基于多角度植被指数的马尾松林LAI反演方法

CHRIS/PROBA是目前具有最高空间分辨率（17 m×17 m）的星载多角度高光谱数据，该款数据在反演植被垂直结构参数，如树高、叶面积指数（leaf area index，LAI）等方面具有重要的应用前景。基于四尺度几何光学模型得到马尾松（Pinus massoniana Lamb.）冠层的归一化差分植被指数（normalized difference vegetation index，NDVI）各向异性分布规律，利用CHRIS红光特征波段和近红外特征波段构建一种新型多角度植被指数（normalized hotspot-dark-spot difference vegetation index，NHDVI），并将其应用于CHRIS数据对马尾松林的LAI遥感估算上。结果显示：（1）相比归一化差分植被指数（NDVI）与土壤调节植被指数（soil adjusted vegetation index，SAVI）而言，NHDVI能很好地融合光谱信息与角度信息，与地面实测LAI的决定系数达到0.7278；（2）利用NHDVI-LAI统计回归模型方法来反演LAI值，将得到的LAI值与地面实测值进行相关性分析，结果拟合优度达到0.8272，均方根误差RMSE为0.1232。与传统植被指数相比，包含角度信息的多角度植被指数对LAI的反演在精度上有较大提升，同时比基于辐射传输模型的反演方法更简易、实用。

Inversion of Masson pine forest LAI by multiple-perspective vegetation index

CHRIS/PROBA is a multiple-angle sensor providing hyper-spectral data with 17 m×17 m spatial resolution that can be applied for data inversion of vegetation canopy structure parameters, such as tree height and leaf area index (LAI). We used a four-scale geometrical optics model to simulate anisotropy distribution regulation of the normalized difference vegetation index (NDVI) of a Pinus massoniana (Masson pine) forest canopy. By extracting the red and near infrared characteristic spectral bands from the 18 bands in CHRIS, a new multi-angle normalized hotspot-dark-spot difference vegetation index (NHDVI) was applied to the estimation of the LAI of Pinus massoniana forest using CHRIS data. The results showed that:(1) Compared with the NDVI and soil adjusted vegetation index (SAVI), NHDVI well integrated the spectral information and angle information with the ground measured LAI, and the coefficient of determination reached 0.7278; (2) The LAI was calculated by statistical regression of NHDVI-LAI. The correlation between the LAI and the measured values was 0.8272, significantly higher than that of SAVI, and the root mean square error (RMSE) was 0.1232. Thus, these findings indicate that angular information is important for improving the retrieval accuracy of LAI.