Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Zhang, Linlin; Meng, Qingyan; Yao, Shun; Wang, Qiao; Zeng, Jiangyuan; Zhao, Shaohua; Ma, Jing

doi:10.3390/s18082675

Cited by 27 publications

(17 citation statements)

References 51 publications

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“…This shows that the LUT inversion under single polarization is feasible to retrieve the soil moisture, consistent with previous results [37,40]. However, the accuracy of the direct or indirect LUT inversion under dual-polarization (m v4 and m v8 ) is unsatisfactory, inconsistent with a previous research by Zhang et al [56]. The possible reason for the inconsistency is that the surface coverage of the study area in the previous research was monotonous, whereas in this study, the surface coverage was complex, and the vegetation types were varied, including a certain proportion of nurseries with evident body scattering effect.…”

Section: Validation and Analysis Of The Individual Soil Moisture Inversion Modelssupporting

confidence: 78%

“…First, Multi-Look processing was applied to make the texture of the original images close to the real condition and to reduce the speckle noise. The enhanced Lee filter with a window size of 5 pixels by 5 pixels was applied to reduce the speckle noise as the filtering and denoising process, providing the best results compared to using the other filters tested in this study [56]. The obtained data were geocoded using digital elevation maps for geometric fine correction.…”

Section: Gf-3 Datamentioning

confidence: 99%

“…As listed in the table, the proportion of sample points in a certain class is similar to the proportion of the class in the total area of the study area, indicating that the sampling is largely representative. The soil moisture measurements were mainly acquired using time-domain reflectometry (TDR) probes for the top 5 cm in all the 94 sample fields, on the basis of the hypothesis that the factors affecting radar signal penetration, such as vertical inhomogeneity of soil moisture content at a depth of 0-5cm, could be ignored [42,56,58]. For each sample field, the probes were placed vertically, and three close-distance measurements (few meters apart) of the near-surface volumetric soil moisture were collected and averaged.…”

Section: In Situ Measurementsmentioning

confidence: 99%

“…Fortunately, the development of remote sensing technology has led to an effective solution to this problem. Published studies confirmed that the VWC has a significant correlation with the vegetation indices obtained from remote sensing datasets [55][56][57][58][59][60]. The vegetation indices often used to estimate the VWC include the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI), difference vegetation index (DVI), and ratio vegetation index (RVI) [61][62][63][64].…”

Section: Vegetation Water Content Estimation Modelmentioning

confidence: 99%

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High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

Han

Wang

et al. 2020

Remote Sensing

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This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

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Section: Validation and Analysis Of The Individual Soil Moisture Inversion Modelssupporting

confidence: 78%

Section: Gf-3 Datamentioning

confidence: 99%

Section: In Situ Measurementsmentioning

confidence: 99%

Section: Vegetation Water Content Estimation Modelmentioning

confidence: 99%

See 2 more Smart Citations

High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

Han

Wang

et al. 2020

Remote Sensing

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“…The most widely used vegetation scattering model is the Water-cloud Model [22,23]. Based on this, several soil moisture retrieval algorithms have been developed and tested for multiple SAR satellites operated at the L/C/X-bands [24], such as ALOS-2 [25,26], Radarsat-2 [27,28], ASAR [29], Sentinel-1 [30,31,32], and TerraSAR-X [33], studies that have used these data to estimate bare soil moisture have achieved promising results. With shorter revisit time, the Sentinel-1 mission is expected to reduce the ill-posed retrieving using the time-series data.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Huang

Ding

Zou

et al. 2019

Sensors

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Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0–10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas.

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Soil Moisture Content Inversion Model on the Basis of Sentinel Multispectral and Radar Satellite Remote Sensing Data

Guo,

Huang,

et al. 2024

J Soil Sci Plant Nutr

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Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Cited by 27 publications

References 51 publications

High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Soil Moisture Content Inversion Model on the Basis of Sentinel Multispectral and Radar Satellite Remote Sensing Data

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