Estimating Soil Salinity in the Yellow River Delta, Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model  

作　　者：SONG Chuangye REN Hongxu HUANG Chong 

机构地区：[1]State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences [2]Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences [3]Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences

出　　处：《Pedosphere》2016年第5期626-635,共10页土壤圈（英文版）

基　　金：financially supported by the National Natural Science Foundation of China (Nos. 41001363 and 41471335);the Ocean Public Welfare Scientific Research Project, China (No. 201305021)

摘　　要：Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model(DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper(TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model(GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion(AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover.Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model （DEM） to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper （TM） images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model （GAM） was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike＇s information criterion （AIC） was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.

关 键 词：Akaike＇s information criterion digital elevation model Landsat TM image soil salt content terrain in indices vegetation cover 

分 类 号：S153[农业科学—土壤学]