Progress on ocean-color remote sensing of particulate organic carbon
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Particulate organic carbon(POC) is an important variable in studying carbon cycle in the ocean.Ocean-color remote sensing of POC provides a useful means for monitoring the variation of carbon cycle on a large scale.This paper is a review on the progress of ocean-color retrieval of POC from satellite data at home and abroad.A brief introduction was given first about sample collecting methods of POC and error analysis.Bio-optical relationships between POC and optical properties were then described in detail.Present ocean-color algorithms for retrieving POC were categorized and compared,and their applications in detecting multi-scale variability of marine ecosystem were summarized.Research perspectives on future study of POC retrieval from ocean color in China were proposed at last.Keywords:
Ocean color
Particulate organic carbon
Carbon fibers
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The general goal of this project was to characterize spatial distributions at basin scales and variability on monthly to interannual timescales of particulate organic carbon (POC) in the high-latitude oceans. The primary objectives were: (1) To collect in situ data in the north polar waters of the Atlantic and in the Southern Ocean, necessary for the derivation of POC ocean color algorithms for these regions. (2) To derive regional POC algorithms and refine existing regional chlorophyll (Chl) algorithms, to develop understanding of processes that control bio-optical relationships underlying ocean color algorithms for POC and Chl, and to explain bio-optical differentiation between the examined polar regions and within the regions. (3) To determine basin-scale spatial patterns and temporal variability on monthly to interannual scales in satellite-derived estimates of POC and Chl pools in the investigated regions for the period of time covered by SeaWiFS and MODIS missions.
SeaWiFS
Ocean color
Particulate organic carbon
Temporal scales
High latitude
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Ocean color remote sensing is an important tool to monitor water quality and biogeochemical conditions of ocean.Atmospheric correction, which obtains water-leaving radiance from the total radiance measured by satellite-borne or airborne sensors, remains a challenging task for coastal waters due to the complex optical properties of aerosols and ocean waters.In this paper, we report a research algorithm on aerosol and ocean color retrieval with emphasis on coastal waters, which uses coupled atmosphere and ocean radiative transfer model to fit polarized radiance measurements at multiple viewing angles and multiple wavelengths.Ocean optical properties are characterized by a generalized bio-optical model with direct accounting for the absorption and scattering of phytoplankton, colored dissolved organic matter (CDOM) and non-algal particles (NAP).Our retrieval algorithm can accurately determine the water-leaving radiance and aerosol properties for coastal waters, and may be used to improve the atmospheric correction when apply to a hyperspectral ocean color instrument.
Ocean color
Atmospheric optics
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Ocean color
Marine ecosystem
Biogeochemical Cycle
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Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate—the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.
Moderate-resolution imaging spectroradiometer
Ocean color
Particulate organic carbon
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Particulate organic carbon
Carbon fibers
Carbon flux
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Citations (71)
SeaWiFS, the sea-viewing wide field-of-view sensor, will bring to the ocean community a welcomed and improved renewal of the ocean color remote sensing capability that was lost when the Nimbus-7 coastal zone color scanner (CZCS) ceased operating in 1986. Because of the role of phytoplankton in the global carbon cycle, data obtained from SeaWiFS will be used to assess the ocean's role in the global carbon cycle, as well as in other biogeochemical cycles. SeaWiFS data will be used to help determine the magnitude and variability of the annual cycle of primary production by marine phytoplankton and to determine the distribution and timing of spring blooms. The observations will help to visualize the dynamics of ocean and coastal currents, the physics of mixing, and the relationship between ocean physics and large-scale patterns of productivity. The data from SeaWiFS will help fill the gap in ocean biological observations between those of CZCS and those of the moderate resolution imaging spectrometer (MODIS) on the Earth Observing Satellite-A (EOS-A).
SeaWiFS
Ocean color
Biogeochemical Cycle
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Open water
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SeaWiFS
Ocean color
Biogeochemical Cycle
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The suspended particulate matter (SPM) concentration (unit: mg l-1) in surface waters is an essential measure of water quality and clarity. Satellite remote sensing provides a powerful tool to derive the SPM with synoptic and repeat coverage. In this study, we developed a new global SPM algorithm utilizing the remote sensing reflectance (Rrs (λ)) at near-infrared (NIR), red, green, and blue bands (NIR-RGB) as input. The evaluations showed that the NIR-RGB algorithm could predict SPM with the median absolute percentage difference of ∼35%-39% over a wide range from ∼0.01 to >2,000 mg l-1. The uncertainty is smaller (29%-37%) for turbid waters where Rrs (671) ≥ 0.0012 sr-1 and slightly higher (41%-44%) for clear waters where Rrs (671) < 0.0012 mg l-1. The algorithm was implemented with the global Rrs (λ) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We provided a brief characterization of the spatial distribution and temporal trends of the SPM products in global waters based on the monthly SPM composites. Case studies of the SPM time series in coastal and inland waters suggest that the satellite SPM estimations registered spatial and seasonal variation and episodic events in regional scales as well. The VIIRS-generated global SPM maps provide a valuable addition to the existing ocean color products for environmental and climate applications.
Ocean color
RGB color model
SeaWiFS
Physical oceanography
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Citations (60)