Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils

作     者：Z. E. MASHIMBYE M. A. CHO J. P. NELL W. P. DE CLERCQ A. VAN NIEKERK D. P. TURNER Z. E. MASHIMBYE;M. A. CHO;J. P.NELL;W. P. DE CLERCQ;A. VAN NIEKERK;D. P.TURNER

作者机构：Department of Soil Science Stellenbosch University Private Bag X1 Matieland 7602 (South Africa) Agricultural Research Council-Institute for Soil Climate and Water Private Bag X79 Pretoria 0001 (South Africa) Department of Geography and Environmental Studies Stellenbosch University Private Bag X1 Matieland 7602 (South Africa) Council for Scientific and Industrial Research Natural Resources and the Environment P.O. Box 395 Pretoria 0001 (South Africa) School of Environmental Science University of Kwazulu-Natal Westville Campus Westville 3630 (South Africa) 

出 版 物：《Pedosphere》 (土壤圈（英文版）)

年 卷 期：2012年第22卷第5期

页      面：640-649页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 090301[农学-土壤学] 

基　　金：Project supported by the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW) of South Africa (No.GW51/072) the National Research Foundation (NRF) of South Africa (No.GW 51/083/01) the Water Research Commission (WRC)of South Africa (No.K5/1849) 

主　　题：electrical conductivity land degradation partial least squares regression salinity index spectral reflectance 

摘      要：Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.