基于光谱指数建模的阿拉尔垦区土壤盐渍化信息提取与分析  

Extraction and analysis of soil salinization information of Alar reclamation area based on spectral index modeling

作　　者：代云豪 管瑶[1] 冯春涌 蒋敏 贺兴宏[1] DAI Yunhao;GUAN Yao;FENG Chunyong;JIANG Min;HE Xinghong(College of Water Conservancy and Architecture Engineering,Tarim University,Alar 843300,China;Department of Geographical Sciences,Beijing Normal University,Beijing 100088,China)

机构地区：[1]塔里木大学水利与建筑工程学院,阿拉尔843300 [2]北京师范大学地理科学学部,北京100088

出　　处：《自然资源遥感》2023年第1期205-212,共8页Remote Sensing for Natural Resources

基　　金：农业部作物需水与调控重点实验室开放课题“南疆极端干旱区不同土质点源滴灌入渗及水盐运移规律研究”(编号:FIRI2019-03-0202);兵团重大项目子课题“新疆空中水资源利用研究与示范”(编号:2017AA002);兵团重点产业项目“南疆生态农田水资源多维调控模式研究”(编号:2021DB017);中国农业大学塔里木大学联合基金“南疆极端干旱区残余盐土滴灌水盐运移原理及水分高效利用研究”(编号:2019TC157);塔里木大学研究生科研创新项目“基于3S技术对塔里木灌区的土壤盐渍化时空动态预测分析研究”(编号:TDGRI202042)共同资助。

摘　　要：为了探究反演新疆维吾尔自治区阿拉尔垦区土壤盐渍化最优遥感盐分监测指数模型,以Landsat8 OLI遥感影像和野外实测数据为基础,通过盐分指数(salinity index,SI)、归一化植被指数(normalized difference vegetation index,NDVI)、修改型土壤调节植被指数(modified soil-adjusted vegetation index,MSAVI)、地表反照率(Albedo)构建遥感盐分监测指数模型(salinization detection index,SDI),提取阿拉尔垦区土壤盐渍化信息并验证模型精度,对比分析得出最优遥感盐分监测指数模型。结果表明:4类遥感盐分监测指数模型中SDI1(SI-NDVI),SDI2(SI-MSAVI),SDI3(SI-Albedo)和SDI4(Albedo-MSAVI)总体分类精度为83.45%,69.78%,53.23%和71.94%;SDI1模型最适合反演阿拉尔垦区土壤盐渍化程度,SDI2和SDI4模型对阿拉尔垦区土壤盐渍化监测有一定参考意义;利用SDI1模型反演阿拉尔垦区土壤盐渍化分布,垦区以非盐渍土和轻度盐渍土为主,重度盐渍土和盐土主要分布在垦区的东北和东南地区。由SI和NDVI构建SDI1对阿拉尔垦区土壤盐渍化信息提取精度较高,可作为反演垦区土壤盐渍化的遥感盐分监测指数模型,可为垦区土壤盐渍化治理和防治提供有效的技术参考。This study aims to explore the optimal remote sensing salinization detection index(SDI)model for the inversion of soil salinization in the Alar reclamation area.Based on Landsat8 OLI remote sensing images and field measured data,this study built the remote sensing SDI models using the salinity index(SI),the normalized difference vegetation index(NDVI),the modified soil adjusted vegetation index(MSAVI),and the surface albedo.Then,using these models,this study extracted the soil salinization information on the Alar reclamation area and verified the model precision.Finally,this study determined the optimal remote sensing-based SDI model through comparative analysis.The results are as follows.The four types of remote sensing-based SDI models SDI1(SI-NDVI),SDI2(SI-MSAVI),SDI3(SI-Albedo),and SDI4(Albedo-MSAVI)had general classification precision of 83.45%,69.78%,53.23%,and 71.94%,respectively.Model SDI1 was the most suitable for the inversion of the degree of soil salinization in the Alar reclamation area.Models SDI2 and SDI4 can be utilized as a reference for soil salinization monitoring of the Alar reclamation area.As revealed by the inversion results of the SDI model,the reclamation area is dominated by non-saline and lightly saline soils,with heavily saline soil and saline soil primarily distributed in the northeast and southeast.Model SDI1 established based on SI and NDVI has high accuracy in extracting the soil salinization information of the Alar reclamation area and can be used as the remote sensing-based SDI model for the inversion of soil salinization in reclamation areas.This study can provide an effective technical reference for the control and prevention of soil salinization.

关 键 词：光谱指数 阿拉尔垦区 土壤盐渍化 遥感盐分监测指数模型 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] S153[自动化与计算机技术—控制科学与工程]