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Open AccessArticle

Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling

1
College of Biology and Geography, Yili Normal University, Yining 835000, China
2
College of Resources and Environmental Sciences, Xinjiang University, Urumqi 830046, China
3
Navigation College, Dalian Maritime University, Dalian 116026, China
4
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2020, 12(3), 880; https://doi.org/10.3390/w12030880
Received: 11 February 2020 / Revised: 7 March 2020 / Accepted: 11 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Focus on the Salinization Issue in the Mediterranean Area)
Soil salinity is one of the major factors causing land degradation and desertification on earth, especially its important damage to farming activities and land-use management in arid and semiarid regions. The salt-affected land is predominant in the Keriya River area of Northwestern China. Then, there is an urgent need for rapid, accurate, and economical monitoring in the salt-affected land. In this study, we used the electrical conductivity (EC) of 353 ground-truth measurements and predictive capability parameters of WorldView-2 (WV-2), such as satellite band reflectance and newly optimum spectral indices (OSI) based on two dimensional and three-dimensional data. The features of spectral bands were extracted and tested, and different new OSI and soil salinity indices using reflectance of wavebands were built, in which spectral data was pre-processed (based on First Derivative (R-FD), Second Derivative (R-SD), Square data (R-SQ), Reciprocal inverse (1/R), and Reciprocal First Derivative (1/R-FD)), utilizing the partial least-squares regression (PLSR) method to construct estimation models and mapping the regional soil-affected land. The results of this study are the following: (a) the new OSI had a higher relevance to EC than one-dimensional data, and (b) the cross-validation of established PLSR models indicated that the β-PLSR model based on the optimal three-band index with different process algorithm performed the best result with R2V = 0.79, Root Mean Square Errors (RMSEV) = 1.51 dS·m−1, and Relative Percent Deviation (RPD) = 2.01 and was used to map the soil salinity over the study site. The results of the study will be helpful for the study of salt-affected land monitoring and evaluation in similar environmental conditions. View Full-Text
Keywords: soil salinization; optimized spectral algorithm; Keriya River; EC; arid region soil salinization; optimized spectral algorithm; Keriya River; EC; arid region
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MDPI and ACS Style

Kasim, N.; Maihemuti, B.; Sawut, R.; Abliz, A.; Dong, C.; Abdumutallip, M. Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling. Water 2020, 12, 880.

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