Abstract
The 10-year archive of MEdium Resolution Imaging Spectrometer (MERIS) data is an invaluable resource for studies on lake system dynamics at regional and global scales. MERIS data are no longer actively acquired but their capacity for global scale monitoring of lakes from satellites will soon be re-established through the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development and validation of in-water algorithms for the accurate retrieval of biogeochemical parameters is thus of key importance if the potential of MERIS and OLCI data is to be fully exploited for lake monitoring. This study presents the first extensive validation of algorithms for chlorophyll-a (chl-a) retrieval by MERIS in the highly turbid and productive waters of Lake Balaton, Hungary. Six algorithms for chl-a retrieval from MERIS over optically complex Case 2 waters, including band-difference and neural network architectures, were compared using the MERIS archive for 2007–2012. The algorithms were locally-tuned and validated using in situ chl-a data (n=289) spanning the five year processed image time series and from all four lake basins. In general, both band-difference algorithms tested (Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI)) performed well, whereas the neural network processors were generally found to much less accurately retrieve in situ chl-a concentrations. The Level 1b FLH algorithm performed best overall in terms of chl-a retrieval (R2=0.87; RMSE=4.19mgm−3; relative RMSE=30.75%) and particularly at chl-a concentrations of ≥10mgm−3 (R2=0.85; RMSE=4.81mgm−3; relative RMSE=20.77%). However, under mesotrophic conditions (i.e., chl-a<10mgm−3) FLH was outperformed by the locally-tuned FUB/WeW processor (relative FLH RMSE<10mgm−3=57.57% versus relative FUB/WeW RMSE<10mgm−3=46.96%). An ensemble selection of in-water algorithms is demonstrated to improve chl-a retrievals.
Highlights
The optical complexity inherent to lakes and other inland waters poses many challenges to the accurate retrieval of biogeochemical parameters using satellite remote sensing (IOCCG, 2000, 2006)
Many standard chlorophyll-a retrieval algorithms originally developed for open ocean waters tend to fail when applied to more turbid inland and coastal waters whose optically properties are strongly influenced by non-covarying concentrations of non-algal particles (NAP) and coloured dissolved organic matter (CDOM) (IOCCG, 2006; Matthews, 2011)
The relationships obtained between chl-a concentrations retrieved by some of the investigated algorithms and in situ chl-a concentrations reported for other sites, elsewhere in the literature and over similar chl-a concentration ranges as Balaton are reported in Table 6, and are compared with relationships obtained for Balaton between in situ concentrations and the processors' default chl-a products (prior to local tuning using a_pig(443) so as to be comparable with the other studies)
Summary
The optical complexity inherent to lakes and other inland waters poses many challenges to the accurate retrieval of biogeochemical parameters using satellite remote sensing (IOCCG, 2000, 2006). Overlying inland and coastal waters and the proximity of the adjacent land surface means that standard approaches to atmospheric correction over ocean waters are not always reliable. In view of these challenges, there is a clear need to develop and validate atmospheric (Moore, Aiken, & Lavender, 1999) and in-water (Doerffer & Schiller, 2007, 2008; Matthews, 2011; Odermatt, Gitelson, Brando, & Schaepman, 2012) algorithms for use in highly turbid inland and coastal waters. Palmer et al / Remote Sensing of Environment 157 (2015) 158–169
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