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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 σ H V 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 ( ϕ H H − V V ) and the co-polarized backscattering coefficient σ H H 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. [ABSTRACT FROM AUTHOR]

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless soil moisture products is challenging due to the inability of optical bands to penetrate clouds and the coarse spatiotemporal resolution of microwave and reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on the triple collocation (TC) method with an explicit physical mechanism, which was dedicated to generating seamless 1 km daily soil moisture products. Current merging techniques based on the TC method often lack seamless daily optical data input. To remedy this deficiency, our study performed a spatiotemporal reconstruction on MODIS LST and NDVI, and retrieved seamless daily optical soil moisture products. Then, the optical-derived sm1, microwave-retrieved sm2 (ESA CCI combined), and reanalysis sm3 (CLDAS) were matched by the cumulative distribution function (CDF) method to eliminate bias, and their weights were determined by the TC method. Finally, the least squares algorithm and the significance judgment were adopted to complete the merging. Although the CLDAS soil moisture presented anomalies over several stations, our proposed method can detect and reduce this impact by minimizing its weight, which shows the robustness of the method. This framework was implemented in the Naqu region, and the results showed that the merged products captured the temporal variability of the SM and depicted spatial information in detail; the validation with the in situ measurement obtained an average ubRMSE of 0.046 m³/m³. Additionally, this framework is transferrable to any area with measured sites for better agricultural and hydrological applications. [ABSTRACT FROM AUTHOR]

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 (ρHHVV) and phase difference (φHHVV) 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), ρHHHV_30°, ρVVHV_30°, φHHHV_30° and φHHVV_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 φHHHV, φVVHV, and φHHVV 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. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract This study proposes a two-component (surface and volume) C-band polarimetric decomposition to retrieve soil moisture over agricultural fields covered by different crop types. RADARSAT-2 data analysis shows that vegetation attenuation on the surface scattering component is stronger for the narrow-leaf (wheat) than broad-leaf crops (canola, corn and soybean). Thus, the vegetation attenuation factor is integrated into the proposed C-band polarimetric decomposition to better account for soil contribution over the whole crop phenological season. After removing the volume scattering component from the full coherency matrix, the surface scattering component was simulated by the Oh soil scattering model, instead of conventional Bragg or X-Bragg model, which is not physically valid at C-band due to limited roughness constraint condition. The proposed retrieval algorithm was applied to the RADARSAT-2 time series acquired during the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). Results indicate that during the crop growth, the volume scattering power of wheat shows weak temporal variation, while it increases for corn, soybean and canola. The sensitivity of the ground scattering component to soil moisture is enhanced due to the removal of the volume scattering from the full signature. The retrieved soil moisture was validated using the soil moisture ground measurements during the SMAPVEX12. The validations indicate correlation coefficients from 0.63 to 0.76, and RMSEs from 0.058 to 0.074 m3/m3 for the entire phenological period of SMAPVEX12 campaign. The negligible dihedral scattering component (1.6–9.2%) at C-band greatly reduces the complexity of the soil moisture retrieval from the ground component. Highlights • A two-component C-band model-based polarimetric decomposition is developed. • Surface scattering component is simulated by Oh model instead of X-Bragg model. • A vegetation attenuation factor is included in the surface scattering component. • Soil moisture is retrieved for the entire phenological stages during SMAPVEX12. [ABSTRACT FROM AUTHOR]

This study investigates and compares the potential of three model-based polarimetric decompositions, namely those developed by Freeman-Durden (1998), Hajnsek et al. (2009) and An et al. (2010), for soil moisture retrieval over agricultural fields covered by several crops. The volume scattering component was first removed from the full coherency matrix. Then, in order to reduce the effect of the incidence angle on the retrieval results, a normalization process at a reference incidence angle was conducted for the first time, on the dominant surface or dihedral scattering component from which the soil moisture was retrieved. The time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the ground measurements of soil and vegetation characteristics collected during the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012 (SMAPVEX12) were used to compare the three decomposition methods with respect to the scattering mechanisms and the soil moisture retrieval performances. The results show that the performance of each decomposition method for soil moisture retrieval depends on the crop types and the crop phenological stages. Indeed, Freeman-Durden model provided the best results for corn and wheat, Hajnsek decomposition performed well for canola, while better results were obtained for soybean using An decomposition. At the early growth stage, both the surface and dihedral scattering components contributed to retrieve the soil moisture, while at a later crop development, the surface scattering component is almost the only scattering mechanism from which soil moisture was retrieved. Thus, the best performance for soil moisture retrieval was obtained a) at the early crop development stage from Hajnsek decomposition which better integrated the dihedral component and b) at a later growth stage from An decomposition which enhanced the surface scattering. Finally, an overall soil moisture underestimation with RMSE of 0.06–0.11 m 3 /m 3 was observed from the three decompositions, and the highest retrieval rate of 33%–39% was obtained from An decomposition as a result of the enhanced surface scattering. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the potential of multiangular SAR data at C-band for soil parameters’ discrimination over bare agricultural fields. In order to exploit the incidence angle diversity to enhance the soil parameter retrievals, the conventional multiangular roughness descriptor \DeltaHH formulated originally as the backscattering difference between two specific incidence angles is adapted to take into account a general incidence angle effect. Moreover, a new angular coherence SAR descriptor \gammaHH is proposed by using a pair of low and high incidence angle SAR data. The adapted \DeltaHH and the proposed \gammaHH are applied to the RADARSAT-2 data with three different incidence angles. The results indicate that the proposed \gammaHH is more sensitive to surface roughness than the adapted \DeltaHH. Thus, the \gammaHH is selected to retrieve the surface roughness, and then the retrieved surface roughness is substituted into a low incidence angle data to retrieve the soil moisture. The RMSE of 6.1–8.5 \textm^3/\textm^3 is obtained for soil moisture retrieval. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

This paper investigates the soil moisture retrievals over Tibetan Plateau using multi-temporal ALOS2-PALSAR2 data acquired in ScanSAR mode, in contrary to the commonly used StripMap SAR imaging mode. Considering the dual-polarimetry with limited observables, the surface roughness parameters such as the RMS height and auto-correlation length are first estimated by optimizing the semi-empirical Oh2004 model applied to the ALOS2 data acquired under bare soil condition. The vegetation water content corresponding to each ALOS2 acquisition time is derived from an empirical model applied to the temporally interpolated MODIS NDVI data. Then, the obtained roughness and vegetation optical depth are substituted into the Water Cloud Model which we modified by adding a double-bounce component according to the L-band scattering processes to retrieve the effective scattering albedo and soil moisture. The results show that the optimized surface RMS height and the associated slope are negatively related to a dual-angular radar index ΔHH, indicating the feasibility to optimize the relatively stable surface roughness parameters before retrieving the soil moisture dynamics. The obtained vegetation optical depth which is cross-validated against a normalized cross-polarized radar descriptor indicates a significant increase from May to August, in response to the vegetation phenological growth. Furthermore, the time-variable scattering albedo is less than 0.08, and slightly increases with the vegetation development. By accounting for the double-bounce component, the retrieved soil moisture better agrees with the ground measurements with R = 0.89 and RMSE = 0.058 m3/m3, but exhibits an overestimation issue for the soil moisture higher than 0.35 m3/m3 due to the saturation of SAR signal and the relatively strong vegetation cover. A positive correspondence (R = 0.81) between the retrieved soil moisture and the interpolated NDVI was found, verifying their close coupling in the soil-vegetation system. This study deepens the insights in the potential integration between the optical and L-band microwave observations to retrieve soil moisture over vegetated area. • ALOS2 data acquired at ScanSAR mode are investigated to retrieve soil moisture. • Microwave and optical data are coupled in the retrieval process. • Water Cloud Model is calibrated by adding a double-bounce scattering component. • Three-component is better than two-component decomposition for soil moisture retrieval. [ABSTRACT FROM AUTHOR]

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 (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 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. [ABSTRACT FROM AUTHOR]

The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product's coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m3/m3 over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies. [ABSTRACT FROM AUTHOR]

• 36 km SMAP brightness temperature (TB) are disaggregated via GWR model. • Disaggregated TB are converted into 1 km soil moisture based on RTM model. • 1 km SM is validated and compared with in-situ measurements and other SM products. Passive microwave soil moisture (SM) products hold tremendous potential for both scientific research and practical applications. However, due to the limitations imposed by brightness temperature (TB), passive microwave SM suffer from lower spatial resolution, which significantly restricts their application in regional scale like crop growth monitoring, water resource management, runoff forecasting, and others. To address this challenge, this study proposed a feasible methodology to disaggregate 36 km Soil Moisture Active Passive (SMAP) SM in the ShanDian River Basin via coupling geographically weighted regression (GWR) and radiative transfer model (RTM). Firstly, we employed GWR to disaggregate 36 km SMAP TB to 1 km via MODIS LST, NDVI and DEM data. Subsequently, in accordance with the microwave radiative transfer model, the disaggregated TB was converted to 1 km SM using a single-channel algorithm (SCA) in V polarization. The disaggregated SM, derived from the disaggregated TB, was validated against in-situ SM from the SMN-SDR network and compared with various SMAP SM products at different spatial resolutions. The results show the disaggregated SM effectively inherits the performance of SMAP official products, while offering enhanced spatial resolution and enabling more accurate applications. This study effectively enhances the performance of coarse-resolution passive microwave brightness temperature data at fine scales. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset. • A multi-temporal dual-channel algorithm is proposed for soil moisture retrieval. • The incidence angle normalization enables the use of Sentinel-1 multi-track data. • Soil roughness and soil unfrozen water content are initially retrieved for frozen period. • Sentinel-1 can map 1-km soil moisture effectively at a local to regional scale. [ABSTRACT FROM AUTHOR]