Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method

Implementing forest protection measures, such as afforestation, can be an effective approach toward slowing down the increase of CO<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> concentration and attaining carbon neutrality. The estimation of forest parameters is of great significance in understanding regional and global climate change patterns, and the Forest Leaf Area Index (LAI) is a crucial parameter. Current LAI products are mostly generated by moderate-resolution remote sensing data which does not meet the precision requirements for mountain forest ecosystems. To overcome this issue, there is an urgent need for higher resolution LAI data. This article proposes a data fusion method to map LAI in Wolong Nature Reserve that utilizes Sentinel-2 reflectance data, solar sensor geometry parameters, and vegetation indices extracted from the Google Earth Engine platform, along with canopy height data derived from canopy height estimation models in previous studies, combined with GLASS LAI V6 to estimate LAI using the random forest algorithm. The resulting LAI distribution map was plotted at a resolution of 20 m. The study demonstrated that incorporating canopy heights into the estimation model led to an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> model accuracy of greater than 0.83. The 20-m resolution LAI map increased spatial details compared to the moderate-resolution LAI map, making it more suitable for mountain forest ecosystems that exhibit significant spatial heterogeneity.