Dataset
BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) for between 1997-2021 at 9 km spatial resolution (derived from the Ocean Colour Climate Change Initiative version 5.0 dataset)
Abstract
The BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) between 1997-2021 at 9km spatial resolution.
Particulate inorganic carbon (PIC) data were generated using a random forest approach that incorporates the following key input variables: remote sensing reflectances (Rrs) at 560 and 665 nm, chlorophyll-a concentration, colour index, and maximum waterclass values. The Rrs(560), Rrs(665), and chlorophyll-a concentration data were obtained directly from the Ocean Colour Climate Change Initiative (OC-CCI) version 5.0. The colour index values were estimated using Mitchell et al. (2017) algorithm: Rrs(560) minus Rrs(665). The maximum waterclass values were estimated using fourteen optical waterclasses obtained from the OC-CCI version 5.0. The PIC data are provided as netCDF files containing global, month PIC concentration at 9 km spatial resolution (1997-2021). For more details on the algorithm and its validation, please see the BICEP algorithm theoretical basline document (https://bicep-project.org/Home).
A related dataset based on the ESA Ocean Colour Climate Change Initiative v5.0 data is also available (see link in the related records section).
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| Access rules: |
Please contact CEDA for information about how to access these data.
For data use licensing information please contact: support@ceda.ac.uk. |
| Data lineage: |
The particulate inorganic carbon data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO). |
| Data Quality: |
BICEP/NCEO: We developed a random forest machine learning approach to estimate global particulate inorganic concentration (PIC) concentrations from satellite OC-CCI version 5.0 data. A large set of in situ PIC data were used to validate the random forest PIC approach. The proposed random forest PIC approach was also compared with existing candidate PIC algorithm (Mitchell et al. 2017). Our results show that the random forest method can retrieve PIC concentrations relatively well across different water types. For more detail of the random forest PIC approach, please see the please see the BICEP report (https://bicep-project.org/Home)
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| File Format: |
Not defined
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Related Documents
| BICEP Project Page |
Process overview
| Title | Computation for BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) for between 1997-2021 at 9 km spatial resolution (derived from the Ocean Colour Climate Change Initiative version 5.0 dataset) |
| Abstract | Computation of the particulate inorganic carbon (PIC) were generated using a random forest approach that incorporates the following key input variables: remote sensing reflectances (Rrs) at 560 and 665 nm, chlorophyll-a concentration, colour index, and maximum waterclass values. The Rrs(560), Rrs(665), and chlorophyll-a concentration data were obtained directly from the Ocean Colour Climate Change Initiative (OC-CCI) version 5.0. The colour index values were estimated using Mitchell et al. (2017) algorithm: Rrs(560) minus Rrs(665). The maximum waterclass values were estimated using fourteen optical waterclasses obtained from the OC-CCI version 5.0. The PIC data are provided as netCDF files containing global, month PIC concentration at 9 km spatial resolution (1997-2021). For more details on the algorithm and its validation, please see the BICEP algorithm theoretical basline document (https://bicep-project.org/Home) |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
No variables found.
Temporal Range
1997-09-01T00:00:00
2021-12-31T23:59:59
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
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-90.0000° |