基于卫星遥感的黄三角濒海区土壤水盐及其耦合关系时空分析  

Spatio-temporal analysis of soil water and salt and their coupling relationship in the Yellow River Delta coastal area based on satellite remote sensing

作　　者：张术伟 常春艳 王卓然[1] 赵铭 赵庚星[1] 李因帅 赵环三 赵永昶 王启尧 周家豪 ZHANG Shuwei;CHANG Chunyan;WANG Zhuoran;ZHAO Ming;ZHAO Gengxing;LI Yinshuai;ZHAO Huansan;ZHAO Yongchang;WANG Qiyao;ZHOU Jiahao(College of Resources and Environment,Shandong Agricultural University,National Engineering Research center for Efficient Utilization of Soil and Fertilizer Resources,Tai′an 271018,China)

机构地区：[1]山东农业大学资源与环境学院,土肥高效利用国家工程研究中心,山东泰安271018

出　　处：《农业资源与环境学报》2023年第4期792-804,共13页Journal of Agricultural Resources and Environment

基　　金：国家自然科学基金项目(41877003);山东省重大科技创新工程项目(2019JZZY010724);山东省“双一流”奖补资金项目(SYL2017XTTD02)。

摘　　要：水盐时空特征及耦合关系是盐渍化土壤治理和区域生态保护管理的重要基础。本研究选择黄河三角洲代表性濒海区域——垦利区,采用野外实测与遥感影像相结合的方法,筛选相关性较高的土壤水盐光谱参量,基于敏感光谱参量构建土壤水盐定量遥感反演模型,利用经典统计分析、耦合度模型、缓冲区分析、支持向量机分类等方法,分析研究区土壤表层水盐时空分异特征及其耦合关系。结果表明:基于水分敏感光谱指数LSWI、NDII和盐分敏感光谱指数SI-T、NDSI建立的各季节土壤水盐估测模型,验证集R2均大于0.650,RMSE均小于7,Sig<0.001;研究区土壤水盐含量总体较高,含盐量以中度盐渍化为主;不同季节土壤含水量排序为夏季>春季>秋季>冬季,土壤含盐量排序为春季>秋季>冬季>夏季,水盐耦合度排序为春季>秋季>冬季>夏季;不同植被类型土壤含水量排序为荒草地>光板地>耕地>林地,土壤含盐量排序为光板地>荒草地>耕地>林地,水盐耦合度排序为光板地>荒草地>耕地>林地;由近海到内陆,研究区土壤表层含水量、含盐量以及水盐耦合度呈现逐步递减的趋势,但土壤含盐量在距海>40~50 km处、含水量在距海>30~40 km处明显上升,耦合度也有所提高。本研究提供了濒海区域土壤水盐及其耦合关系的高效定量时空分析方法,研究结果可为滨海盐碱土改良利用与资源管理提供参考和依据。The spatio-temporal characteristics and coupling relationship of water and salt are important basis of salinization soil management and regional ecological protection management.This study examined representative coastal areas of the Yellow River Delta:Kenli.The method of combining field measurement with remote sensing image was used to screen the soil water and salt spectral parameters with high correlation,and a soil water and salt quantitative remote sensing inversion model based on the sensitive spectral parameters was constructed.The spatio-temporal variability and coupling of soil surface water and salt in the study area were analyzed using classical statistical analysis,coupling degree model,buffer zone analysis,and support vector machine classification.The results showed that the estimation models of soil water and salt based on LSWI,NDII,SI-T,and NDSI were established.The verification set R2 was more than 0.650, and RMSE was below 7, Sig<0.001. The soil water and salt content in the study area was generally high, and the saltcontent was mainly moderate salinization. The order of soil water was summer>spring>autumn>winter, the order of soil salinity was spring>autumn>winter>summer, and the order of coupling degree was spring>autumn>winter>summer. Soil water ranked as grassland>bare land>arable land>forest land, soil salinity ranked as bare land>grassland>arable land>forest land, and the coupling degree was as follows:bareland>grassland>arable land>forest land. The soil surface water content, salt content, and water-salt coupling degree showed a graduallydecreasing trend from offshore to inland in the study area. The soil salt content increased obviously at the distance of >40-50 km and thesoil water content increased obviously at the distance of >30-40 km from the sea, the coupling also improved. This study provides anefficient quantitative spatio-temporal analysis method of soil water and salt and their coupling relationship in coastal areas. The results ofthis study can provide a reference and basis for

关 键 词：土壤含水量 土壤含盐量 耦合关系 时空分异 滨海盐碱土 

分 类 号：S153.6[农业科学—土壤学] TP751[农业科学—农业基础科学] S156.41[自动化与计算机技术—检测技术与自动化装置]