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

The soil moisture changes (
$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$
) 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 (
${\boldsymbol{\varphi }}$
) is sensitive to the changes in soil moisture (
${{\boldsymbol{M}}_{\boldsymbol{v}}}$
), and thus, can be potentially used for monitoring
$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$
. In this article, the relations between
${\boldsymbol{\varphi }}$
and
$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$
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 (R
²
) of 40% to 56% and RMSE of 4.3 vol.% to 6.1 vol.% between the measured and estimated
$\Delta {{\boldsymbol{M}}_{\boldsymbol{v}}}$
for different crop types when the temporal baseline (
$\Delta {\boldsymbol{T}}$
) was very short. As expected, higher accuracies were obtained using UAVSAR given its very short
$\Delta {\boldsymbol{T}}$
and its longer wavelength with R
²
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
$\Delta {\boldsymbol{T}}$
and shorter wavelength (5.6 cm), the accuracies of
${{\bf \Delta }}{{\boldsymbol{M}}_{\boldsymbol{v}}}$
estimations decreased significantly. The results of this study demonstrated that using the
${\boldsymbol{\varphi }}$
information from Sentinel-1 data is a promising approach for monitoring
${{\bf \Delta }}{{\boldsymbol{M}}_{\boldsymbol{v}}}$
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.