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Open AccessArticle

Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data

Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, 46.7 km of Athens-Sounio Avenue, 19013 Attica, Greece
Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Department of Geography, Harokopio University of Athens, El. Venizelou 70, 17671 Kallithea, Athens, Greece
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2087;
Received: 27 May 2020 / Revised: 23 June 2020 / Accepted: 24 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)
Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 8 Operational Land Imager (OLI) sensors. The first step of the methodology involves the implementation of stepwise multiple regression (MLR) analysis of the available Chl-a dataset. Then, principal component analysis (PCA) is performed to explore Greek lakes’ potential interrelationships based on their Chl-a values in conjunction with certain criteria: their characteristics (artificial/natural), typology, and climatic type. Additionally, parameters such as seasonal water sampling and the date difference between sampling and satellite overpass are taken into consideration. Next, is implemented a stepwise multiple regression analysis among different groups of cases, formed by the criteria indicated from the PCA itself. This effort aimed at exploring different remote sensing-derived Chl-a algorithms for various types of lakes. The practical use of the proposed approach was evaluated in a total of 50 lake water bodies (natural and artificial) from 2013–2018, constituting the National Lake Network Monitoring of Greece in the context of the Water Framework Directive (WFD). All in all, the results evidenced the suitability of Landsat data when used with the proposed technique to estimate log-transformed Chl-a. The proposed scheme resulted in the development of models separately for natural (R = 0.78; RMSE = 1.3 μg/L) and artificial lakes (R = 0.76; RMSE = 1.29 μg/L), while the model developed without criteria proved weaker (R = 0.65; RMSE = 1.85 μg/L) in comparison to the other ones examined. The methodological framework proposed herein can be used as a useful resource toward a continuous monitoring and assessment of lake water quality, supporting sustainable water resources management. View Full-Text
Keywords: Landsat; chlorophyll-a; PCA; MLR analysis; lakes; WFD Landsat; chlorophyll-a; PCA; MLR analysis; lakes; WFD
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MDPI and ACS Style

Markogianni, V.; Kalivas, D.; Petropoulos, G.P.; Dimitriou, E. Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data. Remote Sens. 2020, 12, 2087.

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