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 on MNDVI, 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 averagely33.3% lower comparing with methods using traditional vegetation indices.