{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:46:04Z","timestamp":1776278764167,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFD0900805"],"award-info":[{"award-number":["2019YFD0900805"]}]},{"name":"National Key R&amp;D Program of China","award":["42176175"],"award-info":[{"award-number":["42176175"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFD0900805"],"award-info":[{"award-number":["2019YFD0900805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42176175"],"award-info":[{"award-number":["42176175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate habitat prediction is important to improve fishing efficiency. Most of the current habitat-prediction studies use the single-source datasets and the sequence model based on single-source datasets, which, to a certain extent, limits the further improvement of prediction accuracy. In this paper, we propose a habitat-prediction method based on the multi-source heterogeneous remote-sensing data fusion, using product-level remote-sensing data and L1B-level original remote-sensing data. We designed a heterogeneous data feature extraction model based on a Convolution Neural Network (CNN) and Long and Short-Term Memory network (LSTM), and we designed a decision-fusion model based on multi-source heterogeneous data feature extraction. In the habitat prediction for the Northwest Pacific Saury, the mean R2 of the model reaches 0.9901 and the RMSE decreases to 0.01588 in the model validation experiment. It is significantly better than the results of other models, with the single datasets as input. Moreover, the model performs well in the generalization experiment because we limited the prediction error to less than 8%. Compared with the single-source sequence network model in the existing literature, the proposed method in this paper solves the problem of ineffective fusion caused by the differences in the structure and size of heterogeneous data through multilevel feature fusion and decision fusion, and it deeply explores the features of remote-sensing fishery data with different data structures and sizes. It can effectively improve the accuracy of fishery prediction, proving the feasibility and advancement of using multi-source remote-sensing data for habitat prediction. It also provides new methods and ideas for future research in the field of habitat prediction.<\/jats:p>","DOI":"10.3390\/rs14195061","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:50:01Z","timestamp":1665449401000},"page":"5061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Habitat Prediction of Northwest Pacific Saury Based on Multi-Source Heterogeneous Remote Sensing Data Fusion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0682-9157","authenticated-orcid":false,"given":"Yanling","family":"Han","sequence":"first","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6327-7726","authenticated-orcid":false,"given":"Zhenling","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-9808","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-1066","authenticated-orcid":false,"given":"Zhonghua","family":"Hong","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","first-page":"61","article-title":"Saury Resource and Fishing Grounds in the Northwest Pacific","volume":"26","author":"Shan","year":"2004","journal-title":"Mar. Fish."},{"key":"ref_2","first-page":"112","article-title":"Exploration of Saury in the Northwest Pacific","volume":"25","author":"Sun","year":"2003","journal-title":"Mar. Fish."},{"key":"ref_3","unstructured":"Zavolokin, A. (2022, February 06). Priority Species [EB\/OL]. [2018-06-09]. Available online: https:\/\/www.npfc.int\/priority-species."},{"key":"ref_4","unstructured":"Meng, L.W. (2017). Study on Fishery Forecast Research of Cololabis Saira in North Pacific Ocean Based on Habitat Model. [Master\u2019s Thesis, Shanghai Ocean University]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1007\/s13131-019-1486-3","article-title":"Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM","volume":"38","author":"Hua","year":"2019","journal-title":"Acta Oceanol. Sinica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1080\/01431160600987878","article-title":"Validation of integrated potential fishing zone (IPFZ) forecast using satellite based chlorophyll and sea surface temperature along the east coast of India","volume":"28","author":"Choudhury","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","first-page":"609","article-title":"Fishing ground distribution of saury and its correlation with SST in the Northern Pacific high sea in 2010","volume":"21","author":"Yan","year":"2012","journal-title":"J. Shanghai Ocean. Univ."},{"key":"ref_8","first-page":"811","article-title":"Review of the life history, resources and fishing grounds of the Pacific saury in the North Pacific Ocean","volume":"26","author":"Hua","year":"2019","journal-title":"J. Fish. Sci. China"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1111\/j.1365-2419.2004.00314.x","article-title":"Modeling the influence of oceanic-climatic changes on the dynamics of Pacific saury in the northwestern Pacific using a life cycle model","volume":"13","author":"Tian","year":"2004","journal-title":"Fish. Oceanogr."},{"key":"ref_10","first-page":"888","article-title":"Forecasting Pacific saury (Cololabis saira) fisheries based on GAM and weighted analysis in the northwest Pacific","volume":"28","author":"Liu","year":"2021","journal-title":"J. Fish. Sci. China"},{"key":"ref_11","first-page":"158","article-title":"Fishing ground forecasting of Thunnus alalunga in Indian Ocean based on random forest","volume":"35","author":"Chen","year":"2013","journal-title":"Acta Oceanol. Sinica"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mugo, R., and Saitoh, S.-I. (2020). Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models. Remote Sens., 12.","DOI":"10.3390\/rs12162591"},{"key":"ref_13","first-page":"1007","article-title":"Impacts of temporal and spatial scale as well as environmental data on fishery forecasting models for Illex argentinus in the southwest Atlantic","volume":"22","author":"Wang","year":"2015","journal-title":"J. Fish. Sci. China"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1093\/icesjms\/fsy065","article-title":"Deep learning models for the prediction of small-scale fisheries catches: Finfish fishery in the region of \u201cBah\u00eda Magadalena-Almejas\u201d","volume":"75","year":"2018","journal-title":"ICES J. Mar. Sci."},{"key":"ref_15","first-page":"423","article-title":"Fishing ground forecast model of albacore tuna based on fully convolutional networks in the South Pacific","volume":"36","author":"Yuan","year":"2020","journal-title":"Jiangsu J. Agr. Sci."},{"key":"ref_16","first-page":"435","article-title":"Fishery forecasting in the fishing ground based on dual-modal deep learning model","volume":"37","author":"Yuan","year":"2021","journal-title":"Jiangsu J. Agr. Sci."},{"key":"ref_17","first-page":"773","article-title":"Fishing ground distribution of saury and its correlation with marine environment factors in the Northern Parcific high sea in 2013","volume":"24","author":"Zhang","year":"2015","journal-title":"J. Shanghai Ocean. Univ."},{"key":"ref_18","unstructured":"Zhu, H.P. (2021). Construction of Fishing Ground Forecast Model of Ommastrephes bartramii in Northwest Pacific Based on Convolutional Neural Network. [Master\u2019s Thesis, Shanghai Ocean University]."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mammel, M., Naimullah, M., Vayghan, A.H., Hsu, J., Lee, M.A., Wu, J.H., Wang, Y.C., and Lan, K.W. (2022). Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14122932"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.fishres.2015.11.026","article-title":"Spatio-temporal distributions and habitat hot spots of the winter-spring cohort of neon flying squid Ommastrephes bartramii in relation to oceanographic conditions in the Northwest Pacific Ocean","volume":"175","author":"Yu","year":"2016","journal-title":"Fish. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/S0165-7836(00)00306-4","article-title":"Generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery","volume":"53","author":"Walsh","year":"2001","journal-title":"Fish. Res."},{"key":"ref_22","first-page":"101456","article-title":"Prediction of monthly Hilsa (Tenualosa ilisha) catch in the Northern Bay of Bengal using Bayesian structural time series model","volume":"39","author":"Giri","year":"2020","journal-title":"Reg. Stud. Mar. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s12562-017-1161-6","article-title":"The skipjack tuna fishery in the west-central Pacific Ocean: Applying neural networks to detect habitat preferences","volume":"84","author":"Wang","year":"2018","journal-title":"Fish. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1080\/01431161.2015.1042121","article-title":"Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, M., Yang, H., and Kim, J. (2020). Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model. Remote Sens., 12.","DOI":"10.3390\/rs12213654"},{"key":"ref_26","first-page":"74","article-title":"Prediction of Thunnus alalunga fishery based on fusion deep learning model","volume":"46","author":"Yuan","year":"2019","journal-title":"Fish. Mod."},{"key":"ref_27","first-page":"450","article-title":"Comparative study on the forecasting models of squid fishing ground in the northwest Pacific Ocean based on BP artificial neural network","volume":"26","author":"Wei","year":"2017","journal-title":"J. Shanghai Ocean. Univ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5061\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:49:32Z","timestamp":1760143772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/5061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":27,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14195061"],"URL":"https:\/\/doi.org\/10.3390\/rs14195061","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,10]]}}}