基于差异化光谱指数的盐渍土水分含量预测1——以滨海盐土为例  

Prediction of Saline Soil Moisture Content Based on Differential Spectral Index: A Case Study of Coastal Saline Soil

作　　者：刘娅[1,2] 潘贤章[1] 王昌昆[1] 李燕丽[1,2] 石荣杰[1,2] 李志婷[1,2] 

机构地区：[1]中国科学院土壤环境与污染修复重点实验室(南京土壤研究所),南京210008 [2]中国科学院大学,北京100049

出　　处：《土壤》2016年第2期381-388,共8页Soils

基　　金：国家自然科学基金项目(41071140);中国科学院战略性先导科技专项(XDB15040300);土壤学科领域基础科学数据整合与集成应用项目(XXH12504-1-02)资助

摘　　要：以滨海盐土为研究对象,通过添加不同浓度的盐溶液并模拟蒸发过程,获取不同含水、含盐量的土壤样品,并测定土壤光谱和土壤含水量,分别运用光谱指数法和偏最小二乘回归法(PLSR)对土壤含水量进行预测。结果表明:由2 027 nm和1 878 nm构建的土壤水分差异化光谱指数(NDMI2027,1878)是预测土壤水分的最优指数,且适用于任何等级的盐渍化土壤,其建模集和验证集的预测结果均优于PLSR方法,验证集R2达0.99,RMSE仅为21.84 g/kg,可比较准确地预测盐渍化土壤的含水量。Soil samples with various salt and moisture contents were artificially prepared by adding different amount of NaCl solutions to costal saline soil to simulate the evaporation process. During the evaporation process, soil moisture contents and soil spectra were regularly collected, and then analyzed using spectral indices and partial least squares regression （PLSR） to quantify soil moisture content. The results showed that the differential moisture index derived from the reflectance value of 2 027 nm and 1 878 nm was the best index to predict soil moisture content, and the indexes obtained from NDMI 2027,1878 in both calibration and validation process were slightly better than these from PLSR, with the determined coefficient （R2） of the prediction as high as 0.99. The root mean square error （RMSE） was only 21.84 g/kg, and not affected by the salinity grades. It could be concluded that soil moisture content can be accurately predicted by NDMI 2027,1878 .

关 键 词：滨海盐土 土壤含水量 可见-近红外光谱 光谱指数 预测 

分 类 号：S151.9[农业科学—土壤学]