Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations

The soil moisture changes (<inline-formula><tex-math notation="LaTeX">$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula>) have a significant influence on forestry, hydrology, meteorology, agriculture, and climate change. Interferometric synthetic aperture radar (InSAR), as a potential remote sensing tool for change detection, was relatively less investigated for monitoring this parameter. DInSAR phase (<inline-formula><tex-math notation="LaTeX">${\boldsymbol{\varphi }}$</tex-math></inline-formula>) is sensitive to the changes in soil moisture (<inline-formula><tex-math notation="LaTeX">${{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula>), and thus, can be potentially used for monitoring <inline-formula><tex-math notation="LaTeX">$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula>. In this article, the relations between <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\varphi }}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula> over wheat, canola, corn, soybean, weed, peas, and bare fields were investigated using an empirical regression technique. To this end, dual-polarimetric C-band Sentinel-1A and quad-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) airborne datasets were employed. The regression model showed the coefficient of determination (R²) of 40% to 56% and RMSE of 4.3 vol.% to 6.1 vol.% between the measured and estimated <inline-formula><tex-math notation="LaTeX">$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula> for different crop types when the temporal baseline (<inline-formula><tex-math notation="LaTeX">$\Delta {\boldsymbol{T}}$</tex-math></inline-formula>) was very short. As expected, higher accuracies were obtained using UAVSAR given its very short <inline-formula><tex-math notation="LaTeX">$\Delta {\boldsymbol{T}}$</tex-math></inline-formula> and its longer wavelength with R² of 47% to 59% and RMSE of 4.1 vol.% to 6.7 vol.% for different crop types. However, using the Sentinel-1 data with the long <inline-formula><tex-math notation="LaTeX">$\Delta {\boldsymbol{T}}$</tex-math></inline-formula> and shorter wavelength (5.6 cm), the accuracies of <inline-formula><tex-math notation="LaTeX">${{\bf \Delta }}{{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula> estimations decreased significantly. The results of this study demonstrated that using the <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\varphi }}$</tex-math></inline-formula> information from Sentinel-1 data is a promising approach for monitoring <inline-formula><tex-math notation="LaTeX">${{\bf \Delta }}{{\boldsymbol{M}}_{\boldsymbol{v}}}$</tex-math></inline-formula> at an early growing season or before the crop starts growing, but using L-band SAR data and lower temporal baselines are recommended once the biomass increases.