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Remote Sens. 2016, 8(1), 42; doi:10.3390/rs8010042

Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data

1
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 25 November 2015 / Revised: 29 December 2015 / Accepted: 4 January 2016 / Published: 7 January 2016
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Abstract

This research examines the simultaneous retrieval of surface soil moisture and salt concentrations using hyperspectral reflectance data in an arid environment. We conducted laboratory and outdoor field experiments in which we examined three key soil variables: soil moisture, salt and texture (silty loam, clay and silty clay). The soil moisture content models for multiple textures (M_SMC models) were based on selected hyperspectral reflectance data located around 1460, 1900 and 2010 nm and resulted in R2 values higher than 0.933. Meanwhile, the soil salt concentrations were also accurately (R2 > 0.748) modeled (M_SSC models) based on wavebands located at 540, 1740, 2010 and 2350 nm. When the different texture samples were mixed (SL + C + SC models), soil moisture was still accurately retrieved (R2 = 0.937) but the soil salt not as well (R2 = 0.47). After stratifying the samples by retrieved soil moisture levels, the R2 of calibrated M_SSCSMC models for soil salt concentrations improved to 0.951. This two-step method also showed applicability for analyzing soil-salt samples in the field. The M_SSCSMC models resulted in R2 values equal to 0.912 when moisture is lower than 0.15, and R2 values equal to 0.481 when soil moisture is between 0.15 and 0.2. View Full-Text
Keywords: waveband selection; salinity; water; texture; spectroscopy; modeling; stratifying waveband selection; salinity; water; texture; spectroscopy; modeling; stratifying
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xu, C.; Zeng, W.; Huang, J.; Wu, J.; van Leeuwen, W.J. Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data. Remote Sens. 2016, 8, 42.

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