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Article

Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

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College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
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Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
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Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Water 2020, 12(12), 3360; https://doi.org/10.3390/w12123360
Received: 10 September 2020 / Revised: 15 November 2020 / Accepted: 26 November 2020 / Published: 30 November 2020
(This article belongs to the Section Hydrology and Hydrogeology)
The possibility of quantitative inversion of salinized soil moisture content (SMC) from Zhuhai-1 hyperspectral imagery and the application effect of fractional order differentially optimized spectral indices were discussed, which provided new research ideas for improving the accuracy of hyperspectral remote sensing inversion. The hyperspectral data from indoor and Zhuhai-1 remote sensing imagery were resampled to the same spectral scale. The soil hyperspectral data were processed by fractional order differential preprocessing method and optimized spectral indices method, and the Pearson correlation coefficient (PCC/r) analysis was made with SMC data. The sensitive optimized spectral indices were used to establish the ground hyperspectral estimation model, and a variety of modeling methods were used to select the best SMC inversion model. The results were as follows: the maximum one-dimensional r between SMC and the 466–938 nm band was −0.635, the maximum one-dimensional r with the 0.5-order absorbance spectrum was 0.665, and the maximum two-dimensional r with the difference index (DI) calculated by the 0.5-order absorbance spectrum was ±0.72. The maximum three-dimensional r with the triangle vegetation index (TVI) calculated from the 0.5-order absorbance spectrum reached 0.755, which exceeded the one-dimensional r extreme value of 400–2400 nm. The TreeNet gradient boosting machine (TGBM) regression model had the highest modeling accuracy, with a calibration coefficient of determination (R2C) = 0.887, calibration root mean square error (RMSEC) = 2.488%, standard deviation (SD) = 6.733%, and r = 0.942. However, the partial least squares regression (PLSR) model had the strongest predictive ability, with validation coefficient of determination (R2V) = 0.787, validation root mean square error (RMSEV) = 3.247%, and relative prediction deviation (RPD) = 2.071. The variable importance in projection (VIP) method could not only improve model efficiency but also increased model accuracy. R2C of the optimal PLSR model was 0.733, RMSEC was 3.028%, R2V was 0.805, RMSEV was 3.100%, RPD was 1.976, and Akaike information criterion (AIC) was 151.050. The three-band optimized spectral indices with fractional differential pretreatment could to a certain extent break through the limitation of visible near-infrared spectrum in SMC estimation due to the lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery. View Full-Text
Keywords: Zhuhai-1 hyperspectral imagery; salinized soil moisture content; fractional order differential; optimized spectral indices; Ugan–Kuqa Oasis Zhuhai-1 hyperspectral imagery; salinized soil moisture content; fractional order differential; optimized spectral indices; Ugan–Kuqa Oasis
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MDPI and ACS Style

Kahaer, Y.; Tashpolat, N.; Shi, Q.; Liu, S. Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices. Water 2020, 12, 3360. https://doi.org/10.3390/w12123360

AMA Style

Kahaer Y, Tashpolat N, Shi Q, Liu S. Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices. Water. 2020; 12(12):3360. https://doi.org/10.3390/w12123360

Chicago/Turabian Style

Kahaer, Yasenjiang; Tashpolat, Nigara; Shi, Qingdong; Liu, Suhong. 2020. "Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices" Water 12, no. 12: 3360. https://doi.org/10.3390/w12123360

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