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

• DOI: 10.14358/pers.84.1.43
• Publication Date: 2018-02-22T04:39:06Z
• Authors: Nijat Kasim; Tashpolat Tiyip; Abdugheni Abliz; Ilyas Nurmemet; Rukeya Sawut; Balati Maihemuti
• Keywords: Dryland salinity

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