Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

There is a need to develop indicators of mangrovecondition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangroveleaves and (ii) showed the potential of Landsat 8 for estimation of mangroveCC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R2 > 0.5). Indices with spectral bands around the red edge(705–753 nm) were more sensitive to mangroveleaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 mg·cm−2, and R2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangroveareas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edgesuch as Sentinel 2 will improve mangrovemonitoring at higher spatial and temporal resolutions.