Linking leaf area index measurements across scales from smartphone LiDAR to multi-source remote sensing observations.

Leaf Area Index (LAI) is a fundamental biophysical parameter for land surface process models and remote sensing applications. Accurate quantification of leaf area and biomass is essential for understanding vegetation's role in the regional carbon balance and for models simulating biogeochemical processes. Remote sensing data have been widely used in long term and large-scale LAI estimations but the critical ground truth for developing and validating LAI estimations remains challenging especially in remote areas such as Arctic regions.   

This study evaluates the efficacy of low-cost proximity sensing with consumer-grade smart phone LiDAR and structure-from –motion (SfM)  sampling method to derive high resolution 3D plant modelsin Kobbefjord, Greenland. These innovative ground-based datasets were compared against manual in situ LAI measurements to assess their accuracy in tundra ecosystems. We further integrated these high-resolution point clouds into a multi-tiered, data-driven framework, upscaling the measurements through UAV (drone) imagery to satellite observations. This multi-scale approach enhances our ability to e.g. monitor rapid Arctic greening and improves the precision of biophysical inputs for climate and emission models.