Estimation of Leaf Area Index by Normalized Composite Vegetation Index Fusing the Spectral Feature of Canopy Water Content

The accurate inversion of leaf area index (LAI) in canopy is very important for guiding crop management and assessing crop yield. Sixty samples belonging to corn in four different areas of Jilin City were scanned by ASD field pro3 and LAI-2000 for optical data and LAI A new vegetation index, the normalized composite Vegetation index (NCVI), containing the factor of canopy water content, is proposed in the present paper for a better quantitative estimation of LAI than with the remotely sensed normalized difference vegetation index (NDVI), especially in the arid and semi-arid areas. A model was built for inversion of LAI with NCVI, and experience validation. The results showed that there was a good linear correlation between the simulation LAI inversed from NCVI model and the real LAI values. The model breaking the limitations of the traditional empirical models for LAI inversion has a good result for estimating LAI of the dense canopy whose LAI value was greater than 3. In addition, NCVI model was very sensitive to the water environment of soil, and the inversion result in the arid and semi-arid areas was superior to the general area.