Abstract
Leaf area index (LAI) is an important parameter in many ecological models and is used to link remote sensing and ecological models. The relationship of LAI and vegetation indices (VIs) such as the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) are not as linear relationship especially for deciduous broadleaf forests due to leaf seasonality. To obtain the accurate LAI–VI relationship, in this study, in situ observation data of LAI and VIs were used to rigorously examine the LAI–VI relationship for a deciduous broadleaf forest. Spectral reflectance data from a hemispherical spectroradiometer (HSSR) system were used to calculate in situ NDVI and EVI. LAI was estimated using leaf seasonality (in situ measurement of sample shoots) and litter trap approaches. The LAI–VI relationship was analyzed in two ways: (1) as a one-period relationship using data for the entire year and (2) for two separate periods, leaf-expansion to -saturation and leaf-saturation to -fall. There were nearly linear correlations between both in situ NDVI and EVI and in situ LAI when looking at data for the entire year. When separated into two time periods, the LAI–EVI relationship was clearer than the LAI–NDVI relationship, with a linear regression (r2=0.96) in the leaf-expansion to -saturation period and a clear logarithmic curve (r2=0.97) in the leaf-saturation to -fall period. NDVI and EVI estimates of LAI derived from a moderate resolution imaging spectroradiometer (MODIS) were evaluated by applying the regression equations for both the one- and two-period LAI–VI relationships. The two-period relationships presented better results than the single relationship, because they responded directly to the leaf phenological cycle of the deciduous broadleaf forest. These results indicate that the two-period relationships estimate LAI more accurately than the single relationship, especially in the leaf-expansion to -saturation period. However, the LAI–VI relationship in the leaf-saturation to -fall period should be carefully reconsidered to improve the accuracy of primary productivity estimations.
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