An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

Guo, Shanchuan; Bai, Xuyu; Chen, Yu; Zhang, Shao‐Liang; Hou, Huping; Zhu, Qianlin; Du, Peijun

doi:10.3390/rs11030349

Cited by 15 publications

(9 citation statements)

References 64 publications

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“…The physically-based IEM surface scattering model [36], of which various versions have widely been used (e.g., [1][2][3]5,20,49,[59][60][61][62][63][64]), simulates the σ 0 of bare land surfaces as a function of the land surface's geometric and dielectric properties. For more background on the IEM model, readers are referred to Ulaby et al [6].…”

Section: Impact On Soil Moisture Retrieval Accuracymentioning

confidence: 99%

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

Benninga

Velde

2019

Remote Sensing

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The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( σ 0 ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on σ 0 and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb σ 0 were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 σ 0 observations appear to be affected by frozen conditions below an air temperature of 1 ∘ C , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than 1.8 m m of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest σ 0 observations. By spatially averaging the σ 0 observations, the Sentinel-1 radiometric uncertainty improves from 0.85 dB for a surface area of 0.25 h a to 0.30 dB for 10 h a for the VV polarization and from 0.89 dB to 0.36 dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the σ 0 observations are averaged. Deviations in σ 0 were combined with the σ 0 sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from 0.01 m 3 m − 3 up to 0.17 m 3 m − 3 for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the σ 0 disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself.

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Section: Impact On Soil Moisture Retrieval Accuracymentioning

confidence: 99%

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

Benninga

Velde

2019

Remote Sensing

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“…Although Sentinel-1 images have rarely been used as predictor variables for mapping STN, previous studies using these images to monitor vegetation demonstrated the ability of SAR data to capture vegetation information, which can be further applied to map STN because of the relationships in the soil-vegetation system. For example, Guo et al [67] found that the combination of Sentinel-1 and Sentinel-2 obtained the best above-ground biomass monitoring results while Navarro et al [68] used Sentinel-1 to obtain satisfactory soil moisture inversion results on the Loess Plateau of China.…”

Section: Importance Of Predictor Variablesmentioning

confidence: 99%

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

et al. 2019

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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

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“…Soil moisture (SM) is an important state variable that significantly affects the water cycle, ecosystem, and energy exchange between the land and the atmosphere. SM information is important in different fields, such as agriculture, meteorology, hydrology, weather, and evapotranspiration forecasting [1][2][3][4][5][6][7]. In recent decades, in the large-scale domain, the development of remote sensing technology has provided more opportunities.…”

Section: Introductionmentioning

confidence: 99%

“…In the past few decades, synthetic aperture radar (SAR) has been the primary active microwave remote sensing means of monitoring SM [6,21]. However, in vegetation-covered areas, the signal includes direct scattering from plant cover and attenuated backscatter from the ground.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data

Gao

Wang

et al. 2021

Remote Sensing

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Soil moisture (SM) plays a significant part in regional hydrological and meteorological systems throughout Earth. It is considered an indispensable state variable in earth science. The high sensitivity of microwave remote sensing to soil moisture, and its ability to function under all weather conditions at all hours of the day, has led to its wide application in SM retrieval. The aim of this study is to evaluate the ability of ALOS-2 data to estimate SM in areas with high vegetation coverage. Through the water cloud model (WCM), the article simulates the scene coupling between active microwave images and optical data. Subsequently, we use a genetic algorithm to optimize back propagation (GA-BP) neural network technology to retrieve SM. The vegetation descriptors of the WCM, derived from optical images, were the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized multi-band drought index (NMDI). In the vegetation-covered area, 240 field soil samples were collected simultaneously with the ALOS-2 SAR overpass. Soil samples at two depths (0–10 cm, 20–30 cm) were collected at each sampling site. The backscattering of the ALOS-2 with the copolarization was found to be more sensitive to SM than the crosspolarization. In addition, the sensitivity of the soil backscattering coefficient to SM at a depth of 0–10 cm was higher than at a depth of 20–30 cm. At a 0–10 cm depth, the best results were the mean square error (MAE) of 2.248 vol%, the root mean square error (RMSE) of 3.146 vol%, and the mean absolute percentage error (MAPE) of 0.056 vol%, when the vegetation is described as by the NDVI. At a 20–30 cm depth, the best results were an MAE of 2.333 vol%, an RMSE of 2.882 vol%, a MAPE of 0.067 vol%, with the NMDI as the vegetation description. The use of the GA-BP NNs method for SM inversion presented in this paper is novel. Moreover, the results revealed that ALOS-2 data is a valuable source for SM estimation, and ALOS-2 L-band data was sensitive to SM even under vegetation cover.

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An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

Cited by 15 publications

References 64 publications

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data

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