玉米叶面积指数的CHRIS/PROBA数据反演分析

叶面积指数(LAI)是衡量植被生态状况和估算作物产量的一个重要指标。LAI的反演是定量遥感研究的重要内容。传统的经验统计反演方法基于单一观测角度的遥感数据进行,忽略了地物反射率的方向性。若在反演中加入多观测角度的信息,则有可能提升LAI反演的精度。以2008年甘肃省张掖市玉米实验区为研究区,利用欧空局的CHRIS/PROBA多角度高光谱数据对比分析了传统植被指数NDVI、RVI、EVI的变化规律及其反演玉米叶面积指数LAI的精度,并根据NDVI随观测角度的变化规律,构造出新型多角度归一化植被指数MNDVI,分别对实测叶面积指数进行线性回归并利用实测数据对估算LAI进行精度验证,结果表明：新型MNDVI指数相比于传统NDVI、RVI、EVI对LAI的反演精度有了显著提升,估算模型决定系数R²达到0.716,精度验证均方根误差为0.127,平均减小了33.3%。

Estimating Leaf Area Index of Maize Based on Multi-angular CHRIS/PROBA Data

Leaf area index is an important parameter for evaluating vegetation ecological conditions and estimating crop yields. Thus, the estimation of LAI has always been a hotspot of quantitative remote sensing research. A growing number of studies have focused on estimating the leaf area index (LAI) of vegetation using several traditional vegetation indices(the Normalized Difference Vegetation index (NDVI), the Ratio Vegetation Index (RVI), and the Enhanced Vegetation Index (EVI)). These vegetation indices were all based on the data of single view zenith angle, which limited the accuracy of LAI estimation. In this article, we compared the sensitivity of the three vegetation indices for crop canopies, and then put forward a new vegetation index named Multi-angle Normalized Difference Vegetation Index (MNDVI) based on CHRIS/PROBA data which includes information with respect to five different view zenith angles. Using the ground crop LAI data obtained in Zhangye city from Gansu Province in June 2008, this paper compared the estimation models of LAI based on the four vegetation indices including the three traditional indices (NDVI, RVI and EVI) and MNDVI. The result shows that: compared with the traditional ones, MNDVI has a much better correlation with LAI, and the correlation coefficient R² of the LAI calculation model reaches up to 0.716. Besides, in order to verify the accuracy of LAI retrieval model based onMNDVI, this paper calculated the RMSE between the estimated LAI using MNDVI model and the ground-measured LAI, finding that the RMSE was 0.127, which was averagely 33.3% lower comparing with methods using traditional vegetation indices.