Mapping subcanopy light regimes in temperate mountain forests from Airborne Laser Scanning, Sentinel-1 and Sentinel-2

Abstract

Sunlight is the primary source of energy in forest ecosystems and subcanopy light regimes largely determine the establishment, growth and dispersal of plants and thus forest floor plant communities. Subcanopy light regimes are highly variable in both space and time, which makes monitoring them challenging. In this study, we assess the potential of Sentinel-1 and Sentinel-2 time series for predicting subcanopy light regimes in temperate mountain forests. We trained different random forest regression models predicting field-measured total site factor (TSF, proportion of potential direct and diffuse solar radiation reaching the forest floor, here defined as the transition zone between belowground and aboveground biomass) from a set of metrics derived from Sentinel-1 and Sentinel-2 time series. Model performance was benchmarked against a model based on structural metrics derived from Airborne Laser Scanning (ALS) data, serving as an empirical gold-standard in modelling subcanopy light regimes. We found that Sentinel-1 and Sentinel-2 time series performed nearly as good as the model based on high-resolution ALS data (R2/RMSE of 0.80/0.11 for Sentinel-1/2 compared to R2/RMSE of 0.90/0.08 for ALS). We furthermore tested the generalizability of the trained models to two new sites not used for training for which field data was available for validation. Prediction accuracy for the ALS model decreased substantially for the two independent test sites due to variable ALS data quality and acquisition date (ΔR2/ΔRMSE of 0.29/0.05 and 0.11/0.03 for both independent test sites). The prediction accuracy of the Sentinel-1/2 model, however, remained more stable (ΔR2/ΔRMSE of 0.13/0.02 and 0.13/0.04). We therefore conclude that a combination of Sentinel-1 and Sentinel-2 time series has the potential to map subcanopy light conditions spatially and temporally independent of the availability of high-resolution ALS data. This has important implications for the operational monitoring of forest ecosystems across large scales, which is often limited by the challenges related to acquiring airborne datasets.