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

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.

Application of New Hyperspectral Sensors in the Remote Sensing of Aquatic Ecosystem Health: Exploiting PRISMA and DESIS for Four Italian Lakes

“…To date, most of the applications developed for inland water quality monitoring and management have been based on multispectral and mid (e.g., Landsat constellation, Sentinel-2-MSI) to coarse (e.g., ENVISAT-MERIS, Sentinel-3-OLCI) spatial resolution satellites [25]. While research efforts are still ongoing to face the challenges typical of EO of inland waters, such as global chlorophyll-a concentration mapping or corrections for adjacency effects [26][27][28][29][30], it is also true that depending on an optical sensor's specifications, the EO-derived products vary from turbidity, transparency, and concentrations of chlorophyll, suspended particulate matter and colored dissolved organic matter, floating materials and, in the case of shallow waters, bottom depth and type. In the literature of the past and recent years, numerous articles and reviews [31] and reference therein addressed the optical water quality parameters that can be retrieved by remote sensing techniques (i.e., suspended sediments (turbidity), chlorophyll and other secondary pigments, color dissolved organic matter (CDOM), water clarity and temperature) (e.g., [13,32,33]), the different properties of sensors and platforms and their environmental applications (e.g., [34][35][36]) and the algorithms developed and implemented to retrieve water quality products (e.g., [37][38][39]).…”

Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China

“…Different processors are required to achieve the threshold criterion of 30% reflectance error across all bands of sensors according to the Global Climate Observing System [7]. The use of AC processors for inland waters to obtain accurate satellite-derived aquatic reflectance remains challenging [13][14].…”

“…In particular, the European Space Agency (ESA) officially provides the Sen2Cor processor and Case 2 Regional Coast Color (C2RCC) processor, of which the latter was designed for water bodies in Sentinel-2 toolboxes [25]. Although these conveniences for the use of bottom of atmosphere (BOA) products of the MSI sensor were provided directly, the performance of these processors was unstable or unappreciated for all types of inland water bodies [13][14]. ESA, in coordination with the Copernicus Global Land Service program, conducted an iCOR exercise and water quality product generation (e.g., trophic state index, turbidity, etc.)…”

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

“…Recent progress in water quality remote sensing has shown that water quality parameters, such as chlorophyll-A, colored dissolved organic matter, turbidity, total phosphorus, total nitrogen, and heavy metal pollution can be detected by satellite images (e.g., from Landsat, Sentinel, and MODIS). Water quality parameters indicate the health of a water body and the potential risks of fish and other aquatic organisms [30], and are used as important parameters in limnology and oceanography to evaluate the nutritional status and ecological health of the aquatic environment [31]. Since aquaculture heavily relies on water quality (e.g., dissolved oxygen, ammonia nitrogen, and nitrite) to maintain the optimal growth conditions for fish products to ensure the best yield [32], it may be an effective approach to using water quality parameters in the extraction of aquaculture ponds.…”