We have constructed a phenological model of leaf area index (LAI) of forests based on biological principles of leaf growth. Field data of maximum LAI from 794 plots with mature or nearly mature stand ages over China were used to parameterize and calibrate the model. New measurements of maximum LAI from 16 natural forest sites were used to validate the simulated maximum LAI. The predictions of seasonal LAI patterns were compared with seasonal changes derived from the 1-km satellite AVHRR-NDVI data for nine undisturbed forest sites in eastern China. Then, we used the model to map maximum LAI values for forests in China.
Model results indicated that the PhenLAI model generally predicted maximum LAI well for most forest types, even when maximum LAI is > 6. This suggests an ecological approach to the saturation problem in satellite detection of high forest LAI where the relationship between NDVI and LAI reaches an asymptote near a projected LAI value of 5 or 6. Furthermore, the predictions of seasonal LAI patterns in timing and dynamics were generally consistent with the satellite NDVI changes, except for monsoon forest and rain forest in south China where satellite detection of seasonal variation in leaf area is hardly possible. Compared with average projected LAI measurements of global forests from 809 field plots in literature data, our maximum LAI values were close to the global literature data for most of Chinese forests, but the average area-weighted maximum LAI for all forests of China (6.68 ± 3.85) was higher than the global mean LAI of the 809 field plots (5.55 ± 4.14). We believe that forest LAI in China is commonly > 6, especially in tropical rainforest, subtropical evergreen broad-leaved forest, temperate mixed forest, and boreal/alpine spruce-fir forest where satellite detection of high LAI is hardly possible.
Abbreviations: LAI = Leaf Area Index; NDVI = Normalized Difference Vegetation Index; fPAR = Fraction of incoming Photosynthetically Active Radiation absorbed by plant canopy; WBM = Water balance model.