Exploring the potential of SAR and terrestrial and airborne LiDAR in predicting forest floor spectral properties in temperate and boreal forests

Forest floor vegetation plays a crucial role in ecosystem processes of temperate and boreal forests. Remote sensing offers a valuable tool to characterize the forest floor through reflectance spectra. While passive optical airborne and satellite data have been used to map spectral properties of forest understory, these sensors are limited by cloud cover, especially in high latitudes. To date, LiDAR and SAR have not been explored for this application even though their data are less dependent on illumination conditions and provide information on tree canopy structure and tree distribution which is connected to forest floor properties. We investigated active remote sensing techniques to establish links between forest structure and spectral properties of forest floor across European temperate, hemiboreal and boreal forest ecosystems. First, in the exploratory part, the research question was : Which forest structure metrics are connected to the spectral properties of the forest floor? Next, our predictive part focused on: What is the potential of (1) terrestrial laser scanning (TLS) data, (2) airborne laser scanning data, (3) satellite-borne SAR data, and (4) these data sources combined to predict forest floor spectral properties? Our results revealed that nine forest structure metrics were potentially associated with forest floor reflectance. We identified TLS-derived clumping index and SAR-derived VV backscatter coefficient and VH/VV ratio as significantly connected to forest floor reflectance in certain Sentinel-2 spectral bands. Overall, the active remote sensors achieved the best predictions for forest floor reflectance in red-edge, near-infrared and shortwave infrared regions. Using data from all three sensors together to predict the forest floor spectra yielded better results than using any of the sensors alone. When data from a single sensor were used, the highest prediction accuracies for forest floor reflectance in the red-edge and near-infrared regions were achieved with SAR data, and in the shortwave infrared region with either SAR or TLS data. In the future, the accuracy of predicting forest floor characteristics in temperate and boreal forests could benefit from a synergy of passive and active technologies. • Retrieval of forest floor spectral properties from active remote sensing data. • Forest structure metrics derived from radar, terrestrial and airborne lidar data. • Clumping index, VV intensity and VH/VV ratio related to forest floor spectra. • The three sensors combined predicted the forest floor spectrum the best. • Red-edge, NIR and SWIR reflectances were predicted the best.