Statistical and physical models for mapping canopy chlorophyll content from Sentinel-2 Data

Assessment of canopy chlorophyll content (CCC) is an essential variable in developing indicators for biodiversity monitoring and climate change studies. The Sentinel-2 Multi-Spectral Imager (MSI) is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving CCC from Sentinel-2 in Bavarian forest national park, Germany. Fourteen statistical-based methods, including 13 different vegetation indices (VIs) and a non-parametric statistical approach, and two physical-based methods such as INFORM and PROSAIL radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field data collected in July 2017, and cloud-free Sentinel-2 image acquired on 13 July 2017 were used for evaluating the performance of the methods. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. The modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE = 0.22 g/m2 than mSR3. Further, the physical-based approach-INFORM inversion using look-up table resulted in an RMSE = 0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for large scale mapping.