A PolSAR rotation model for crop classification and soil moisture retrieval in complex agricultural environments

As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission has been developed for assessing the accuracy of soil moisture retrievals from passive L-band remote sensing. The implementation of the OSSE is based on the following: a 1-km land surface model over the Red-Arkansas River Basin, a forward microwave emission model to simulate the radiometer observations, a realistic orbital and sensor model to resample the measurements mimicking Aquarius operation, and an inverse soil moisture retrieval model. The simulation implements a zero-order radiative transfer model. Retrieval is performed by direct inversion of the forward model. The Aquarius OSSE attempts to capture the influence of various error sources, such as land surface heterogeneity, instrument noise, and retrieval ancillary parameter uncertainty, all on the accuracy of Aquarius surface soil moisture retrievals. In order to assess the impact of these error sources on the estimated volumetric soil moisture, a quantitative error analysis is performed by comparison of footprint-scale synthetic soil moisture with “true” soil moisture fields obtained from the direct aggregation of the original 1-km soil moisture field input to the forward model. Results show that, in heavily vegetated areas, soil moisture retrievals have a positive bias that can be suppressed with an alternative aggregation strategy for ancillary parameter vegetation water content (VWC). Retrieval accuracy was also evaluated when adding errors to 1-km VWC (which are intended to account for errors in VWC derived from remote sensing data). For soil moisture retrieval root-mean-square error on the order of 0.05 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , the error in VWC should be less than 12%.

This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and $R^{2} $ of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and $R^{2} $ of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., $kh_{RMS} , of the AIEM narrows.

Soybean, a pivotal global source of oil and protein, exhibits heightened sensitivity to soil moisture conditions throughout its growth cycle. Accurate monitoring of soil moisture (SM) in soybean fields during the growing season is indispensable for optimizing yields and forecasting sustainable agricultural practices. Leveraging advancements in remote sensing technology, passive microwave soil moisture retrieval has emerged as a crucial tool for large-scale precision agriculture and enduring environmental monitoring. However, challenges in the L-band Microwave Emission of the Biosphere (L-MEB) model, particularly in the computation of vegetation transmissivity, may compromise the accuracy of soil moisture retrieval. In this study, we improved the Beer-Lambert law to more accurately quantify the attenuation effect of the vegetation layer on microwave signals, aiming to ameliorate the inherent limitations in the L-MEB model. The proposed soil moisture retrieval method, primarily validated in soybean fields, was also subjected to supplementary experiments in canola and wheat fields to further assess its effectiveness and generalizability. The proposed method integrates passive microwave and optical data, demonstrating a substantial improvement in accuracy. Experimental results reveal that our enhanced method significantly outperforms the L-MEB model in soybean fields: Pearson correlation coefficients of soil moisture, derived using vegetation water content and leaf area index, are 0.712 and 0.692 respectively. Furthermore, root mean square errors have decreased to 0.056m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and 0.050 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, a reduction of 39.78% and 19.35%, respectively. In canola and wheat fields, the method exhibited an approximate 10% enhancement in retrieval accuracy. This advancement not only furnishes novel technical support for water management in soybean cultivation but also contributes theoretical and technical insights to the domain of passive microwave soil moisture retrieval. Index Terms-L-MEB model, passive microwave, soil moisture retrieval, vegetation transmissivity.

Soil moisture retrieval is often confounded by the influence of vegetation and surface roughness on the backscattered radar signal in vegetated areas. In this study, a semi-empirical methodology is proposed to retrieve soil moisture in prairie areas. The effect of vegetation is eliminated by the ratio vegetation method and water cloud model (WCM), respectively. The conditions of vegetation are characterized by leaf area index (LAI), vegetation water content (VWC), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI), respectively. To remove the dependence on surface roughness, the dielectric constant is explicitly expressed as the function of co-polarization backscattering coefficients and sensor parameters based on the Dubois model. The ground measurements and satellite data collected from the Ruoergai and Wutumeiren prairies of China allow for validating the feasibility and effectiveness of the proposed methodology. From the perspective of soil moisture retrieval accuracy, the ratio vegetation method performs better than WCM. In the Ruoergai prairie, the best soil moisture retrieval result is obtained when EVI is used, with correlation coefficient (r) and root mean square error (RMSE) of 0.87 and 3.50 vol.%, respectively. While in the Wutumeiren prairie, the lowest retrieval error is obtained when LAI is used, with r and RMSE values of 0.79 and 5.73 vol.%, respectively. These results demonstrate that the Dubois model has a potential for enhancing soil moisture retrieval in prairie areas using synthetic aperture radar (SAR) and optical data.

Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer. The algorithm consists of two sub-algorithms: a surface emission model and a soil moisture retrieval model. In analyses of the advanced integral equation model (AIEM) simulated database, the surface emission model was developed to diminish the effects of surface roughness using dual-polarization surface reflectivity. The soil moisture retrieval model, which was calibrated using the Dobson simulated database, is based on the relationship between the adjusted real refractive index N r and the volumetric soil moisture. Soil moisture can be determined via a numerical solution that uses several freely available input parameters: dual-polarization microwave brightness temperature, surface temperature, and the contents of sand and clay. The results showed good agreement with the input soil moisture values simulated by the AIEM model, with root mean square errors (RMSEs) lower than 3% at all incidence angles. The algorithm was then verified based on data from the four-year L-band experiments conducted at Beltsville Agricultural Research Center (BARC) test sites, achieving RMSEs of 4.3% and 3.4% at 40° and 50°, respectively. These results indicate that the simplified algorithm proposed in this paper shows a very good accuracy in soil moisture retrieval. Additionally, the algorithm exhibits a better performance for the large incidence angle radiometers in L-band such as those produced by the Soil Moisture Active and Passive (SMAP).

In passive microwave remote sensing, the estimation of the surface roughness parameter is a significant obstacle for soil moisture (SM) retrieval. For a given SM content, the geometric soil surface roughness has been shown to have a large impact on the surface emission at L-band frequency, which affects the SM retrieval success when using the information observed from the radiometer and is represented through the so-called the surface roughness parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{R}$ </tex-math></inline-formula> ). Moreover, no previous study has examined the effect of this factor in the context of road construction, where the geometric soil surface roughness is affected by the compaction process, resulting in a substantial change in roughness before and after compaction. Accordingly, a series of experiments at various compaction levels and SM contents was performed for a sand subgrade material in order to identify their effects on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{R}$ </tex-math></inline-formula> . The soil brightness temperature (TB) was measured using an L-band radiometer at different incidence angles and a laser profiler was used to measure the surface roughness standard deviation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma$ </tex-math></inline-formula> ) before and after compaction. The results of this article have demonstrated that the incidence angle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta$ </tex-math></inline-formula> ) and SM both affect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{R}$ </tex-math></inline-formula> and its relation to the geometric soil surface roughness. Importantly, these factors are not accounted for by existing models. Consequently, a modified surface roughness parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{R}$ </tex-math></inline-formula> ) model, based on the traditional Choudhury model, was developed to include the contribution of these two factors, and its impact on the accuracy of SM retrieval results tested. Specifically, it was shown that it is possible to obtain SM retrieval results with an accuracy of 0.04 cm3/cm3 at almost all incidence angles using either dual-polarization [both horizontal (H) and vertical polarization (V)] or only vertical polarization observations. The modified surface roughness parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{R}$ </tex-math></inline-formula> ) model has improved the performance of the SM retrieval model to achieve an accuracy of 0.04 cm3/cm3, whereas the traditional Choudhury model achieved an accuracy of only 0.05 cm3/cm3.

Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} with the improved method is decreased by 9.1% using SMAP SM as reference with the SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} = 0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} and SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} values with the improved method are 0.80 and 0.064 cm3/cm3, respectively. The average R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R$$\\end{document} of the chosen sites is increased by 22.7%, and the average SRMSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_{\\mathrm{RMSE}}$$\\end{document} is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this paper, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 km and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient (R) and root mean square error (RMSE) of the 36 km grid are 0.50 and 0.057 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , respectively. Such performances are better than the traditional method. Moreover, the method proposed in this paper preserves more grids. Take the 3 km spatial resolution for example, it preserves 2.2 fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this paper is 16.3% (8.2% for the traditional method). In the in-situ validation, the overall improvement of RMSE is 19.4% (-1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body removal method proposed in this paper can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

Marked by high efficiency, multiple channels and low cost, meteorological satellites have the applications extending from the use only in a meteorological context to quite a few scopes of research and operation. This paper introduces the establishment of statistical models between altitudes of stations at different latitudes/longitudes and corresponding NOAA/AVHRR remote sensing data to obtain the terrain altitudes in Ningxia and its surroundings whereupon the heights of different parts of Ningxia are found, with the results applied to the retrieval of spring soil moisture, thereby leading to the construction of soil moisture retrieval models from satellite data and altitudes for real-time monitoring soil moisture. Results show that it is successful to make verification of satellite-data calculated altitudes against measurements, which, when introduced into the soil moisture retrieval models, improve the accuracy to greater extent. On this basis we developed the operational models for remote sensing based spring soil moisture monitoring in the target region that are run in an easy, quick and visual way, thus providing an efficient means of farmland soil moisture/dryness distributions monitoring.

In global navigation satellite system reflectometry (GNSS-R) research, polarization was historically overlooked. However, it has garnered increasing attention in recent years. This article focuses on analyzing the first publicly available dual-polarization, single-frequency dataset from the airborne global navigation satellite system reflectometry instrument (GLORI), collected during the summer of 2021. Based on theoretical analysis, multiple soil moisture retrieval algorithms were implemented. Initially, only the surface reflectivity of LHCP-receiving/RHCP-transmitting (LR) and RHCP-receiving/RHCP-transmitting (RR) polarizations was investigated. As additional surface parameters, such as surface roughness and vegetation, were integrated into the algorithm, the retrieval accuracy, measured by root mean square error (RMSE), improved significantly from approximately 0.07 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to 0.03 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. The retrieval accuracy of RR polarization is slightly better than that of LR polarization. Nevertheless, when both dual polarizations were considered, the retrieval accuracy was comparable to that of using only one polarization. When surface roughness, leaf area index, and incidence angle are taken into account, the soil moisture retrieval accuracy, indicated by RMSE, reaches 0.0344 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>. This clearly demonstrates the great potential of dual polarization in soil moisture estimation. GLORI data is the first publicly available dual-polarization GNSS-R data that encompasses not only coherent and but also noncoherent scattering scattering information. This article further discusses the noncoherent scattering properties of LR and RR polarizations. In the context of coherent scattering, it is found that the scattering properties at LR polarization are stronger than those at RR polarization. Conversely, for noncoherent scattering, the scattering properties at LR polarization are weaker than those at RR polarization for corresponding land surface types. By employing both cohrent and noncoherent scattering properties with the consideratons of LR and RR polarizations, the soil moisture retrieval accuracy indicated by RMSE and correlation coefficients (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i>) will be 0.0316 and 0.9043, respectively. The analysis of dual-polarization data incorporating both coherent and noncoherent scattering mechanisms holds significant potential for advancing soil moisture retrieval accuracy through future data mining efforts, while also informing the design of next-generation polarimetric GNSS-R payloads, such as ESA’s HydroGNSS and China’s GNSS-ReSAR.

Abstract. Using an observing system simulation experiment (OSSE), we investigate the potential soil moisture retrieval capability of the National Aeronautics and Space Administration (NASA) Aquarius radiometer (L-band 1.413 GHz) and scatterometer (L-band, 1.260 GHz). We estimate potential errors in soil moisture retrievals and identify the sources that could cause those errors. The OSSE system includes (i) a land surface model in the NASA Land Information System, (ii) a radiative transfer and backscatter model, (iii) a realistic orbital sampling model, and (iv) an inverse soil moisture retrieval model. We execute the OSSE over a 1000 × 2200 km2 region in the central United States, including the Red and Arkansas river basins. Spatial distributions of soil moisture retrieved from the radiometer and scatterometer are close to the synthetic truth. High root mean square errors (RMSEs) of radiometer retrievals are found over the heavily vegetated regions, while large RMSEs of scatterometer retrievals are scattered over the entire domain. The temporal variations of soil moisture are realistically captured over a sparely vegetated region with correlations 0.98 and 0.63, and RMSEs 1.28% and 8.23% vol/vol for radiometer and scatterometer, respectively. Over the densely vegetated region, soil moisture exhibits larger temporal variation than the truth, leading to correlation 0.70 and 0.67, respectively, and RMSEs 9.49% and 6.09% vol/vol respectively. The domain-averaged correlations and RMSEs suggest that radiometer is more accurate than scatterometer in retrieving soil moisture. The analysis also demonstrates that the accuracy of the retrieved soil moisture is affected by vegetation coverage and spatial aggregation.