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Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method
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Fangfang Wu1, 2, Zhiyi Fu1, 2, Linshu Hu1, 2, Feng Zhang1, 2, *, Zhenhong Du1, 2, Renyi Liu1, 2
Haiyang Xuebao | 2021, 43(9) : 126 - 136
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Haiyang Xuebao | 2021, 43(9): 126-136
• Article •
Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method
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Fangfang Wu1, 2, Zhiyi Fu1, 2, Linshu Hu1, 2, Feng Zhang1, 2, *, Zhenhong Du1, 2, Renyi Liu1, 2
Affiliations
  • 1Zhejiang Provincial Key Laboratory of Geographic Information Science, Zhejiang University, Hangzhou 310028, China
  • 2School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Published: 2021-09-25 doi: 10.12284/hyxb2021146
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Salinity is an important parameter to characterize physical and biogeo-chemical processes. Optical satellite images with high resolution can avoid radio frequency interference, and provide a feasible way to monitor sea surface salinity (SSS) in coastal regions. Using an extensive dataset of ship-based SSS and MODIS estimated remote sensing reflectance (Rrs) at 412 nm, 443 nm, 488 nm, 555 nm and 667 nm and sea surface temperature (SST) a random forest (RF) model has been utilized to retrieve the SSS. Based on the predicted SSS, we analyze the spatiotemporal heterogeneity of SSS in the Gulf of Mexico and contribution of each factor with correlations coefficient. The results show that: (1) the RF model can accurately estimate the SSS in the Gulf of Mexico (RMSE=0.335, R2=0.931); (2) the spatial distribution pattern of SSS shows a ring-shaped inward value increase, which is affected by river discharge, wind forcing and circulation; (3) there is a strong correlation between SSS and SST, and SST significantly impact in retrieving SSS; (4) the correlation between SST, Rrs and SSS appears spatial heterogeneity.

sea surface salinity  /  random forest  /  spatial-temporal heterogeneity  /  Gulf of Mexico
Fangfang Wu, Zhiyi Fu, Linshu Hu, Feng Zhang, Zhenhong Du, Renyi Liu. Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method[J]. Haiyang Xuebao, 2021 , 43 (9) : 126 -136 . DOI: 10.12284/hyxb2021146
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Article Info
doi: 10.12284/hyxb2021146
  • Receive Date：2021-02-03
  • Online Date：2026-02-26
  • Published：2021-09-25
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  • Received：2021-02-03
  • Revised：2021-06-16
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    1Zhejiang Provincial Key Laboratory of Geographic Information Science, Zhejiang University, Hangzhou 310028, China
    2School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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