基于改进植被指数土壤水分遥感反演

摘要/Abstract

摘要： 以渭-库绿洲为例，基于Landsat8 OLI遥感数据，考虑到短波红外特征与土壤水分有很好的关联，将短波红外波段引入可见光-近红外波段构成的传统植被指数中，旨在建立新的植被指数监测土壤水分。基于改进前后共8种植被指数，通过灰色关联分析（GRA）筛选出3种高关联度植被指数，再用偏最小二乘回归（PLSR）进行建模，然后用该模型对研究区土壤水分反演，并对其空间分布格局进一步分析。结果显示：（1）在传统植被指数的基础上引入信息量较大的短波红外，可大幅度降低植被指数间的VIF，消除其多重共线性。（2）通过GRA分析可知，改进后的植被指数与土壤水分之间的关联度均要高于传统植被指数。（3）通过GRA分析筛选出3种高关联度植被指数建立得到精度较高，稳定性较好的PLSR模型，并反演研究区土壤水分分布状况，土壤水分总体上至西向东，由北到南降低，然而土壤水分最小值主要分布在绿洲—荒漠交错带，使得交错带成为“生态裂谷”。研究表明：将短波红外波段引入到可见光-近红外植被指数中，建立的新植被指数可获得较好的土壤水分空间分布反演结果。

关键词: 土壤水分, 植被指数, 灰色关联分析, 偏最小二乘回归

Abstract: Fast acquisition of the soil moisture content,characteristics,and spatial distributing are the objective needs of agricultural drought management and control. This paper focused on the soil moisture content on arid and semi-arid bytaking Delta Oasis of the Weigan and Kuqa Rivers in Xinjiang as an example. Based on the multi-spectral remote sensing image of Landsat 8 OLI which were collectedon July 15,2015,the traditional vegetation index (VI)was extended by adding the short-wave infrared band,then Grey Relational Analysis (GRA) was used to sort the vegetation index before and after the improvement,and the first three vegetationindiceswhich hada larger gray relational degree were screened out,at the same time,the Partial least squares regression (PLSR)inversion models of soil moisture content were built and validated. Finally,the spatial distribution of soil moisture content wasanalyzed by using the PLSR model in the study area. The study results show as follows: (1)On the basis of the traditional vegetation index,the shortwave infrared with large amount of information was introduced,by comparing the variance inflation factor (VIF)vegetation index before and after the improvement, the multi-collinearity between vegetation indices was greatly reduced by extending the traditional vegetation index; (2)Grey Relational Analysis was used to sort the vegetation index before and after the improvement,the analysis results were as follows:EEVI >ERVI >ENDVI >RVI >EDVI >DVI >NDVI >EVI,the gray relational degree between EEVI,ERVI,ENDVI and soil moisture were all over 0.8,and it also showed that the gray relational degreebetween the extended vegetation indicesand the soil moisture content was higher than the corresponding traditional vegetation index; (3)The first three vegetation indices which had larger gray relational degree were screened out to build the Partial least squares regressioninversion models of soil moisture content,in the PLSR model,coefficient of determination (R²)of the calibration and validation were 0.82 and 0.80 respectively,the root mean squares error (RMSE)of the calibration and validation were 0.030 and 0.034 respectively,and the validation relative prediction deviation RPD equals to 2.07,greater than 2.00.Themain reasons of improving the model precision were that the shortwave infrared (SWIR)on Landsat 8 OLI had more informationand could protrude the difference in vegetation coverage and production status. Therefore,the model had very high accuracy andreliability, and the spatial distribution of the soil moisture content in the studyarea was analyzed based on the PLSR model. The statistical information of the inversed soil moisture content showed that the soil moisture content was gradually reduced from the west to east,and north to south. However,the minimum soil moisture content was mainly distributed in the oasis-desert ecotone,which made the ecotone become "ecological rift".Therefore,the experiment indicated that the vegetationindex was modified by the introduction of the shortwave infrared based on Landsat 8 OLI,and the PLSR model of soil moisture content had been built,by which better inversion result of soil salinity spatial distribution could be obtained. But in order to achieve accurate inversion,in the future,to enhance the experimental dataprocessing abilityand improve the simulation accuracy,more samples and accurate modelswill be laid and builtas much as possible.

Key words: soil moisture, vegetation index, grey relational analysis, partial least squares regression

中图分类号:

S152.7

蔡亮红, 丁建丽. 基于改进植被指数土壤水分遥感反演[J]. 干旱区地理, 2017, 40(6): 1248-1255.

CAI Liang-hong, DING Jian-li. Remote sensing inversion of soil moisture based on modified vegetation index[J]. , 2017, 40(6): 1248-1255.

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