{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:32:48Z","timestamp":1760229168979,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Russian Science Foundation","award":["19-77-10074"],"award-info":[{"award-number":["19-77-10074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Onshore seeps are recognized as strong sources of methane (CH4), the second most important greenhouse gas. Seeps actively emitting CH4 were recently found in floodplains of West Siberian rivers. Despite the origin of CH4 in these seeps is not fully understood, they can make substantial contribution in regional greenhouse gas emission. We used high-resolution satellite Sentinel-2 imagery to estimate seep areas at a regional scale. Convolutional neural network based on U-Net architecture was implemented to overcome difficulties with seep recognition. Ground-based field investigations and unmanned aerial vehicle footage were coupled to provide reliable training dataset. The seep areas were estimated at 2885 km2 or 1.5% of the studied region; most seep areas were found within the Ob\u2019 river floodplain. The overall accuracy of the final map reached 86.1%. Our study demonstrates that seeps are widespread throughout the region and provides a basis to estimate seep CH4 flux in entire Western Siberia.<\/jats:p>","DOI":"10.3390\/rs14112661","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T04:46:23Z","timestamp":1654145183000},"page":"2661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mapping Onshore CH4 Seeps in Western Siberian Floodplains Using Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7798-0815","authenticated-orcid":false,"given":"Irina","family":"Terentieva","sequence":"first","affiliation":[{"name":"A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia"}]},{"given":"Ilya","family":"Filippov","sequence":"additional","affiliation":[{"name":"A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia"},{"name":"UNESCO Department \u201cEnvironmental Dynamics and Global Climate Changes\u201d, Ugra State University, Khanty-Mansiysk 628012, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6501-912X","authenticated-orcid":false,"given":"Aleksandr","family":"Sabrekov","sequence":"additional","affiliation":[{"name":"A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia"},{"name":"UNESCO Department \u201cEnvironmental Dynamics and Global Climate Changes\u201d, Ugra State University, Khanty-Mansiysk 628012, Russia"}]},{"given":"Mikhail","family":"Glagolev","sequence":"additional","affiliation":[{"name":"A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia"},{"name":"UNESCO Department \u201cEnvironmental Dynamics and Global Climate Changes\u201d, Ugra State University, Khanty-Mansiysk 628012, Russia"},{"name":"Faculty of Soil Science, Lomonosov Moscow State University, Moscow 119991, Russia"},{"name":"Institute of Forest Science, Russian Academy of Sciences, Moscow 143030, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108164","DOI":"10.1016\/j.ecolind.2021.108164","article-title":"Sizable carbon emission from the floodplain of Ob River","volume":"131","author":"Krickov","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s13157-018-1088-4","article-title":"Highly dynamic methane emission from the West Siberian boreal floodplains","volume":"39","author":"Terentieva","year":"2019","journal-title":"Wetlands"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.5194\/essd-12-1561-2020","article-title":"The global methane budget 2000\u20132017","volume":"12","author":"Saunois","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_4","unstructured":"Canadell, J.G., Monteiro, P.M., Costa, M.H., Da Cunha, L.C., Cox, P.M., Alexey, V., Henson, S., Ishii, M., Jaccard, S., and Koven, C. (2021). Global Carbon and Other Biogeochemical Cycles and Feedbacks, OceanRep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5944","DOI":"10.1128\/AEM.01539-14","article-title":"Gammaproteobacterial methanotrophs dominate cold methane seeps in floodplains of West Siberian rivers","volume":"80","author":"Oshkin","year":"2014","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sabrekov, A., Terentieva, I., Litti, Y., Glagolev, M., and Filippov, I. (2021, January 19\u201330). Methane emission from seeps of West Siberian middle taiga river floodplains. Proceedings of the 23rd EGU General Assembly, Online.","DOI":"10.5194\/egusphere-egu21-480"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1306\/01051009122","article-title":"Methanogenic biodegradation of petroleum in the West Siberian Basin (Russia): Significance for formation of giant Cenomanian gas pools","volume":"94","author":"Milkov","year":"2010","journal-title":"AAPG Bull."},{"key":"ref_8","unstructured":"Ulmishek, G.F. (2003). Petroleum Geology and Resources of the West Siberian Basin, Russia, US Department of the Interior, US Geological Survey Reston."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1038\/s41467-020-16229-1","article-title":"Influence of tectonics on global scale distribution of geological methane emissions","volume":"11","author":"Ciotoli","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/essd-11-1-2019","article-title":"Gridded maps of geological methane emissions and their isotopic signature","volume":"11","author":"Etiope","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4615","DOI":"10.5194\/bg-13-4615-2016","article-title":"Mapping of West Siberian taiga wetland complexes using Landsat imagery: Implications for methane emissions","volume":"13","author":"Terentieva","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"045214","DOI":"10.1088\/1748-9326\/6\/4\/045214","article-title":"Regional methane emission from West Siberia mire landscapes","volume":"6","author":"Glagolev","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"035201","DOI":"10.1088\/1748-9326\/6\/3\/035201","article-title":"Evaluation of methane emissions from West Siberian wetlands based on inverse modeling","volume":"6","author":"Kim","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_14","first-page":"16329","article-title":"Exploring the response of West Siberian wetland methane emissions to future changes in climate, vegetation, and soil microbial communities","volume":"10","author":"Bohn","year":"2013","journal-title":"Biogeosci. Discuss."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"GB3004","DOI":"10.1029\/2003GB002190","article-title":"A high-resolution GIS-based inventory of the west Siberian peat carbon pool","volume":"18","author":"Sheng","year":"2004","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2013.06.012","article-title":"The use of ALOS\/PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia","volume":"137","author":"Peregon","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.5194\/bg-12-3321-2015","article-title":"WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia","volume":"12","author":"Bohn","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.5194\/essd-13-2001-2021","article-title":"Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5151","DOI":"10.5194\/essd-13-5151-2021","article-title":"BAWLD-CH 4: A Comprehensive Dataset of Methane Fluxes from Boreal and Arctic Ecosystems","volume":"13","author":"Kuhn","year":"2021","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"075001","DOI":"10.1088\/1748-9326\/11\/7\/075001","article-title":"A process-based model of methane consumption by upland soils","volume":"11","author":"Sabrekov","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sabrekov, A.F., Danilova, O.V., Terentieva, I.E., Ivanova, A.A., Belova, S.E., Litti, Y.V., Glagolev, M.V., and Dedysh, S.N. (2021). Atmospheric Methane Consumption and Methanotroph Communities in West Siberian Boreal Upland Forest Ecosystems. Forests, 12.","DOI":"10.3390\/f12121738"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_23","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1134\/S0026261721050040","article-title":"Microbial Community Composition of Floodplains Shallow-Water Seeps in the Bolshaya Rechka Floodplain, Western Siberia","volume":"90","author":"Danilova","year":"2021","journal-title":"Microbiology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.5194\/essd-13-2595-2021","article-title":"Hydrometeorological dataset of West Siberian boreal peatland: A 10-year record from the Mukhrino field station","volume":"13","author":"Dyukarev","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.21105\/joss.02305","article-title":"geemap: A Python package for interactive mapping with Google Earth Engine","volume":"5","author":"Wu","year":"2020","journal-title":"J. Open Source Softw."},{"key":"ref_29","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python Tutorial, Centrum voor Wiskunde en Informatica."},{"key":"ref_30","unstructured":"QGIS Association (2021). QGIS Geographic Information System, QGIS Association."},{"key":"ref_31","unstructured":"Ho, T.K. (1995, January 14\u201315). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, J., Li, J., and Zhang, D.D. (2018). Multi-spectral water index (MuWI): A native 10-m multi-spectral water index for accurate water mapping on Sentinel-2. Remote Sens., 10.","DOI":"10.3390\/rs10101643"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.09.021","article-title":"Remote sensing of inland waters: Challenges, progress and future directions","volume":"157","author":"Palmer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 19\u201324). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"14629","DOI":"10.1038\/s41598-021-94190-9","article-title":"A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)","volume":"11","author":"Ghorbanzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dang, K.B., Nguyen, M.H., Nguyen, D.A., Phan, T.T.H., Giang, T.L., Pham, H.H., Nguyen, T.N., Tran, T.T.V., and Bui, D.T. (2020). Coastal wetland classification with deep U-Net convolutional networks and Sentinel-2 imagery: A case study at the Tien Yen estuary of Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12193270"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Torres, D.L., Turnes, J.N., Soto Vega, P.J., Feitosa, R.Q., Silva, D.E., Marcato Junior, J., and Almeida, C. (2021). Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images. Remote Sens., 13.","DOI":"10.3390\/rs13245084"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_46","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013). API design for machine learning software: Experiences from the scikit-learn project. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","article-title":"Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification","volume":"33","author":"Kaynig","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s00027-020-00729-9","article-title":"Challenges of predicting gas transfer velocity from wind measurements over global lakes","volume":"82","author":"Klaus","year":"2020","journal-title":"Aquat. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1098\/rsta.2010.0341","article-title":"Global atmospheric methane: Budget, changes and dangers","volume":"369","author":"Dlugokencky","year":"2011","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1525\/elementa.383","article-title":"Global geological methane emissions: An update of top-down and bottom-up estimates","volume":"7","author":"Etiope","year":"2019","journal-title":"Elem. Sci. Anthr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.5194\/bg-14-3715-2017","article-title":"Variability in methane emissions from West Siberia\u2019s shallow boreal lakes on a regional scale and its environmental controls","volume":"14","author":"Sabrekov","year":"2017","journal-title":"Biogeosciences"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:23:44Z","timestamp":1760138624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":51,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112661"],"URL":"https:\/\/doi.org\/10.3390\/rs14112661","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,6,2]]}}}