河套灌区土壤盐渍化微波雷达反演  

Soil salinity inversion in Hetao Irrigation district using microwave radar

作　　者：刘全明[1] 成秋明[2] 王学[1] 李相君[1] 

机构地区：[1]内蒙古农业大学水利与土木建筑工程学院测绘工程系,呼和浩特010018 [2]加拿大约克大学地球空间科学与工程系

出　　处：《农业工程学报》2016年第16期109-114,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51249007;51569018);内蒙古自然科学基金项目(2013MS0609)

摘　　要：目前中国西北干旱、半干旱地区的土壤盐渍化情况日益趋于严重,动态、快速而精确地监测与评价土壤盐渍化显得尤为重要。微波遥感所具有的优点使其成为探测土壤盐分分布的新兴而有潜力的方法。快速获取大范围地表土壤盐渍化的空间分布是一个迫切急需解决的科学难题。该文目的是试验与评价C波段RADARSAT-2 SAR(synthetic aperture radar)数据反演土壤盐渍化的性能。以受盐渍化影响较严重的内蒙古河套灌区解放闸灌域为试验区,基于SAR后向散射系数和土壤盐分实测值,利用多元线性回归(multiple linear regress,MLR)、地理加权回归(geographically weighted regression,GWR)和BP人工神经网络(back propagation artificial neural networks,BP ANN)方法建立土壤含盐量的定量反演模型,重点构建了8∶140∶1结构的3层BP ANN模型,经模型验证发现MLR、GWR模型均偏向于弱相关,其标准误差SE分别为0.55、0.47 mg/g,而ANN(BP)模型的内部、外部检验标准误差SE分别为0.24、0.33 mg/g,优于前2种模型,其反演的盐渍化面积占比65.4%,与地面验证结果基本一致。该文建立的考虑土壤水分影响、组合雷达后向散射系数反演土壤盐分的人工智能模型,无需复杂的介电常数模型,能够在一定程度上满足土壤盐渍化监测的需要,可促进微波遥感在土壤盐渍化监测中的开拓应用。The expanding trend of soil salinization has become more and more severe especially in arid and semi-arid areas in the northwest China. Therefore it is specially important to dynamically monitor the soil salinization in the arid and semi-arid areas scientifically, accurately and rapidly. Microwave remote sensing technique has become a promising method to detect and monitor the soil salinity due to its many advantages. The aim of this study was to investigate the capability of C-band RADARSAT-2 SAR（synthetic aperture radar） data in soil salinity estimation over agricultural fields. In this study, Jiefangzha zone of Hetao irrigation district, Inner Mongolia, China was selected as the study area. Based on the back-scatter coefficient value and soil salt content, this paper used 3 kinds of methods including the multiple linear regression（MLR）, geographically weighted regression（GWR） and back propagation artificial neural network（BP ANN） to establish the quantitative inversion models of soil salt content. Soil salinity information was extracted from the RADARSAT-2 SAR data, which had a kind of fine four-polarization SLC（single look complex） format and were bought in 2013, and covered an area of 25 km × 25 km with 8 m ground resolution. Taking the spatial unevenness distribution of the saline soil into account, 69 sampling points were designed in the study area, and field digging depth of soil was 10 cm. Hand-held GPS（global position system） receiver was used to record the coordinates of sampling points, and the soil total soluble salt content was measured in the indoor. Mainly use the SAR Scape module of ENVI software to perform the radar image processing, including radiometric calibration, geometric correction, slant range turning and filtering. The four-polarization back-scatter coefficient values corresponding to the sampling points were extracted based on the previous results by the spatial analysis module of Arc GIS. Total salt content was took as dependent variable, four-polarization bac

关 键 词：土壤 神经网络 雷达 土壤盐渍化 反演 

分 类 号：S156.4[农业科学—土壤学]