利用HJ-1-A/BCCD2数据反演冬小麦叶面积指数

Retrieving leaf area index of winter wheat using HJ-1-A/B CCD2 data

叶面积指数是十分重要的作物生理生态参数,为提高利用国产环境减灾小卫星CCD数据反演冬小麦叶面积指数的精度,该文以5种常用的植被指数(归一化差值植被指数(normalized difference vegetation index,NDVI),增强植被指数(enhanced vegetation index,EVI),双波段增强植被指数(2-bands enhanced vegetation index,EVI2),比值植被指数(ratiovegetation index,RVI),土壤调节植被指数(soil-adjusted vegetation index,SAVI)为基础,结合3种常用的回归模型,按生长阶段比较分析了不同植被指数和回归模型反演叶面积指数的精度。结果表明,除生殖生长阶段外,叶面积指数和5种植被指数之间均有较强的相关关系;指数模型和一元线性模型分别为全生育期和营养生长阶段的最佳拟合模型;EVI在全生育期拟合时的表现好于其他4个指数(R2=0.9348),SAVI则是营养生长阶段表现最佳的指数(R2=0.9404)。该研究为进一步利用植被指数反演叶面积指数提供了参考。

Leaf area index（LAI） is one of the most important biophysical parameters of crop and other land surface vegetation.In order to improve the accuracy of retrieving leaf area index of winter wheat using HJ CCD data,the accuracy of different vegetation indices and regression models were compared and analyzed from the aspects of growth stages on the basis of five common vegetation indices including NDVI（normalized difference vegetation index）,EVI（enhanced vegetation index）,EVI2（two-bands enhanced vegetation index）,RVI（ratio vegetation index） and SAVI（soil adjusted vegetation index） and three common regression models.The results showed that LAI and all five vegetation indices had good correlative relationship except at reproductive growth stage.Exponential and linear regression model were the best regression models for the whole growth stage and the vegetative stage,respectively.EVI performed better than other four indices when simulated at the whole growth stage（R2=0.9348）,and SAVI was the best index for LAI retrieving at vegetative growth stage（R2=0.9404）.This paper provides the reference for LAI inversion by vegetation index of winter wheat.