Abstract
High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m−3 to 1.11 mg m−3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m−3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.
Highlights
There are over 100 million lakes and reservoirs (Verpoorter et al, 2014) with the vast majority not observable using the spatial resolution of current ocean colour sensors
For Lake Windermere, all concurrent MultiSpectral Imager (MSI)-Ocean and Land Colour Instrument (OLCI) observations were removed during the filtering process since all had fewer than five valid neighbours in the 3 × 3 macro-pixel, whereas Lake Douglas had no matching valid clear water after flag ging and atmospheric correction
If we assume the retrieval of reflectance from OLCI to be relatively more reliable, these results provide another indication that atmospheric correction of MSI over water should remain a research priority
Summary
There are over 100 million lakes and reservoirs (Verpoorter et al, 2014) with the vast majority not observable using the spatial resolution of current ocean colour sensors. 60/EC), which aim to record ambient surface water quality of inland and coastal water resources, is only feasible over large areas or hazardous regions when remote sensing is used (Papathanasopoulou et al, 2019). Lake water-leaving reflectance (LWLR) was recently adopted in the Lakes Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS 200, 2016). LWLR, or reflectance in short, describes the fate of sunlight entering the water column and al lows biogeochemical quantities such as chlorophyll-a (chl-a) or water transparency, turbidity, suspended particle concentration or dissolved organic matter, to be derived from diagnostic reflectance signatures. And consistently deriving these quantities in relatively small inland water bodies is a vital step for the water quality remote sensing research community, with progress made in regional studies And consistently deriving these quantities in relatively small inland water bodies is a vital step for the water quality remote sensing research community, with progress made in regional studies (e. g. Toming et al, 2016; Bresciani et al, 2019; Pahlevan et al, 2019)
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