Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Zhang, Yuan; Tan, Kun; Wang, Xue; Chen, Yu

doi:10.3390/rs12142239

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…Soil moisture affects the intensity and absorption features of spectral reflectance, causing the difference in spectral reflectance under different soil texture classes. Band 12 (SWIR) and NDWI that are related to soil moisture are important variables for predicting soil texture in our study [87], [88]. Liao et al [86] found a good positive correlation between the reflectance of SWIR and sand (r = 0.57, p < 0.01), which is inconsistent with ours (see Fig.…”

Section: B Critical Factors Related To Soil Texture Classes Based On ...contrasting

confidence: 90%

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques

Zhou

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Understanding the spatial variability of soil texture classes is essential for agricultural management and environment sustainability. Sentinel-2 data offer valuable vegetation information as proxies for soil properties inference. However, the applications of them in soil texture classification are still limited. This study investigated the usefulness of Sentinel-2 data for predicting soil texture class using an interpretable machine learning (ML) strategy. Specifically, multitemporal Sentinel-2 images were used to get exhaustive vegetation cover information. Basic digital elevation map (DEM) derivatives and stratum were extracted. Three support vector machines with different input parameters (purely DEM derivatives and stratum, purely Sentinel-2, and Sentinel-2 plus DEM derivatives and stratum) were developed. Moreover, in order to improve the transparency in black box ML models, the novel SHapley Additive exPlanations (SHAP) method was applied to interpret the outputs and analyze the importance of individual variables. Results showed that the model with all variables provided desirable performance with overall accuracy of 0.8435, F1-score of 0.835, kappa statistic of 0.7642, precision of 0.8388, recall of 0.8355, and area under the curve of 0.9451. The model with purely Sentinel-2 data performed much better than that with solely DEM derivatives and stratum. The contributions of Sentinel-2 data to explain soil texture class variability were about 17%, 41%, and 28% for sandy, loamy and clayey soils, respectively. The SHAP method visualized the decision process of ML and indicated that elevation, stratum, and red-edge factors were critical variables for predicting soil texture classes. This study offered much-needed insights into the applications of Sentinel-2 data in digital soil mapping and ML-assisted tasks. Index Terms-Machine learning (ML), sentinel-2, shapley additive explanations (SHAP), soil texture classes. I. INTRODUCTION S OIL texture controls soil water holding capacity, permeability, solute movement and aeration [1], and then drives soil physical, chemical, biological, and hydrological processes that are closely related to plant growth and soil erosion [2], [3].

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Section: B Critical Factors Related To Soil Texture Classes Based On ...contrasting

confidence: 90%

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques

Zhou

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…All soil samples ( n = 240) were randomly divided into calibration set ( n = 160) and validation set ( n = 80) (Zhang et al., 2020). The descriptive statistical analysis for clay content and SOM of all samples showed that the clay content ranged from 13.96 to 54.11% with an average of 29.51%, indicating the loam clay is the major soil texture class for current study area (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Chao

Qiao

et al. 2021

Agronomy Journal

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Visible and near-infrared reflectance (Vis-NIR) spectroscopy is considered a promising tool for the estimation of soil properties. Soil clay content and soil organic matter (SOM) are main components affecting soil spectra. Accurate assessment of clay content and SOM is essential before achieving accurate prediction for other soil properties. Selecting the proper spectral transformation technique and optimal calibration method are important processes to improve model performance. In this study, a total of 240 soil samples were collected from the main area of winter wheat (Triticum aestivum L.) fields in the Southwest region of Shanxi province, northern China. Six spectral pre-treatments and three multivariate methods were utilized to realize the estimation of clay content and SOM. Finally, the important spectral wavelengths were identified

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“…Because soil moisture is an important index to evaluate the degree of drought and flood disaster and directly relates to crop growth, it is of great significance to measure soil moisture content (SMC) in real time and accurately for disaster monitoring and guiding farmland production (Yuan et al., 2019). Although the traditional measurement method of soil moisture had high accuracy, it is difficult to be applied on a large scale (Zhang et al., 2020). For the determination of SMC, there are three main categories of remote sensing methods, namely, optical methods, thermal infrared methods, and microwave sensing (Sadeghi et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

Yan

Qiao

Yang

et al. 2023

Soil Science Soc of Amer J

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Soil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC.

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Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Cited by 17 publications

References 43 publications

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Hyperspectral response and monitoring study of soil moisture content based on the optimized spectral index

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