Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods

Abstract Chlorophyll is a good indicator of health status and nutritional condition as plant grows. Many studies have investigated the feasibility of retrieving leaf chlorophyll content (LCC) using vegetation indices (VIs) of multiple plant species, yet very few studies have examined the multi-temporal Sentinel-2 images for mapping LCC of mangrove forests. With field collected leaf SPAD values (relative chlorophyll content), this study explored the relationship of leaf SPAD values against five types of newly-developed VIs derived from leaf hyperspectral data and Sentinel-2 data of four periods (May 2018, January 2019, August 2019, and December 2019). Linear regression with best-performing VIs and Kernel Ridge Regression (KRR) were developed to construct the SPAD retrieval model in each period. The leave-one-out cross-validation technique was employed to compare the estimation results of VIs and KRR method, and the four periods of SPAD maps were produced by the best-performing model. The results showed that the newly-developed index (ratio of single-band reflectance to the sum of two bands reflectance, RSSI) achieved the high correlation coefficient with leaf SPAD value at both leaf and canopy level. At canopy level, the linear model using RSSI (B8/(B2 + B5), B8a/(B2 + B4), B8/(B2 + B5), and B8/(B2 + B3)) outperformed than that using traditional broadband indices and KRR model with R2adjust = 0.496, 0.742, 0.681, and 0.801; RMSE = 5.75, 4.29, 4.00, and 3.46; and RE = 7.67%, 5.68%, 4.97%, and 4.63% in each period. We concluded that there are great potentials of newly-developed index of RSSI using Sentinel-2 data for regional retrieving and mapping LCC of mangrove forests across different time periods, which is essential for mangrove ecological conservation and restoration.