Raman, Mini and Nayak, Shailesh
(2023)
Remote Sensing-Based Estimation of Primary Production in the Arabian Sea.
In:
Dynamics of Planktonic Primary Productivity in the Indian Ocean.
Springer, pp. 245-280.
ISBN 9783031344664
Full text not available from this repository.
| Abstract: |
The key element in regulating the carbon dynamics of the oceans through biological processes is the microscopic free-floating autotrophic phytoplankton and associated rates of primary production. Accurate assessment of large-scale spatiotemporal dynamics of primary production by traditional platforms is frustrating due to limited spatial resolution and undersampling. By the virtue of its broad, synoptic coverage, ocean color imagery provides a two-dimensional window onto the dynamic state of phytoplankton biomass fields indexed as chlorophyll-a concentration. An important application of remotely sensed ocean data is the estimation of oceanic primary production. Compared with high seas, regional seas such as Arabian Sea are characterized by definite geographical boundaries encompassing coastal regions, continental shelves, and current systems. Estimation of primary production in the Arabian Sea from the Indian Ocean color monitor OCM-1 involved the use of a depth-integrated nonspectral model to compute the daily rate of euphotic zone primary production. The model driven by OCM-1derived chlorophyll data was operated with additional information on surface irradiance, light transmission in the water column, day length, and photosynthetic rate parameters, which accounts for the light capture and utilization by the phytoplankton. Euphotic zone primary production maps were generated covering the broad continental shelf, slope, and open ocean waters of the Arabian Sea, and computed values were validated with in situ measured rates of primary production. Statistical analysis indicated that the model explained 70% variance in the in situ dataset with a low negative bias of 3% and an overall uncertainity of 41.8% in the euphotic zone primary production estimates that was within the desired accuracy goal of 45% set by ocean color missions. The optimum performance of the model was due to region-specific chlorophyll algorithm (OC-OCM) for Arabian Sea as input compared to global chlorophyll algorithms such as OC2 and OC4. |
| Item Type: |
Book Chapter
|
| Subjects: |
School of Natural and Engineering Sciences > Environment |
| Divisions: |
Schools > Natural Sciences and Engineering |
| Date Deposited: |
09 May 2024 06:07 |
| Last Modified: |
09 May 2024 06:07 |
| Official URL: |
https://link.springer.com/chapter/10.1007/978-3-03... |
| Related URLs: |
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| Funders: |
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| Projects: |
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| DOI: |
https://doi.org/10.1007/978-3-031-34467-1_11 |
| URI: |
http://eprints.nias.res.in/id/eprint/2707 |
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