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1. A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two

   https://doi.org/10.5194/essd-11-1037-2019  

   Valente, André; Sathyendranath, Shubha; Brotas, Vanda; Groom, Steve; Grant, Michael; Taberner, Malcolm; Antoine, David; Arnone, Robert; Balch, William M.; Barker, Kathryn; et al (January 2019, Earth System Science Data)

   Abstract. A global compilation of in situ data is useful to evaluate thequality of ocean-colour satellite data records. Here we describe the datacompiled for the validation of the ocean-colour products from the ESA OceanColour Climate Change Initiative (OC-CCI). The data were acquired fromseveral sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.Observations of the following variables were compiled: spectralremote-sensing reflectances, concentrations of chlorophyll a, spectralinherent optical properties, spectral diffuse attenuation coefficients andtotal suspended matter. The data were from multi-project archives acquiredvia open internet services or from individual projects, acquired directlyfrom data providers. Methodologies were implemented for homogenization,quality control and merging of all data. No changes were made to theoriginal data, other than averaging of observations that were close in timeand space, elimination of some points after quality control and conversionto a standard format. The final result is a merged table designed forvalidation of satellite-derived ocean-colour products and available in textformat. Metadata of each in situ measurement (original source, cruise orexperiment, principal investigator) was propagated throughout the work andmade available in the final table. By making the metadata available,provenance is better documented, and it is also possible to analyse each setof data separately. This paper also describes the changes that were made tothe compilation in relation to the previous version (Valente et al., 2016).The compiled data are available athttps://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019). 

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2. An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

   https://doi.org/10.3390/s19194285  

   Sathyendranath, Shubha; Brewin, Robert; Brockmann, Carsten; Brotas, Vanda; Calton, Ben; Chuprin, Andrei; Cipollini, Paolo; Couto, André; Dingle, James; Doerffer, Roland; et al (October 2019, Sensors)

   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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3. 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

   https://doi.org/10.1007/s10661-021-09684-w  

   Schaeffer, Blake; Salls, Wilson; Coffer, Megan; Lebreton, Carole; Werther, Mortimer; Stelzer, Kerstin; Urquhart, Erin; Gurlin, Daniela (March 2022, Environmental Monitoring and Assessment)

   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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4. Evaluation of Vertical Patterns in Chlorophyll‐A Derived From a Data Assimilating Model of Satellite‐Based Ocean Color

   https://doi.org/10.1029/2023EA003378  

   Arteaga, Lionel A; Rousseaux, Cecile S (July 2024, Earth and Space Science)

   Abstract Satellite‐based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC‐Argo floats are now filling the information gap at depth. Here we use BGC‐Argo data to assess depth‐resolved information on chlorophyll‐a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data‐assimilating model replicates well the general seasonality and meridional gradients in surface and depth‐resolved chlorophyll‐a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float‐based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non‐iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m⁻³, which should allow the detection of seasonal changes in float‐based biomass (varying between 0.01 and >1 mg Chl m⁻³) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth‐resolved observations. Uncertainties in float bio‐optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color. 

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5. Raw files from flight RU_BP_TR2_FL012R of the uncrewed aerial vehicle remote sensing imagery of postfire vegetation in Siberian larch forests 2018-2019

   https://doi.org/10.18739/A2K35MF52  

   Loranty, Michael; Forbath, Elena; Talucci, Anna; Alexander, Heather; DeMarco, Jennie; Hewitt, Rebecca; Lichstein, Jeremy; Mack, Michelle; Paulson, Alison; McEwan, Ryan (January 2021, NSF Arctic Data Center)

   This data set contains the raw files from flight RU_ALN_TR1_FL007R. The remote sensing imagery is collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018). Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27. 

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