Improving space-time forest canopy LAI simulation by fusing forest growth model (3-PG) with remote sensing data

Abstract

Leaf area index (LAI) is an important biophysical variable indicating forest growth. A major challenge is to improve the LAI estimates for large forest-covered areas. One way to obtain LAI value is using current LAI products. Current LAI products contain many uncertainties and need improvement. This paper aims to improve forest LAI estimates by combining satellite reflectance derived LAI with forest growth model (physiological principals predicting growth, 3-PG) estimates of LAI. 3-PG can give an accurate estimation of forest inter-annual growing trend, while remote sensing data can provide long time series observation of seasonal variations of forest phenology. We applied this method to Chinese fir forest in China, where the detailed data are available. The combined results were more accurate than either the satellite or the 3-PG estimates. We conclude that we can improve the space-time forest canopy LAI estimates by combining forest growth model with satellite imagery.