Abstract
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 hyperspetral-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.
| Original language | English (US) |
|---|---|
| Article number | 7 |
| Journal | Frontiers in Environmental Science |
| Volume | 8 |
| DOIs | |
| State | Published - Feb 5 2020 |
Bibliographical note
Funding Information:The authors would like to thank Stu Frye from NASA Goddard Space Flight Center Greenbelt, MD, who tasked EO-1 to acquire images at the same time that in situ sampling was being carried out. We would also like to recognize the invaluable support provided by researchers in Guatemala, Estuardo Bocel, Juan Carlos Bocel, Isabel Arriola, Cesar Arriola, and the rest of the AMSCLAE team in 2013 who collaborated to collect in situ samples for this study. Thank you to Sundar Christopher who provided crucial guidance to perform this analysis. Thanks also to UAH faculty Tom Sever, Udaysankar Nair, Quingyan Han, John Christy, staff Michele Kennedy, Jan Williamson, Linda Berry, David Corredor, and David Cook. Additional support for subsequent research in this area provided by National Geographic and Microsoft AI grant.
Funding Information:
First and foremost, we would like to thank SERVIR leadership, Dan Irwin, Ashutosh Limaye, and NASA Applied Sciences, Capacity Building Program Manager, Nancy Searby, for their constant support to this work. Thanks also to USAID Climate Change office for their support to SERVIR, Jenny Frankle-Reed, Pete Epanchin, Carry Stokes, and Kevin Coffey. This work was based on the SERVIR thesis research, done by Africa Flores (Flores, 2013) under NASA-UAH Cooperative Agreement NNM11AA01A. The authors would like to thank Stu Frye from NASA Goddard Space Flight Center Greenbelt, MD, who tasked EO-1 to acquire images at the same time that in situ sampling was being carried out. We would also like to recognize the invaluable support provided by researchers in Guatemala, Estuardo Bocel, Juan Carlos Bocel, Isabel Arriola, Cesar Arriola, and the rest of the AMSCLAE team in 2013 who collaborated to collect in situ samples for this study. Thank you to Sundar Christopher who provided crucial guidance to perform this analysis. Thanks also to UAH faculty Tom Sever, Udaysankar Nair, Quingyan Han, John Christy, staff Michele Kennedy, Jan Williamson, Linda Berry, David Corredor, and David Cook. Additional support for subsequent research in this area provided by National Geographic and Microsoft AI grant.
Publisher Copyright:
© Copyright © 2020 Flores-Anderson, Griffin, Dix, Romero-Oliva, Ochaeta, Skinner-Alvarado, Ramirez Moran, Hernandez, Cherrington, Page and Barreno.
Keywords
- Guatemala
- Lake Atitlán
- chlorophyll a concentration
- hyperspectral remote sensing
- water quality
