基于Landsat8的黄河三角洲盐渍化反演  

Soil Salinization Retrieval for the Yellow River Delta Based on Landsat8

作　　者：樊彦国[1] 李潭潭 李祥昌 

机构地区：[1]中国石油大学(华东)地球科学与技术学院,山东青岛266580

出　　处：《山东农业科学》2015年第2期119-124,共6页Shandong Agricultural Sciences

基　　金：山东省科技攻关项目"盐渍化遥感监测及暗管改碱与节水集成技术及其应用"(2008GG10009018)

摘　　要：土壤盐渍化是最常见的土壤退化过程,垦利县位于黄河三角洲核心地带,是土壤盐渍化比较典型的区域。本研究在盐渍土野外调查采样的基础上,依据土壤理化分析和Landsat8卫星光谱数据,选取了相关性以及诊断指数较好的3个波段的反射率作为盐分反演因子,分别建立数理统计模型与BP神经网络盐分反演模型。研究表明:BP神经网络模型的精度明显优于传统多元回归模型,且反演模型更适合高盐度区域土壤盐渍化反演制图,具有较好的应用前景。Soil salinization is one of the most common land degradation processes. Kenli County is loca- ted in the core area of the Yellow River Delta, where the soil salinization is typical. Based on investigating and sampling saline soil in field, and according to the physical and chemical analysis of soil and Landsat8 satellite spectral data, the reflectivities of 3 wave bands with better correlation and diagnostic indexes were choosed as the salinity inversion factors, and then the statistic model and BP neural network model were constructed. The results showed that the precision of BP neural network model was far better than that of the traditional multiple regression model. The inversion model was more suitable for soil salinization inversion mapping in high salinity region. It had a good application prospect.

关 键 词：BP神经网络 土壤盐渍化 Landsat8 黄河三角洲 

分 类 号：S156.4[农业科学—土壤学]