This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m³/m³ and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.

This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R²_OLS = 0.981, R²_SVM = 0.943, R²_RF = 0.983) and low RMSE (RMSE_OLS = 0.016 cm³/cm³, RMSE_SVM = 0.047 cm³/cm³, RMSE_RF = 0.016 cm³/cm³). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R²_OLS = 0.376~0.85, R²_SVM = 0.376~0.814, R²_RF = 0.39~0.854; RMSE_OLS = 0.049~0.105 cm³/cm³, RMSE_SVM = 0.073~0.1 cm³/cm³, RMSE_RF = 0.047~0.102 cm³/cm³), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

The model-based polarimetric decomposition under multi-angular condition is refined to estimate soil moisture over agricultural fields covered by different crops from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) time series. The approach allows to disentangle the vegetation and ground scattering components in order to invert the latter for the retrieval of soil moisture. For the vegetation volume separation, the crop orientations were estimated from SAR observations acquired at different incidence angles, and the associated volume scattering component was subtracted from each acquisition individually. Afterward, the soil moisture was retrieved from both ground scattering components (surface, dihedral), using the developed multi-angular cost functions comprised of dominant Bragg surface (β) or Fresnel dihedral (α) scattering parameters. Compared to former soil moisture retrievals from model-based decomposition of multi-angular polarimetric SAR data, the present refined approach that integrated both ground components, surface and dihedral, is independent of the power attenuation and loss during the microwave propagation through the vegetation. In this way, the ambiguity in the dihedral scattering component (most prominent around 45° incidence angle) was overcome, enabling a more robust retrieval methodology by clearly decoupling the soil and vegetation dielectric constants. The proposed multi-angular approach for soil moisture retrieval was validated with respect to the ground measurements conducted during the Soil Moisture Active Passive Validation Experiment in 2012. Due to the increased number of valid dominant surface/dihedral components which are used to retrieve the soil moisture in the multi-angular approach, an overall retrieval rate of 90%, significantly higher than that of the single-angular condition (50%), is obtained. The results indicate an overall retrieval rmse of 0.07–0.09 m3/m3 for the early crop growth stage, and a rmse of 0.09–0.12 m3/m3 for the later crop development until mature stage. However, the retrieval performance is highly dependent on the crop structure and phenological development stages, but the multi-angular rmse range is mostly lower than all single-angular rmses, indicating better quality of the multi-angular inversion than the single-angular one.

This study investigates the potential of L- and C- bands Polarimetric Synthetic Aperture Radar (PolSAR) data to monitor soil moisture over the forested sites of SMAP Validation Experiment 2012 (SMAPVEX12). The optimal backscattering coefficients and polarimetric parameters to characterize the soil moisture were determined based on L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), C-band RADARSAT-2, and ground measurements composed of soil and vegetation parameters collected during SMAPVEX12. Linear and circular backscattering coefficients (σ⁰) and polarimetric parameters such as correlation coefficients (ρ_HHVV) and phase difference (φ_HHVV) between HH and VV, pedestal height (PH), entropy (H), anisotropy (A), α angle, surface (Ps), and double bounce (Pd) powers were used to develop the relationships with soil moisture. The analysis of these relationships shows that over the forested sites of SMAPVEX12: (a) at L-band several optimal backscattering coefficients and polarimetric parameters allow the monitoring of soil moisture, particularly the linear and circular σ⁰ (r = 0.60–0.96), Ps (r = 0.59–0.84), Pd (r = 0.60–0.82), ρ_HHHV_30°, ρ_VVHV_30°, φ_HHHV_30° and φ_HHVV_30° (r = 0.56–0.81). However, compared to the results obtained with σ⁰, there is no added value of the polarimetric parameters for soil moisture retrievals. (b) at C-band, only a few polarimetric parameters φ_HHHV, φ_VVHV, and φ_HHVV are correlated with soil moisture (r = ~0.90). They can contribute to soil moisture retrievals over forested sites when L-band data are not available.

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient ${\sigma }_{HV}^0$; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (${\varphi }_{HH-VV}$) and the co-polarized backscattering coefficient ${\sigma }_{HH}^0$. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m³/m³) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m³/m³, with an explained variance of 44%. The simplified model has the lowest error (0.074 m³/m³) and explains 53.5% of soil moisture variation.