Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

We present the first comparison of Sentinel-2A (S2) MSI (Multi-Spectral Instrument) and Landsat 8 (L8) OLI (Operational Land Imager) data in the retrieval of forest canopy cover (CC), effective canopy cover (ECC), and leaf area index (LAI). We used S2 and L8 images obtained from Suonenjoki, Finland on 17 and 22 August 2015, respectively. A combination of airborne lidar data and field plots was used to calculate CC, ECC and LAI for a set of 746 systematically placed lidar plots. Generalized additive models were used to link these variables with various types of spectral indices. Our results indicated that instead of using the native image resolution, the model accuracies were better for 60 m lidar plots comprising of nine (S2) or four (L8) image pixels. The best-case absolute root mean square errors of prediction (RMSEPs) (relative RMSEPs shown in parentheses) obtained from ten-fold cross validation of multivariate models for S2 CC, ECC and LAI were 0.126 (24.0%), 0.100 (20.8%), and 0.596 (19.6%), respectively. For L8, the corresponding RMSEPs were 0.128 (24.5%), 0.108 (22.4%), and 0.614 (20.2%). The marginally better performance of S2 models may be related to the 705 nm red edge band, which frequently occurred among the selected predictors. When testing indices that used bands available for both sensors, there were no systematic differences between S2 and L8.