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Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes
Abstract Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll- a (chl- a , µg L −1 ) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl- a with in-water algorithms. Validation of chl- a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl- a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH (P) ) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl- a data. The best performance based on mean absolute multiplicative error (MAE mult ) was demonstrated by the merged algorithm referred to as C 15 −M 10 (MAE mult = 1.8, bias mult = 0.97, n = 836). In the C 15 −M 10 algorithm, the MPH (P) chl- a value was retained if it was > 10 µg L −1 ; if the MPH (P) value was ≤ 10 µg L −1 , the C2RCC value was selected, as long as that value was < 15 µg L −1 . Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl- a algorithms previously reported independently. Additionally, it contributes to the transition of chl- a algorithm maturity by quantifying error statistics for a number of locations and times.
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- Award ID(s):
- 2025982
- PAR ID:
- 10399281
- Date Published:
- Journal Name:
- Environmental Monitoring and Assessment
- Volume:
- 194
- Issue:
- 3
- ISSN:
- 0167-6369
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Green Lake is the deepest natural inland lake in Wisconsin, with a maximum depth of about 72 meters. In the early 1900s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity) with low dissolved oxygen (DO) concentrations occurring in only the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake have increased; these changes have led to increased algal production and low DO concentrations not only in the deepest areas but also in the middle of the water column (metalimnion). The U.S. Geological Survey has routinely monitored the lake since 2004 and its tributaries since 1988. Results from this monitoring led the Wisconsin Department of Natural Resources (WDNR) to list the lake as impaired because of low DO concentrations in the metalimnion, and they identified elevated TP concentrations as the cause of impairment. 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Macroinvertebrates are collected from selected shoreline and deep water locations in the seven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, and unnamed lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) in the Trout Lake area using modified Hester-Dendy samplers. Samplers are placed at fyke net and gill net locations in August and retrieved 3-4 weeks later. Macroinvertebrates are preserved in ethanol. This dataset contains counts of various groups of macroinvertebrates identified from specific samples. The majority of the identifications are at the genus level. The data table "Benthic Macroinvertebrate Codes" identifies the taxonomic group represented by each group code. Taxonomic references: Ecology and Classification of North American Freshwater Invertebrates, Edited by James H Thorp and Alan P Covich, Academic Press, Inc, 1991; Aquatic Insects of Wisconsin, William L Hilsenhoff, Natural History Museums Council, University of Wisconsin-Madison (1995). Sampling Frequency: annually Number of sites: 7. Samplers were set in all seven lakes in 1981-1989,1992 and 1993. Only Trout, Sparkling, and Crystal Lakes were sampled in 1990, 1991, and 1994 to present. No lakes were sampled in 2020 or 2021.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10661-021-09684-w
