Estimating Particulate Inorganic Carbon Concentrations of the Global Ocean From Ocean Color Measurements Using a Reflectance Difference Approach. Issue 11 (13th November 2017)

• Record Type: Journal Article
• Authors: Mitchell, C.; Hu, C.; Bowler, B.; Drapeau, D.; Balch, W. M.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 11(2017)
• Page Start: 8707
• Page End: 8720
• Publication Date: 2017-11-13
• Subjects: particulate inorganic carbon -- coccolithophores -- remote sensing; Oceanography -- Periodicals; 551.4605
• DOI: 10.1002/2017JC013146
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: A new algorithm for estimating particulate inorganic carbon (PIC) concentrations from ocean color measurements is presented. PIC plays an important role in the global carbon cycle through the oceanic carbonate pump, therefore accurate estimations of PIC concentrations from satellite remote sensing are crucial for observing changes on a global scale. An extensive global data set was created from field and satellite observations for investigating the relationship between PIC concentrations and differences in the remote sensing reflectance ( Rrs ) at green, red, and near‐infrared (NIR) wavebands. Three color indices were defined: two as the relative height of Rrs (667) above a baseline running between Rrs (547) and an Rrs in the NIR (either 748 or 869 nm), and one as the difference between Rrs (547) and Rrs (667). All three color indices were found to explain over 90% of the variance in field‐measured PIC. But, due to the lack of availability of Rrs (NIR) in the standard ocean color data products, most of the further analysis presented here was done using the color index determined from only two bands. The new two‐band color index algorithm was found to retrieve PIC concentrations more accurately than the current standard algorithm used in generating global PIC data products. Application of the new algorithm to satellite imagery showed patterns on the global scale as revealed from field measurements. The new algorithm was more resistant to atmospheric correction errors and residual errors in sun glint corrections, as seen by a reduction in the speckling and patchiness in the satellite‐derived PIC images. Plain Language Summary: The oceans are full of plant life which provides food for all the larger animals in the oceans. This plant life is really small and you can only see individual plants with a microscope. However, when there is a lot of this plant life in one place, it can change the color of the ocean so much that we can see it from a ship, a plane or even from satellites. We call these plants algae, or phytoplankton. Just like on the land, where there are lots of different types of plant, there are lots of different types of phytoplankton. We are interested in one particular type, which has a chalk outer shell, causing the ocean to turn a milky blue when there are lots of them growing together. These chalk covered phytoplankton play a major role in regulating carbon in the oceans, and so it is important to know both where these phytoplankton are and how many of them there are. We have developed a new way to estimate how much chalk is in the ocean from satellite observations to help us estimate where these chalk covered phytoplankton are. Key Points: Extensive field observations are used to develop a new particulate inorganic carbon algorithm The new algorithm provides more accurate estimation of PIC compared to the standard algorithm White cap, sun glint, and atmospheric correction error artifacts are reduced in satellite imagery