Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala - Flores-Anderson, Africa I.; Griffin, Robert; Dix, Margaret; Romero-Oliva, Claudia S.; Ochaeta, Gerson; Skinner-Alvarado, Juan; Ramirez Moran, Maria Violeta; Hernandez, Betzy; Cherrington, Emil; Page, Benjamin; Barreno, Flor (Frontiers in Environmental Science, 2020)
In this study we evaluated the applicability of a space-borne hyperspectral sensor, Hyperion, to resolve for chlorophyll a (Chl a) concentration in Lake Atitlan, a tropical mountain lake in Guatemala. In situ water quality samples of Chl a concentration were collected and correlated with water surface reflectance derived from Hyperion images, to develop a semi-empirical algorithm. Existing operational algorithms were tested and the continuous bands of Hyperion were evaluated in an iterative manner. A third order polynomial regression provided a good fit to model Chl a. The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chl a concentrations in Lake Atitlán during the dry season, with a relative error of 33%. This analysis confirmed the suitability of hyperspectral imagers like Hyperion, to model Chl a concentrations in Lake Atitlán. This study also highlights the need to test and update this algorithm with operational multispectral sensors such as Landsat and Sentinel-2.