Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information
AbstractThe variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface water. However, remote sensors do not measure soil water content directly. Therefore, it is of great importance to establish a SM retrieval model. In this paper, the relationship between SM and diffuse reflectance was first derived using the absorption coefficient and scattering coefficient related to SM. Then, based on Kubelka–Munk (KM) theory, the SM retrieval model using reflectance information was further derived, which is a semi-empirical model with an unknown parameter obtained either from fitting or from experimental measurements. The validity and reliability of the model were confirmed with the validation set. The results showed that the root mean square errors of prediction (RMSEPs) of four soils were generally less than 0.017, while the coefficients of determination (R2s) of four soils were generally more than 0.85, and the ratios of the performance to deviation (RPDs) of four soils were greater than 2.5 (470–2400 nm). Therefore, the model has high prediction accuracy, and can be well applied to the prediction of water content in different sorts of soils. View Full-Text
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Yuan, J.; Wang, X.; Yan, C.-X.; Wang, S.-R.; Ju, X.-P.; Li, Y. Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information. Remote Sens. 2019, 11, 366.
Yuan J, Wang X, Yan C-X, Wang S-R, Ju X-P, Li Y. Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information. Remote Sensing. 2019; 11(3):366.Chicago/Turabian Style
Yuan, Jing; Wang, Xin; Yan, Chang-xiang; Wang, Shu-rong; Ju, Xue-ping; Li, Yi. 2019. "Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information." Remote Sens. 11, no. 3: 366.
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