Volume 42 Issue 6
Jun.  2023
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Liang Chang, Yanli Yang, Xinjun Chen, Wei Yu, Yangdong Li, Guiping Feng, Yang Zhang. Assessment of prediction model of the CPUE of neon flying squid with different sources of remote sensing data[J]. Acta Oceanologica Sinica, 2023, 42(6): 33-38. doi: 10.1007/s13131-022-2049-6
Citation: Liang Chang, Yanli Yang, Xinjun Chen, Wei Yu, Yangdong Li, Guiping Feng, Yang Zhang. Assessment of prediction model of the CPUE of neon flying squid with different sources of remote sensing data[J]. Acta Oceanologica Sinica, 2023, 42(6): 33-38. doi: 10.1007/s13131-022-2049-6

Assessment of prediction model of the CPUE of neon flying squid with different sources of remote sensing data

doi: 10.1007/s13131-022-2049-6
Funds:  The National Key Research and Development Program of China under contract No. 2019YFD0901404; the National Natural Science Foundation of China under contract No. 42174016; the Shanghai Science and Technology Innovation Action Plan under contract No. 19DZ1207502; the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources under contract No. QNHX2324.
More Information
  • Corresponding author: E-mail: xjchen@shou.edu.cn
  • Received Date: 2022-02-18
  • Accepted Date: 2022-04-15
  • Available Online: 2023-01-18
  • Publish Date: 2023-06-25
  • Accurately building the relationship between the oceanographic environment and the distribution of neon flying squid (Ommastrephes bartramii) is very important to understand the potential habitat pattern of O. bartramii. However, when building the prediction model of O. bartramii with traditional oceanographic variables (e.g., chlorophyll a concentration (Chl a) and sea surface temperature (SST)) from space-borne observations, part of the important spectrum characteristics of the oceanic surface could be masked by using the satellite data products directly. In this study, the neglected remote sensing information (i.e., spectral remote sensing reflectance (Rrs) and brightness temperature (BT)) is firstly incorporated to build the prediction model of catch per unit effort (CPUE) of O. bartramii from July to December during 2014–2018 in the Northwest Pacific Ocean. Results show that both the conventional oceanographic variables and the neglected remote sensing data are suitable for building the prediction model, whereas the overall root mean square error (RMSE) of the predicted CPUE of O. bartramii with the former is typically less accurate than that with the latter. Hence, the Rrs and BT could be a more suitable data source than the Chl a and SST to predict the distribution of O. bartramii, highlighting that the potential value of the neglected variables in understanding the habitat suitability of O. bartramii.
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