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. 2021 Nov:265:112651.
doi: 10.1016/j.rse.2021.112651.

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll- a and turbidity algorithms

Mark A Warren  1 , Stefan G H Simis  1 , Nick Selmes  1
Affiliations

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll- a and turbidity algorithms

Mark A Warren et al. Remote Sens Environ. 2021 Nov.

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.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
(a) Map showing lake locations used in the analysis. (b) reproduction from Spyrakos et al. (2018) showing the 13 inland optical water types.
Fig. 2
Fig. 2
a: reflectance distributions after filtering dataset for each chl-a algorithm as labelled, for the bands used in that algorithm. b: reflectance distributions after filtering dataset for each wavelength as labelled, for the band used in that version of the Nechad algorithm. c: density plots (log scale) of OLCI vs MSI Rw for all data (unfiltered).
Fig. 3
Fig. 3
Density plots showing residual chl-a versus distance to land for the four chl-a algorithms. The colour denotes the number of observations per cell.
Fig. 4
Fig. 4
Band ratios for the (a) 490: 560 nm (b) max(443,490): 560 nm and (c) 708: 665 nm wavebands. Note that pixels with density of fewer than 100 are not shown. The dotted line shows unity and the solid line regression.
Fig. 5
Fig. 5
Panel of results showing (column 1) optimized log chl-a plots, (column 2) residuals prior to optimisation and (column 3) residuals after optimisation. Each row is for a different algorithm (OC2, OC3, Gilerson and Gons05 from top to bottom). Dashed lines on residual plots show the 5th and 95th percentiles. Solid and dashed lines on the density plots show the regression and line of unity respectively.
Fig. 6
Fig. 6
(a) Density plot of per-lake optimized log chl-a using 16 different parametrizations – one for each lake. (b) Density plot of optimized log chl-a using the median of the per-lake coefficients.
Fig. 7
Fig. 7
Dominant optical water type clustering of OC2 derived chl-a observations. Shading denotes the density of points per-pixel.
Fig. 8
Fig. 8
Log chl-a plot of the OC2 algorithm when using the scaled MSI band ratio with OLCI coefficients (OC2scale). Dashed line is the line of unity.
Fig. 9
Fig. 9
Spatial overview of the median residual (a) before and (b) after optimisation of the OC2 algorithm, (c) the number of observations and (d) the modal dominant optical water type per pixel for Lake Victoria.
Fig. 10
Fig. 10
Spatial overview of the median residual (a) before and (b) after optimisation of the OC3 algorithm, (c) the number of observations and (d) the modal dominant optical water type per pixel for Lake Victoria.
Fig. 11
Fig. 11
Spatial overview of the median residual (a) before and (b) after optimisation of the Gilerson algorithm, (c) the number of observations and (d) the modal dominant optical water type per pixel for Lake Sasyk.
Fig. 12
Fig. 12
Spatial overview of the median residual (a) before and (b) after optimisation of the Gons05 algorithm, (c) the number of observations and (d) the modal dominant optical water type per pixel for Lake Sasyk.
Fig. 13
Fig. 13
(a) Distribution of residual turbidity values calculated from 665, 708, 778 and 865 nm bands.
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