冬小麦不同生育时期叶面积指数反演方法

针对当前作物叶面积指数遥感反演过程中，在不同生育时期采用相同的植被指数进行反演存在叶面积指数反演精度较低的问题。以冬小麦为研究对象，选取了对冬小麦覆盖度响应程度不同的六种宽带和四种窄带共10种植被指数，分析比较了在冬小麦整个生育期选用当前广泛使用的归一化植被指数(NDVI)反演冬小麦的LAI和在冬小麦不同生长阶段选用不同的植被指数反演冬小麦LAI的结果差异。在冬小麦整个生育期内使用NDVI反演小麦LAI得到的LAI反演值和真实值之间的R2=0.558 5，RMSE=0.320 9。改进的比值植被指数(mSR)适合于反演冬小麦生长前期(拔节期之前)的LAI，得到的LAI反演值和真实值之间的相关系数r=0.728 7，均方根误差RMSE=0.297 1;比值植被指数(SR)适于反演冬小麦生长中期(拔节到抽穗前)，得到的LAI反演值和真实值之间的R2=0.654 6，RMSE=0.306 1;NDVI适于反演冬小麦生长后期(抽穗到成熟期)的LAI，得到的LAI反演值和真实值之间的R2=0.679 4，均方根误差RMSE=0.316 4。 研究表明：在冬小麦的不同生育时期，根据地表作物覆盖度的变化和反射率的变化，选择不同的植被指数建立冬小麦LAI的反演模型获得的反演精度均高于在冬小麦整个生育期使用NDVI获得的反演结果。说明在冬小麦的不同生育时期选择不同的植被指数构建LAI的分段反演模型可以改善冬小麦LAI的反演精度。

Inversion of Leaf Area Index during Different Growth Stages in Winter Wheat

Being orientated to the low prescion of crop leaf area index (LAI) inversion using the same spectral vegetation index during different crop growth stages, the present paper analyzed the precision of LAI inversion by employing NDVI(normalized difference vegetation index). Ten vegetation indices were chosen including six broad-band vegetation indices and four narrow-band vegetation indices responding to vegetation cover to inverse LAI in different growth stages. Several conclusions were drawn according to the analysis. The determinant coefficient (R2) and root mean square error(RMSE) between LAI inversion value and true value were 0.558 5 and 0.320 9 respectively during the whole growth duraton. The mSR(modified simple ratio index) index was appropriate to inverse of LAI during earlier growth stages (before jointing stage) in winter wheat. The R2 and RMSE between LAI inversion value and true value were 0.728 7 and 0.297 1 respectively. The SR(simple ratio index) index was suitable enough to inverse of LAI during medium growth stages (from joingting stagess to heading stagess). The R2 and RMSE between LAI inversion value and true value were 0.654 6 and 0.306 1 respectively. The NDVI(normalized difference vegetation index) index was proven to be fine to inverse LAI during later growth stages(from heading stage to ripening stage). The R2 and RMSE between LAI inversion value and true value were 0.679 4 and 0.316 4 respectively. Therefore it was indicated that the results of LAI inversion was much better inverse of winter wheat LAI choosing different vegetation indices during differen growth stages for winter wheat according to the change of vegetation cover and canopy reflectance than merely with NDVI to inverse LAI in the whole growth stages. It was concluded that the precision of LAI inversion was significantly improved with segmented models based on different vegetation indices.