurrent active and passive measurements from WindSat/AMSR2 and the Soil Moisture Active Passive radar, which was active from late April until mid-July of 2015. A time series of L-band radar backscatter observations was used to populate an underdetermined 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 in-situ 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 unbiased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.067 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively, when using WindSat to constrain the algorithm. When using AMSR2 to constrain the algorithm, the RMSE and unbiased RMSE were 0.093 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.090 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively.

Keywords

• Change detection
• data fusion
• hydrology
• radar
• radiometer
• remote sensing