Estimating forest leaf area index and canopy chlorophyll content with Sentinel-2: an evaluation of two hybrid retrieval algorithms

Brown, LA; Ogutu, BO; Dash, J

Abstract

Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.

Citation

Brown, L., Ogutu, B., & Dash, J. (2019). Estimating forest leaf area index and canopy chlorophyll content with Sentinel-2: an evaluation of two hybrid retrieval algorithms. Remote Sensing, 11(15), https://doi.org/10.3390/rs11151752

• Journal Article Type: Article
• Acceptance Date: Jul 23, 2019
• Publication Date: Jul 25, 2019
• Deposit Date: Oct 28, 2022
• Publicly Available Date: Oct 28, 2022
• Journal: Remote Sensing
• Publisher: MDPI
• Volume: 11
• Issue: 15
• DOI: https://doi.org/10.3390/rs11151752
• Publisher URL: https://doi.org/10.3390/rs11151752