Retrieval of Specific Leaf Area From Landsat-8 Surface Reflectance Data Using Statistical and Physical Models

One of the key traits in the assessment of ecosystem functions is a specific leaf area (SLA). The main aim of this study was to examine the potential of new generation satellite images, such as Landsat-8 imagery, for the retrieval of SLA at regional and global scales. Therefore, both statistical and radiative transfer model (RTM) inversion approaches for estimating SLA from the new Landsat-8 product were evaluated. Field data were collected for 33 sample plots during a field campaign in summer 2013 in the Bavarian Forest National Park, Germany, while Landsat-8 image data concurrent with the time of field campaign were acquired. Estimates of SLA were examined using different Landsat-8 spectral bands, vegetation indices calculated from these bands, and the inversion of a canopy RTM. The RTM inversion was performed utilizing continuous wavelet analysis and a look-up table (LUT) approach. The results were validated using R² and the root-mean-square error (RMSE) between the estimated and measured SLA. In general, SLA was estimated accurately by both statistical and RTM inversion approaches. The relationships between measured and estimated SLA using the enhanced vegetation index were strong (R² = 0.77 and RMSE = 4.44%). Furthermore, the predictive model developed from combination of the wavelet features at 654.5 nm (scale 9) and 2200.5 nm (scale 2) correlated strongly with SLA (R² = 0.79 and RMSE = 7.52%). The inversion of LUT using a spectral subset consisting of bands 5, 6, and 7 of Landsat-8 (R² = 0.73 and RMSE = 5.33%) yielded a higher accuracy and precision than any other spectral subset. The findings of this study provide insights into the potential of the new generation of multispectral medium-resolution satellite imagery, such as Landsat-8 and Sentinel-2, for accurate retrieval and mapping of SLA using either statistical or RTM inversion methods.