Remote sensing retrievals of particulate organic carbon in the Yangtze Estuary from OLCI and its response to extreme weather events
ID:707 View Protection:ATTENDEE Updated Time:2025-01-01 00:47:33 Hits:712 Poster Presentation

Start Time:2025-01-16 18:50(Asia/Shanghai)

Duration:15min

Session:S24 Session 24-Estuaries and Coastal Environments Stress - Observations and Modelling » S24-PEstuaries and Coastal Environments Stress - Observations and Modelling

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Abstract
The Yangtze Estuary provides a critical link between the land and ocean, transporting vast amounts of terrestrial particulate organic carbon (POC) to the marginal East China Sea, thereby exerting significant influence on the coastal carbon cycles. Here, using paired in situ remote-sensing reflectance (Rrs) and POC data collected during 2021-2023, we developed a robust POC retrieval algorithm to accurately estimate POC from ocean color remote sensing using the convolutional neural network (CNN) method (with mean absolute percent differences, MAPD, less than 15%). Application of the algorithm to multi-year satellite Sentinel-3 OLCI imagery captured the impact of Yangtze inputs, wind regimes, and climate extremes (both typhoons and extreme floods) on POC fluxes at the estuarine scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in river delta-marginal sea systems.
Keywords
satellite ocean color, particulate organic carbon, remote sensing retrieval, extreme events
Speaker
Qingwen Wang
Master East China Normal University

Submission Author
Fang Cao East China Normal University
Qingwen Wang East China Normal University
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Important Date
  • Conference Date

    Jan 13

    2025

    to

    Jan 17

    2025

  • Sep 27 2024

    Draft paper submission deadline

  • Feb 17 2025

    Registration deadline

Sponsored By
State Key Laboratory of Marine Environmental Science, Xiamen University
Organized By
State Key Laboratory of Marine Environmental Science, Xiamen University
Department of Earth Sciences, National Natural Science Foundation of China
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