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Open AccessArticle

Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information

by 1,2, 1,2, 1,3,*, 1, 1,2 and 1,2
1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2
University of the Chinese Academy of Sciences, Beijing 100049, China
3
Center of Materials Science and Optoelectrics Engineering, University of Chinese Academy of Science, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 366; https://doi.org/10.3390/rs11030366
Received: 26 December 2018 / Revised: 31 January 2019 / Accepted: 7 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
The 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
Keywords: hyperspectral remote sensing; soil moisture retrieval model; reflectance; semi-empirical model hyperspectral remote sensing; soil moisture retrieval model; reflectance; semi-empirical model
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MDPI and ACS Style

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. https://doi.org/10.3390/rs11030366

AMA Style

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. https://doi.org/10.3390/rs11030366

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. https://doi.org/10.3390/rs11030366

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