4.7 Article

Estimating Particulate Inorganic Carbon Concentrations of the Global Ocean From Ocean Color Measurements Using a Reflectance Difference Approach

期刊

JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
卷 122, 期 11, 页码 8707-8720

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1002/2017JC013146

关键词

particulate inorganic carbon; coccolithophores; remote sensing

资金

  1. NASA [NNX14AQ43A, NNX14AM63G, NNX15AB13A, NNG04HZ25C, NNG04Gl11G, NNX08AJ88A, NNX07AD01G, NNX10AT67G, NNX11AO72G, NNX11AL93G, NAS5-31363, NNX14AM77G, NNX14AQ41G]
  2. NSF [OCE-0136541, OCE-0728582, OCE-0322074, S0993A-D, OCE-0961660]
  3. NASA [NNX08AJ88A, 100434, 141781, NNX11AL93G, 674507, NNX14AQ41G, 139997, NNX11AO72G, 808932, NNX15AB13A, 679079, NNX14AM63G, 122169, NNX10AT67G, NNX14AM77G, 678989] Funding Source: Federal RePORTER

向作者/读者索取更多资源

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 (R-rs) at green, red, and near-infrared (NIR) wavebands. Three color indices were defined: two as the relative height of R-rs(667) above a baseline running between R-rs(547) and an R-rs in the NIR (either 748 or 869 nm), and one as the difference between R-rs(547) and R-rs(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 R-rs(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.

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