Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

Authors

Wesley J. Moses, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-LincolnFollow
Anatoly Gitelson, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-LincolnFollow
Richard L. Perk, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-LincolnFollow
Daniela Gurlin, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln
Donald C. Rundquist, Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln
Bryan C. Leavitt, University of Nebraska-LincolnFollow
Tadd M. Barrow, School of Natural Resources, University of Nebraska-Lincoln
Paul Brakhage, Nebraska Department of Environmental Quality

ORCID IDs

Anatoly Gitelson

Document Type

Article

Date of this Version

2012

Comments

This paper was published in Water Resources: vol. 46, issue 4, pages 993-1004.

Abstract

Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophylla concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll-a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll-a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll- a concentration, with a root mean square error of 5.54 mg m^-3 for chlorophylla concentrations in the range 2.27-81.17 mg m^-3.