Published January 3, 2024
| Version v1
Dataset
Open
Northern Indian Ocean 3-D Chlorophyll-a Concentration
Authors/Creators
- 1. Second Institute of Oceanography, Ministry of Natural Resources
- 2. School of Marine Technology and Geomatics, Jiangsu Ocean University
Description
This product consists of 3D fields of Chlorophyll-a concentration (Chla) in the northern Indian Ocean during 2000-2019. The reprocessed product is provided at 0.25°x0.25° horizontal resolution, over 32 levels from the surface to 200 m depth. The random forest (RF)-based method estimates the vertical distribution of Chla concentration from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers.
Files
Files
(2.0 GB)
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md5:e3e7bbfed99997d197d4e8a831bcfb46
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101.2 MB | Download |
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101.0 MB | Download |
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101.1 MB | Download |
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md5:841f0fb5ea52e28c9aec9fb495791af8
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100.3 MB | Download |
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md5:60d8e9d9e44a6ff8dbbb3a52692b7aea
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100.3 MB | Download |
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md5:3b5cc39db151592090869b0bc5e8ed13
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100.2 MB | Download |
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md5:9b140cc5c9490885858c9f2bbf2edea4
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100.3 MB | Download |
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md5:621caf72fa425cdb40fc8990a0420036
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100.2 MB | Download |
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md5:7dca27313ea51c71adf6c8d06c4e6d28
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100.0 MB | Download |
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md5:b56ef9e774682f5396dfdf3ede5a4f59
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100.3 MB | Download |
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100.2 MB | Download |
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md5:621d5fa51445c7169452bfc6b6065b00
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100.1 MB | Download |
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md5:2dc2d7100688cf97b6a02262df701a0d
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100.2 MB | Download |
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md5:eb782c723acc6fe7f4704189a4b6ca0f
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100.0 MB | Download |
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md5:c1ac63c5786e5f186c1a64664dfc8751
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100.3 MB | Download |
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md5:dd19ab3cefdb0c2120f8dd30697fb52f
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100.3 MB | Download |
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md5:c1a4e3a5f7402f6a002fd3a4373b408e
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99.9 MB | Download |
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md5:e54e8c40844abb8cbe890ab693ee2f8a
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100.3 MB | Download |
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md5:1a768735020070d3da92a65c3624adeb
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99.9 MB | Download |
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md5:8ec7ec9db2249e4c7edc209058e2854b
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100.1 MB | Download |
Additional details
References
- Hu, Q., X. Chen, Y. Bai, X. He, T. Li, and D. Pan. Reconstruction of 3-D Ocean Chlorophyll a Structure in the Northern Indian Ocean Using Satellite and BGC-Argo Data. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4200513.