The goal of this work is to assess the impact of polarimetric SAR decompositions for soil moisture retrieval, and identify the decomposition that performs best for varying vegetation covers and soil conditions. Seven polarimetric decompositions are applied to three L-band radar time-series to evaluate their relative performances for future inclusion within a soil moisture retrieval scheme. Three agricultural sites with different soil and vegetation characteristics are selected across a latitudinal gradient in America. Two time-series of quad-polarimetric data collected by the NASA/JPL UAVSAR airborne instrument are considered for the first two sites, while quad-polarimetric images acquired by the SAOCOM-1A mission are examined for the third site. We extract a set of radar polarimetric descriptors, including the backscattering coefficients, to analyze their sensitivity to soil moisture and vegetation through correlation analysis. We also apply a simple linear regression model to each crop type and site for estimating soil moisture (or Soil Water Index) by alternatively considering a combination of the decomposition powers and of the total backscattering coefficients ( ${{\bm{\gamma }}^0},\ {{\bm{\sigma }}^0}$ ). The linear regression analysis shows that the estimates are generally comparable in terms of linear correlation and root mean square error. Results also reveal that the sensitivity of polarimetric decomposition descriptors to soil moisture and vegetation parameters depend both on crop type and area of interest, without significant differences among the various decompositions tested in this study.

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop.

This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman-Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the Root Mean Square Error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in situ data, shows a significant improvement: the calibrated GFD powers leads to an increased linear correlation (0.32 to 0.57), while the RMSE is reduced (0.096 to 0.055 m³/m³).

The new Copernicus Radar Observing System for Europe in L-band (ROSE-L), expected to work in synergy with the C-band Sentinel-1 mission, will create a multi-platform Synthetic Aperture Radar (SAR) facility acquiring data in a systematic and coordinated way. This paper investigates the performance of a novel soil moisture retrieval scheme, extending the capability of a previously proposed multi-temporal and multi-polarization algorithm to the case of multi-frequency SAR data. It relies on a Bayesian statistical criterion to invert a forward electromagnetic model based on the hypothesis that soil moisture can change abruptly, whereas soil roughness remains stable over time. The algorithm is applied to simulated data to compare two possible operational scenarios of ROSE-L and Sentinel-1 observations: L-band and C-band coincident (LC) or alternate (L-C) acquisitions. The case of single frequency (L or C) data is also considered in the analysis. Additionally, quad-polarization (VV, VH, HH) and dual-polarization (VV, VH) data for ROSE-L are compared when combined with dual-polarization (VV, VH) data for Sentinel-1. The simulated multi-polarization C-band and L-band SAR data are generated considering time variant scenarios of bare soil and crop covered fields. The algorithm is also tested on a time-series of non-coincident L-band SAOCOM-1A and C-band Sentinel-1A data to evaluate the improvements of the soil moisture retrieval against in-situ data when the two frequencies are merged in the multi-temporal scheme. For the simulated case, results for bare soils show that the alternate configuration reaches a retrieval accuracy higher than that of single frequency, with an average percentage improvement in RMSE of approximately 18% compared to single C-band and 5% compared to single L-band. In many cases, it approaches the performance of the coincident acquisitions, maintaining a key advantage in terms of revisit time. The experiment on real data further confirms the advantage of alternating the acquisitions from the two frequency bands when exploited within a multi-temporal framework.