文献类型：期刊文献

中文题名：基于Ts-EVI特征空间的土壤水分估算

英文题名：Estimation of soil moisture from Ts-EVI feature space

作者：闫峰[1] 王艳姣[2]

第一作者：闫峰

机构：[1]中国林业科学研究院荒漠化研究所;[2]中国气象局国家气候中心

年份：2009

卷号：29

期号：9

起止页码：4884-4891

中文期刊名：生态学报

外文期刊名：Acta Ecologica Sinica

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金资助项目(40801173);中国博士后科学基金资助项目(20070420308)

语种：中文

中文关键词：Ts;EVI;特征空间;土壤水分;河北

外文关键词：Ts; EVI; feature space ; soil moisture ; Hebei

分类号：Q149;Q154.1

摘要：温度-植被指数特征空间耦合了地表温度和植被信息,是当前实现土壤水分遥感估算和农业旱情监测的重要方法。采用EOS-MODIS地表温度Ts和增强型植被指数EVI数据,研究Ts-EVI三角形特征空间中干边、湿边方程参数的确定方法,分析比较了温度植被干旱指数TVDI对不同土壤深度水分状况的估算能力,为利用特征空间法实现土壤水分监测提供理论依据。研究表明:特征空间中干边和湿边的确定以最大拐点处为始点进行线性拟合的常规方法并不完善,根据像元的分布频率,以采用能同时保留最大量有效信息和较高拟合精度的端点逼近法获取参数的效果较好;基于Ts-EVI特征空间构建的TVDI可以较好地估算土壤表层10、20cm和50cm土壤深度处土壤水分状况,其相关性均通过了α=0.001水平的t检验,但TVDI对表层土壤(20cm和10cm)水分的估算精度相对较高。
Temperature-vegetation index feature space, which couples information of land surface temperature （ Ts ） and vegetation, is an important method for soil moisture estimation and agricultural drought monitoring. In this paper, Ts and enhanced vegetation index （EVI）, which derived from AQUA- MODIS （Moderate Resolution Imaging Spectroradiometer） data, were used to build triangular Ts-EVI feature space. How to determine better parameters in dry edge and wet edge equations was discussed and temperature vegetation drought index （TVDI） was calculated. Relations between TVDI and relative soil moisture （RSM） in different depths were analyzed and the abilities of soil moisture estimation of TVDI were also compared. Results showed that ： the traditional method which used the biggest inflection point as the starting one to fit linear regression equations was not perfect to get parameters of dry edge and wet edge. However, according to frequency distribution of different pixel value, the endpoint approximatioss method, which retained the maximum effective information and keep higher fitting precision, had strong ability to get better parameters. TVDI got from Ts-EVI feature space could estimate RSM in soil surface with the depth of 10, 20cm and 50cm, and the correlations between RSM and TVDI passed t- test at significance level α = 0. 001, but the accuracy of soil moisture estimation by TVDI for the depths of 20cm and 10cm were relatively higher.