Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a ret...

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Autores principales:	Michael A. Dallosch, Irena F. Creed
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optical water types phytoplankton algal blooms Landsat water quality lakes Science Q
Acceso en línea:	https://doaj.org/article/73c9876e8f6740a1b6c3091fe50cdd29
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spelling	oai:doaj.org-article:73c9876e8f6740a1b6c3091fe50cdd292021-11-25T18:54:41ZOptimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types10.3390/rs132246072072-4292https://doaj.org/article/73c9876e8f6740a1b6c3091fe50cdd292021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4607https://doaj.org/toc/2072-4292The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-<i>a</i>) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-<i>a</i> retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-<i>a</i> were built for each OWT. The performances of chl-<i>a</i> retrieval algorithms within each OWT were compared to those of global chl-<i>a</i> algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-<i>a</i>:turbidity ratio; turbid lakes with a mixture of high chl-<i>a</i> and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-<i>a</i> and turbidity measurements; and eutrophic lakes with a high chl-<i>a</i>:turbidity ratio. With one exception (r<sup>2</sup> = 0.26, <i>p</i> = 0.08), the best performing algorithm in each OWT showed improvement (r<sup>2</sup> = 0.69–0.91, <i>p</i> < 0.05), compared with the best performing algorithm for all lakes combined (r<sup>2</sup> = 0.52, <i>p</i> < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-<i>a</i> over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.Michael A. DalloschIrena F. CreedMDPI AGarticleoptical water typesphytoplanktonalgal bloomsLandsatwater qualitylakesScienceQENRemote Sensing, Vol 13, Iss 4607, p 4607 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optical water types phytoplankton algal blooms Landsat water quality lakes Science Q
spellingShingle	optical water types phytoplankton algal blooms Landsat water quality lakes Science Q Michael A. Dallosch Irena F. Creed Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
description	The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-<i>a</i>) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-<i>a</i> retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-<i>a</i> were built for each OWT. The performances of chl-<i>a</i> retrieval algorithms within each OWT were compared to those of global chl-<i>a</i> algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-<i>a</i>:turbidity ratio; turbid lakes with a mixture of high chl-<i>a</i> and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-<i>a</i> and turbidity measurements; and eutrophic lakes with a high chl-<i>a</i>:turbidity ratio. With one exception (r<sup>2</sup> = 0.26, <i>p</i> = 0.08), the best performing algorithm in each OWT showed improvement (r<sup>2</sup> = 0.69–0.91, <i>p</i> < 0.05), compared with the best performing algorithm for all lakes combined (r<sup>2</sup> = 0.52, <i>p</i> < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-<i>a</i> over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.
format	article
author	Michael A. Dallosch Irena F. Creed
author_facet	Michael A. Dallosch Irena F. Creed
author_sort	Michael A. Dallosch
title	Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
title_short	Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
title_full	Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
title_fullStr	Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
title_full_unstemmed	Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types
title_sort	optimization of landsat chl-<i>a</i> retrieval algorithms in freshwater lakes through classification of optical water types
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/73c9876e8f6740a1b6c3091fe50cdd29
work_keys_str_mv	AT michaeladallosch optimizationoflandsatchliairetrievalalgorithmsinfreshwaterlakesthroughclassificationofopticalwatertypes AT irenafcreed optimizationoflandsatchliairetrievalalgorithmsinfreshwaterlakesthroughclassificationofopticalwatertypes
_version_	1718410596505878528

Optimization of Landsat Chl-<i>a</i> Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

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