Soil salinity is one of the common factors leading to land degradation problems on earth, especially in arid and semiarid regions. There is an urgent need for rapid, accurate and cost-effective monitoring and assessment of soil salinization. Remote Sensing (<small>RS</small>)
and Geographical Information Systems (<small>GIS</small>) are employed as viable technologies for detecting, monitoring, and predicting spatial-temporal patterns of soil salinization. The purpose of this study is to establish partial least squares regression (<small>PLSR</small>)
models that are based on remotely sensed data and field measured electrical conductivity (<small>ECa</small>) and to retrieve soil salinity estimates by constructing an optimal model. First, the soil adjusted vegetation index (<small>SAVI</small>) was calculated based
on WorldView-2 images. Second, a statistical regression method was applied to analyze the correlation between ECa and <small>SAVI</small> under different parameters. The <small>SAVI</small> that was measured as the most stable parameter was an optimum index. Finally,
a <small>PLSR</small> prediction model of soil salinity was established based on the sensitivity bands, the optimum index and ECa. The results of this study are the following: (a) According to the adjusted parameter (L = 100), the <small>SAVI</small> index illustrated
the best correlation with ECa, and ECa was also significantly related to the bands ((Red Edge) Band6, (Near-IR1) Band7 and (Near-IR2) Band8) derived from a World-view-2 image. (b) The results of the <small>PLSR</small> predictive model calibration showed that the model-D performed
best through the sensitivity bands and optimal index, with the highest coefficient of determination (R2 = 0.67) and the smallest root mean square error (<small>RMSE</small>) of 1.19 dS·m-1. The results indicated that the model-D that is constructed
and applied in this paper could provide quantitative information for detecting and monitoring soil salinization in the Keriya Oasis and could also supply examples for the study of soil salinization in arid and semiarid regions with similar environmental conditions.
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.
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