Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-a using Landsat TM/ETM+ over turbid waters
Remote sensing of chlorophyll-a is challenging in water containing inorganic suspended sediments (i.e. non-volatile suspended solids, NVSS) and coloured dissolved organic matter (CDOM). The effects of NVSS and CDOM on empirical remote-sensing estimates of chlorophyll-a
in inland waters have not been determined on a broad spatial and temporal scale. This study evaluated these effects using a long-term (1989–2012) data set that included chlorophyll-a, NVSS, and CDOM from 39 reservoirs across Missouri (USA). Model comparisons indicated that the
machine-learning algorithm BRT (boosted regression trees, validation Nash–Sutcliffe coefficient = 0.350) was better than linear regression (validation Nash–Sutcliffe coefficient = 0.214) for chlorophyll-a estimate using Landsat Thematic Mapper (TM) and
Enhanced Thematic Mapper Plus (ETM+) imagery. Only a small proportion of BRT model residuals could be explained by sediments or CDOM, and the observed trends in BRT residuals were different from the theoretical effects expected from NVSS and CDOM. Our results also indicated a small systematic
bias by the BRT model, but it was not likely caused by NVSS or CDOM.
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Document Type: Research Article
Department of Integrative Biology, Michigan State University, East Lansing, MI, USA
Department of Geography, Environment, and Spatial Sciences, and Center of Global Change & Earth Observation, Michigan State University, East Lansing, MI, USA
School of Natural Resources, University of Missouri, Columbia, MO, USA
Publication date: March 4, 2018
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