A three-variables cokriging method to estimate bare-surface soil moisture using multi-temporal, VV-polarization synthetic-aperture radar data

A cokriging model using three variables is developed to estimate bare-surface soil moisture content based on multi-temporal synthetic-aperture radar (SAR) data. This model utilizes cross-semivariogram function to take into account spatially varied correlation among multiple variables. Here, five sentinel-1 SAR scenes were acquired on different dates using the interferometric wide-swath (IW) mode and a mean incidence angle of 39.02° to build the backscatter temporal-ratio in VV polarization. This algorithm is generally based on the assumption of contributions of soil moisture and surface roughness to the backscattering coefficient under the given radar configurations. In this study, soil moisture is the target variable, and the surface roughness and backscatter temporal-ratio in VV polarization are the auxiliary variables. A cross-semivariogram relationship is formulated among those three spatial variables; then ordinary cokriging is used, based on that cross-semivariogram formula, to estimate the spatial distribution of bare soil moisture content. The root mean square error (RMSE) of soil-moisture retrieval ranges from 2.62 to 2.66 vol%. The new empirical model described in this paper will provide new insights into the study of soil environments.