Journal of Marine Sciences ›› 2023, Vol. 41 ›› Issue (1): 82-95.DOI: 10.3969-j.issn.1001-909X.2023.01.007

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Satellite retrieval algorithm of high spatial resolution sea surface partial pressure of CO2: Application of machine learning in Xiangshan Bay in autumn

LIU Tingyu1(), BAI Yan1,2,*(), ZHU Bozhong1,2, LI Teng1, GONG Fang1   

  1. 1. Second Institute of Oceanography, MNR, Hangzhou 310012, China
    2. Ocean College, Zhejiang University,Zhoushan 316021, China
  • Received:2022-12-27 Revised:2023-02-01 Online:2023-03-15 Published:2023-04-28


Coastal bays are greatly affected by human activities and natural changes, and the influence mechanism of variation in seawater carbon source and sink patterns is extremely complex. Due to the small spatial scale of the bay, it is necessary to use wide-bands high-spatial resolution satellite remote sensing for monitoring the air-sea CO2 flux. Compared with the traditional kilometer-level ocean color satellite data, the retrieval of the sea surface partial pressure of CO2 (pCO2), the key parameter to calculate air-sea CO2 flux, is extremely challenging in small-scale bays. Taking Xiangshan Bay in Zhejiang Province in autumn as an example, a satellite retrieval algorithm for sea surface pCO2 was proposed based on the in situ pCO2 data and Sentinel-2 satellite images in the past five years, using the machine learning method of support vector machine (SVM). The algorithm validation results showed a good performance with R2 of 0.92 and RMSE of 23.23 μatm, and the satellite-derived results were consistent with the in situ values. On this basis, the satellite products of pCO2 in Xiangshan Bay in autumn from 2017 to 2021 (September to November) were produced. The results revealed that the pCO2 of Xiangshan Bay showed a decreasing trend from the top of the bay to the mouth of the bay, with an average value of 514.56 μatm, of which the average pCO2 in the inner bay was 551.94 μatm and the average pCO2 in the outer bay was 477.19 μatm, which implied that Xiangshan Bay was a source of atmospheric CO2 as a whole. There was no significant trend change of pCO2 in autumn in the past five years. Combined with the analysis of in situ data of multiple parameters, it was found that the sea surface pCO2 of autumn in Xiangshan Bay in 2021 was jointly regulated by physical mixing and biological activities. Sea surface temperature (SST) had a good positive correlation with pCO2, which was mainly reflected by the thermodynamic equilibrium of carbonate system. In addition, the normalized pCO2(NpCO2) with average temperature had a good negative correlation with seawater salinity and dissolved oxygen saturation. The relationship between NpCO2 and salinity resulted from the exchange of sea water inside the bay and offshore coastal water under tidal effect. Long-time series satellite data analysis also confirmed that sea surface pCO2 had a relatively consistent trend with the average tide height inside and outside the bay, and this trend was stronger in the outside bay than that in the inner bay. In this study, a set of pCO2 remote sensing retrieval methods in the small-scale bay was constructed, which laid a good foundation for the subsequent long-time series satellite monitoring of sea-air CO2 fluxes.

Key words: coastal bay, sea surface pCO2, support vector machines, high spatial resolution satellite remote sensing, Xiangshan Bay

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