Abstract
Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.
Highlights
Satellite observation of ocean-colour radiometry involves detection of spectral variations in the water-leaving radiance, which is the sunlight backscattered out of the ocean after interaction with water and its constituents
Applying an algorithm to average upwelling radiance observed over a pixel ∼1 km2 with a nominal penetration depth of ∼10 m, and comparing the result with an in situ observation based on 1 L of water represents a scale difference of 1:10−10. This raises concerns on how representative an in situ sample of a 1 km2 body of water is. This issue was investigated by Brewin et al (2016) who used measurements from two Atlantic Meridional Transect (AMT) cruises between 50◦ N and 50◦ S of along-track particulate absorption calibrated against HPLC–chl-a to obtain multiple in situ measurements within a satellite ‘pixel.’ With this approach they found that the uncertainty in satellite retrievals reduced to 0.157 log10chl-a on average, which is less than half that reported from previous global studies
Radiometric calibration As noted by International Ocean Colour Coordinating Group [IOCCG] (2013) ‘uncertainty requirements for scientific applications, e.g., 5% absolute in the blue and 1% relative, or 30% on chl-a concentration, radiometric calibration should be accurate to a fraction of 1%.’
Summary
Satellite observation of ocean-colour radiometry involves detection of spectral variations in the water-leaving radiance (or reflectance), which is the sunlight backscattered out of the ocean after interaction with water and its constituents. Because the water signal is needed to within 5% (GCOS, 2016), the spaceborne sensor needs to retrieve TOA signal to 0.5% (International Ocean Colour Coordinating Group [IOCCG], 2012) and, be well calibrated to avoid residual errors propagating to the water signal. This review starts with a summary of the user community requirements, discusses current and upcoming satellite EO missions and in situ data, including citizen science/crowdsourcing, that support the ocean-colour sensors. It describes ground segments, which take the raw ocean-colour data to generate intermediary or end products for users and provide data delivery and access mechanisms. It does not cover polarimetry or LiDAR observations that are both described in Jamet et al (2019)
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