A COMPARATIVE ANALYSIS OF SPECTRAL VEGETATION INDICES TO ESTIMATE CROP LEAF AREA INDEX

Leaf area index (LAI) is a key variable to reflect crop growth status and forecast crop yield. Many spectral vegetation indices (SVIs) suffer the saturation effect which limits the usefulness of optical remote sensing for crop LAI retrieval. Besides, leaf chlorophyll concentration and soil background reflectance are also two main factors to influence crop LAI retrieval using SVIs. In order to make better use of SVIs for crop LAI retrieval, it is significant to evaluate the performances of SVIs under varying conditions. In this context, PROSPECT and SAILH models were used to simulate a wide range of crop canopy reflectance in an attempt to conduct a comparative analysis. The sensitivity function was introduced to investigate the sensitivity of SVIs over the range of LAI. This sensitivity function is capable of quantifying the detailed relationship between SVIs and LAI. It is different with the regression based statistical parameters, such as coefficient of determination and root mean square, can only evaluate the overall performances of SVIs. The experimental results indicated that (1) LAI = 3 was an appropriate demarcation point for comparative analyses of SVIs; (2) when LAI was no more than three, the variations of soil background had significant negative effects on SVIs. LAI Determining Index (LAIDI), Optimized Soil-adjusted Vegetation Index (OSVI) and Renormalized Difference Vegetation Index (RDVI) were relatively optimal choices for LAI retrieval; (3) when LAI was larger than three, leaf chlorophyll concentration played an important role in influencing the performances of SVIs. Enhanced Vegetation Index 2(EVI2), LAIDI, RDVI, Soil Adjusted Vegetation Index (SAVI), Modified Triangular Vegetation Index 2(MTVI2) and Modified Chlorophyll Absorption Ratio Index 2 (MCARI2) were less affected by leaf chlorophyll concentration and had better performances due to their higher sensitivity to LAI even when LAI reached seven. The analytical results could be used to guide the selection of optimal SVIs for crop LAI retrieval in different phenology periods.