摘要
分别利用6S模型和FLAASH模型对TM影像进行了大气校正,建立了两种校正模型下的叶面积指数(LAI)与多种植被指数(VI)间的线性与非线性统计回归关系,并从植被指数角度和大气校正模型角度对LAI-VI回归相关性进行了分析,最后通过验证数据组LAI预测值(Y)与LAI实测值(X)的RMSE均方根误差及Nash-Sutcliffe效率系数对各模型下的LAI反演结果进行了精度对比验证。结果表明:除RVI外,两种大气校正模型下的LAI与SAVI、MSAVI有良好的相关性,其中6S模型下的LAI-MSAVI一元二次多项式回归模型拟合优度最佳;不同大气校正模型对LAI-VI回归方程的相关性有显著影响,6S模型的LAI反演精度优于FLAASH模型;借助遥感技术定量提取植被生理参数时,应慎重选择适宜的大气校正模型。
Using 6S model and FLAASH model respectively to carry on the atmospheric correction TM image, and establish the statistical regression relationship of the linear and nonlinear between leaf area index （LAI） and a variety of vegetation index （VI） under the two correction model. LAI - VI regression correlation was analyzed from the perspectives of vegetation index and the atmospheric correction model. The root mean square error and Nash - Sutcliffe efficiency coefficients of validation data set of predicted LAI value （Y） and the measured LAI values （X） were calculated. The results show that apart from RVI, two kinds of atmospheric correction model, namely, LAI and SAVI, MSAVI have good correlation. Among them LAI - MSAVI of 6S model quadratic equa- tion model tiffed best; Different atmospheric correction models had a significant influence on LAI - VI regres- sion equation. 6S model is better than that of FLAASH model at LAI estimation accuracy. With the aid of remote sensing technology to quantitatively extract the vegetation physiological parameters, suitable atmospheric correc-tion model should be taken to select considerately.
出处
《干旱区资源与环境》
CSSCI
CSCD
北大核心
2014年第7期62-66,共5页
Journal of Arid Land Resources and Environment
基金
国家自然科学基金项目(31200534)
江苏省2009年度研究生科研创新计划项目
南京林业大学高学历人才基金项目
江苏省高等学校林学优势学科建设项目资助
关键词
遥感
叶面积指数
大气校正
remote sensing
leaf area index
atmospheric correction
