Validation of the AIEM Through Correlation Length Parameterization at Field Scale Using Radar Imagery in a Semi-Arid Environment

Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.

The aim of this letter is to discuss the influence of radar frequency on the relationship between surface soil moisture and the nature of radar backscatter over bare soils. In an attempt to address this issue, the advanced integral equation model was used to simulate backscatter from soil surfaces with various moisture vertical profiles, for three frequency bands, namely, L, C, and X. In these computations, we investigated the influence of the vertical heterogeneity of soil moisture on the characteristics of the backscattered signals. The influence of radar frequency is clearly demonstrated. A database produced from Envisat ASAR and TerraSAR-X data, which was acquired over bare soils with in situ measurements of moisture content and ground surface roughness, was used to validate the utility of taking the soil moisture heterogeneity into account in the backscatter model.

Soil moisture (SM) is a key parameter for irrigation monitoring, scheduling, and supporting precision agriculture. In this study, we used Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to estimate SM at high spatial resolution (20 m) in the Lower Chenab Canal Command (LCC) area of Punjab, Pakistan. To achieve this, we applied a semiempirical water cloud model (WCM) using both the VV and VH polarizations from SAR data. Additionally, two widely used machine learning (ML) models, random forest (RF) and support vector machines (SVM), were employed to estimate SM at the field scale. To assess model reliability and transferability, reference data from two field sites were split using two approaches: (1) stratified random sampling, with 50% of data from each site used for training and 50% for validation, and (2) spatial splitting, where one site was used solely for training and the other for validation. Results showed that with random splitting, ML models, particularly SVM, outperformed the WCM, with a coefficient of determination (R²) of 0.58, root mean square error (RMSE) of 6.78 Vol.%, and mean absolute error (MAE) of 5.71 Vol.%. Moreover, VV outperformed VH with an RMSE (R 2 ) of 7.37 Vol.% (0.51), compared with 7.69 Vol.% (0.46) when a stratified random split was used. In contrast, the spatial split yielded lower accuracy, as WCM with VH polarization performed best, achieving an R 2 of 0.45 and RMSE (MAE) of 7.82 (6.23) Vol.%. The high-resolution SM estimates offer practical value for optimizing irrigation management, particularly in agricultural areas challenged by limited water availability.

Surface soil moisture estimation over the AMMA Sahelian site in Mali using ENVISAT/ASAR data

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In the present study, two currently available coarse-scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time-invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well-organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset-calibrated time-invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors (RMSE) ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased RMSE between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel-scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time-invariable coefficients when using publicly accessed coarse-scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

Both passive and active microwave sensors have long been used in retrieving the soil moisture, however, only the SAR can provide the high spatial resolution data (10 m~1 km) suitable for watershed scale hydrological studies. In this paper, we took a preliminary study on deriving the catchment scale soil moisture content and their spatial variations in a typical semi-arid rangeland located at the Loess Plateau of China with active microwave remotely sensed data. The Advanced Integral Equation Model (AIEM) was coupled with the Envisat ASAR images by using the simulated surface roughness information retrieved from the multi-angle (15deg a 31deg) ASAR data as constraining condition to estimate the surface soil moisture content within the studied watershed, the Jinhe River Basin. In general, the soil moisture map derived from this study shows high wetness in most part of the basin, which is in agreement with the actual rainy condition in the study region.

Water cloud model (WCM) relates the backscatter coefficient (σo) with soil moisture. The backscatter coefficient includes the backscatter coefficient due to vegetation (σoveg), and the backscatter coefficient due to soil (σosoil). Theσovegof WCM depends upon vegetation characteristics. The present study is aimed to investigate the effect of different vegetation descriptors in estimating soil moisture from WCM. The study is carried out in Solani River catchment of India. Envisat Advanced Synthetic Aperture Radar (ASAR) images of three dates were acquired for the study. The field data, volumetric soil moisture from the upper 0–10 cm soil layer, soil texture, soil surface roughness, leaf area index (LAI), leaf water area index, normalized plant water content and average plant height corresponding to satellite pass dates were collected. Genetic algorithm optimization technique is used to estimate the WCM vegetation parameters. The use of LAI as vegetation descriptor results in minimum root mean square error (RMSE) of 1.77 dB between WCM computed backscatter and Envisat ASAR observed backscatter. Also, use of LAI in WCM as vegetation descriptor results in the least RMSE of 4.19%, between estimated and observed soil moisture for the first field campaign, whereas it was 5.64% for the last field campaign which was undertaken after 35 days of first campaign. It is concluded that LAI can be treated as the best vegetation descriptor in studies retrieving soil moisture and backscatter from microwave remote sensing data. Copyright © 2014 John Wiley & Sons, Ltd.

Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing

Surface soil moisture has a wide application in climate change, agronomy, water resources, and in many other domain of science and engineering. Measurement of soil moisture at high spatial and temporal resolution at regional and global scale is needed for the prediction of flood, drought, planning and management of agricultural productivity to ensure food security. Recent advancement in microwave remote sensing, especially after the launch of Sentinel operational satellites has enabled the scientific community to estimate soil moisture at higher spatial and temporal resolution with greater accuracy. This study evaluates the potential of Sentinel-1A satellite images to estimate soil moisture in a semi-arid region. Exactly at the time when satellite passes over the study area, we have collected soil samples at 37 different locations and measured the soil moisture from 5 cm below the ground surface using ML3 theta probe. We processed the soil samples in laboratory to obtain volumetric soil moisture using the oven dry method. We found soil moisture measured from calibrated theta probe and oven dry method are in good agreement with Root Mean Square Error (RMSE) 0.025 m 3 /m 3 and coefficient of determination (R 2 ) 0.85. We then processed Sentinel-1A images and applied modified Dubois model to calculate relative permittivity of the soil from the backscatter values ( σ ∘ ). The volumetric soil moisture at each pixel is then calculated by applying the universal Topp’s model. Finally, we masked the pixels whose Normalised Difference Vegetation Index (NDVI) value is greater than 0.4 to generate soil moisture map as per the Dubois NDVI criterion. Our modelled soil moisture accord with the measured values with RMSE = 0.035 and R 2 = 0.75. We found a small bias in the modelled soil moisture ( 0.02 m 3 / m 3 ). However, this has reduced significantly ( 0.001 m 3 / m 3 ) after applying a bias correction based on Cumulative Distribution Function (CDF) matching. Our approach provides a first-order estimate of soil moisture from Sentinel-1A images in sparsely vegetated agricultural land.

Weather stations often provide key information related to soil moisture; temperature and evaporation are used by farmers to decide farm operations of nearby agricultural fields. However, the site conditions at the weather stations where data are recorded may not be similar with these nearby fields. The objective of this study was to determine the level of discrepancies in surface soil moisture between weather stations and nearby agricultural fields based on (i) the soil texture, crop residue cover, crop type, growth stages and (ii) temporal dependency of soil moisture to recent rainfall and evaporation rates. Soil moisture from 25 weather stations in the North Dakota Agricultural Weather Network (NDAWN) and 75 nearby fields were measured biweekly during the 2019 growing season in Red River Valley. Field characteristics including soil texture, crop residue cover, crop type, and growth stages along with rainfall and PET were collected during the study period. The regression analysis between surface soil moisture at weather station and nearby field showed higher values for corn at V10 stage (r2 = 0.92) and for wheat at flowering stage (r2 = 0.68) and opposite was observed with soybean. We found the regression coefficient of soil moisture with 4-d cumulative rainfall slightly increased to 0.51 with an increase in percent residue cover resulting in a decreased root mean square error (RMSE) to 0.063 m3·m−3. In general, we observed that surface soil moisture at weather stations could reasonably predict moisture in nearby agricultural fields considering crop type, soil type, weather, and distance from weather station.

Surface and profile soil moisture spatio-temporal analysis during an excessive rainfall period in the Southern Great Plains, USA

Soil moisture is vital for studying hydrological processes, vegetation growth, and climate change. Recently, Global Navigation Satellite System-Reflectometry (GNSS-R) technology has shown great potential for soil moisture monitoring. However, most existing GNSS-R algorithms rely empirically on multiple land surface parameters, which increases model complexity and the risk of overfitting. This study investigates underlying surface influencing factors for soil moisture retrieval and developes a new soil moisture retrieval with considering the factors that influence the relationship between GNSS-R measurements and surface soil moisture, using Cyclone Global Navigation Satellite System (CYGNSS) data in the middle and lower reaches of the Yangtze River (23.5°N-35°N, 110°E-123°E). Monthly CYGNSS reflectivity and SMAP soil moisture data from 2019 to 2024 are analyzed with consideration of influencing factors such as elevation, slope, biomass, land use, tree height, and Normalized Difference Vegetation Index (NDVI). Results show the following key findings: (1) Reflectivity exhibits a significant correlation with soil moisture, with 84.05% displaying a positive correlation. (2) Over the six-year period from 2019 to 2024, the correlation between SMAP soil moisture and CYGNSS reflectivity shows clear seasonal variability, with lower correlations in spring and autumn and higher correlations in summer and winter, peaking in summer. (3) Geodetector analysis revealed that land use had the most significant effect on soil moisture in the single-factor analysis, while biomass and reflectivity had the strongest interaction in the two-factor analysis, characterized by nonlinear enhancement. (4) A retrieval scheme was designed based on the influencing factors, and its accuracy was validated using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm. The root mean square error (RMSE) of the soil moisture retrieval using all selected features was 0.0526 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 major feature group achieved an RMSE of 0.0527 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>, while the minor feature group yielded an RMSE of 0.0611 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>. Spatial RMSE analysis further demonstrated the effectiveness of Geodetector in guiding feature selection. The retrieval results were further validated using the SSM V2.0 high-resolution soil moisture product, which has been extensively evaluated against in situ measurements, demonstrating strong consistency and confirming the robustness and practical applicability of the proposed method.

The Doppler frequency signature of a man walking into the forest is presented in this paper. The results are based on measured data that have been collected by a bistatic radar working in continuous wave (CW) in the UHF-band. VV and HH polarizations have been employed for our study. The spectrograms have been generated at 1 GHz and 435 MHz by using a Short-Time Fourier Transform (STFT). The purpose of the analysis is to highlight the impact of both the chosen frequencies and the VV and HH polarizations on the Doppler spectrum of a target moving in a cluttered environment. The Doppler signal has been detected at both used frequencies, but it has appeared strongly fragmented by using the VV polarization nevertheless. The HH polarization instead has proved to be more efficient in detecting the Doppler return of the subject moving around the obstructing trees.