Mapping and Modeling of Soil Salinity Using WorldView-2 Data and EM38-KM2 in an Arid Region of the Keriya River, China

Abstract

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.

Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape

“…In addition, the spatiotemporal dynamic nature of soil salinity makes it very difficult to use conventional methods for comparisons over large territory [5]. Several authors have examined the advantages of remote sensing methods and sensors for the assessment of soil degradation due to salinity [1, [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Remote sensing methods are relatively easy to apply and reliable in certain conditions.…”

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

“…A combination of the hyper dry arid climate, geomorphological, soil formation conditions, and shallow groundwater levels facilitated the movement of dissolved salts to the land surface. Our research team also did some study on soil salinity detection and maps on the Keriya River site [16,17]. We found that such phenomena caused soil salinization and desertification, especially in the transitional belt between the oasis and the desert.…”

“…The Keriya River is located in the southern part of the Taklamakan Desert in the Xinjiang Uyghur Autonomous Region of China [17,18]. This river originates from the Kunlun Mountains and flows through the Keriya Oasis, and after approximately 700 km, the river ends in the hinterland of the Taklamakan Desert [18].…”

“…(1) WV-2 multispectral data includes multispectral bands and was described in Table 1. The images were geo-rectified to a Universal Transverse Mercator (UTM) coordinate system using the World Geodetic System (WGS) 1984 datum assigned to north UTM zone 44 [17]. (2) Spectral radiometric calibration and atmospheric and Geometric corrections were performed on the WV-2 [21].…”

“…To select the optimal number of factors and to avoid overfitting, we calibrated the model by an iterative leave-one-out cross-validation criterion called the "minimum predicted residual sum of squares". Root mean square error (RMSE) was minimized by iteratively leaving one sample out of the calibration dataset and by calibrating the model from the remaining dataset [17,[25][26][27]].…”