Improving leaf area index (LAI) estimation by integrating forest inventory and remote sensing

The mission of this study is to improve the accuracy of leaf area index (LAI) using ground-based forest inventory 'bottom-up' LAI with satellite-derived 'top-down' LAI estimates. Specifically, we compared LAI values obtained using allometric equations applied to over 30,000 trees in the Austrian National Forest Inventory (NFI) with satellite-derived LAI estimates from MODIS (Moderate Resolution Imaging Spectroradiometer) and Sentinel data sets (Sentinel-3 TOC reflectance and PROBA-V). Our results indicate that satellite-derived LAI estimates often underestimate the actual LAI observed in terrestrial data. This discrepancy is mainly due to the inability of remote sensing technologies to account for the Crown Competition Factor (CCF), which significantly influences canopy structure. As LAI is a critical parameter in ecosystem modelling, accurate LAI estimates are essential for reliable model outputs. To address this issue, we developed a logistic correction function by incorporating bottom-up and top-down LAI to improve the accuracy of LAI estimates for a sustainable forest management.