{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T03:27:34Z","timestamp":1764646054847,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2015,5,29]],"date-time":"2015-05-29T00:00:00Z","timestamp":1432857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.<\/jats:p>","DOI":"10.3390\/rs70607105","type":"journal-article","created":{"date-parts":[[2015,5,29]],"date-time":"2015-05-29T10:37:21Z","timestamp":1432895841000},"page":"7105-7125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0680-5427","authenticated-orcid":false,"given":"Jining","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2766-0845","authenticated-orcid":false,"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Computer Science, China University of Geoscience, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lajiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingjun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bomin","family":"Huang","sequence":"additional","affiliation":[{"name":"Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, L., Chen, L., Ma, Y., Zhu, X., and Chu, B. (2013, January 21\u201326). Application of DDDAS in Marine Oil Spill Management: A New Framework Combining Multiple Source Remote Sensing Monitoring and Simulation as a Symbiotic Feedback Control System. Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723842"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1029\/2011EO060001","article-title":"Tracking the Deepwater Horizon oil spill: A modeling perspective","volume":"92","author":"Liu","year":"2011","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1016\/j.marpolbul.2011.09.018","article-title":"Preliminary study on responses of marine nematode community to crude oil contamination in intertidal zone of Bathing Beach, Dalian","volume":"62","author":"Lv","year":"2011","journal-title":"Mar. Pollut. Bull"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.marpolbul.2013.03.028","article-title":"Satellite observations and modeling of oil spill trajectories in the Bohai Sea","volume":"71","author":"Xu","year":"2013","journal-title":"Mar. Pollut. Bull"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2012.03.024","article-title":"State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill","volume":"124","author":"Leifer","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.marpolbul.2014.03.059","article-title":"Review of oil spill remote sensing","volume":"83","author":"Fingas","year":"2014","journal-title":"Mar. Pollut. Bull"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Plaza, J., P\u00e9rez, R., Plaza, A., Mart\u00ednez, P., and Valencia, D. (2005). Mapping oil spills on sea water using spectral mixture analysis of hyperspectral image data. Proc. SPIE, 5995.","DOI":"10.1117\/12.631149"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6297","DOI":"10.1080\/01431160802175587","article-title":"A GIS approach to mapping oil spills in a marine environment","volume":"29","author":"Ivanov","year":"2008","journal-title":"Int. J. Remote Sens"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"236","DOI":"10.3390\/s8010236","article-title":"Advances in remote sensing for oil spill disaster management: state-of-the-art sensors technology for oil spill surveillance","volume":"8","author":"Jha","year":"2008","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.3390\/rs70101112","article-title":"Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery","volume":"7","author":"Pisano","year":"2015","journal-title":"Remote Sens"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.3390\/rs3122630","article-title":"Oil detection in a coastal marsh with polarimetric synthetic aperture radar (SAR)","volume":"3","author":"Ramsey","year":"2011","journal-title":"Remote Sens"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jhazmat.2003.11.009","article-title":"A high-level synthesis of oil spill response equipment and countermeasures","volume":"107","author":"Ventikos","year":"2004","journal-title":"J. Hazard. Mater"},{"key":"ref_13","first-page":"777","article-title":"Application of an Eulerian-Lagrangian oil spill modeling system to the Prestige accident: trajectory analysis","volume":"1","author":"Azevedo","year":"2009","journal-title":"J. Coast. Res"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Weisberg, R.H., Hu, C., and Zheng, L. (2011). Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise, Elsevier.","DOI":"10.1029\/GM195"},{"key":"ref_15","first-page":"1","article-title":"Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record Breaking Enterprise","volume":"195","author":"Liu","year":"2011","journal-title":"Geophysical Monograph Series"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.5194\/os-8-1105-2012","article-title":"Predictions for oil slicks detected from satellite images using MyOcean forecasting data","volume":"8","author":"Zodiatis","year":"2012","journal-title":"Ocean Sci"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.5194\/gmd-6-1871-2013","article-title":"MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting\u2014Part 2: Numerical simulations and validations","volume":"6","author":"Pinardi","year":"2013","journal-title":"Geosci. Model Dev"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11270-007-9413-1","article-title":"Oil spill simulation and validation in the Arabian (Persian) Gulf with special reference to the UAE coast","volume":"184","author":"Elhakeem","year":"2007","journal-title":"Water Air Soil Pollut"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Darema, F. (2004, January 6\u20139). Dynamic data driven applications systems: A new paradigm for application simulations and measurements. Krak\u00f3w, Poland.","DOI":"10.1007\/978-3-540-24688-6_86"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1029\/2003EO330002","article-title":"MODIS detects oil spills in Lake Maracaibo, Venezuela","volume":"84","author":"Hu","year":"2003","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1029\/2012EO160001","article-title":"Polarimetric synthetic aperture radar utilized to track oil spills","volume":"93","author":"Migliaccio","year":"2012","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_22","unstructured":"Zhang, Y., Li, Y., and Lin, H. (2014). Advanced Geoscience Remote Sensing, InTech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Amoon, M., Bozorgi, A., and Rezai-rad, G.A. (2013). New method for ship detection in synthetic aperture radar imagery based on the human visual attention system. J. Appl. Remote Sens, 7.","DOI":"10.1117\/1.JRS.7.071599"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/LGRS.2007.907174","article-title":"Classifiers and confidence estimation for oil spill detection in ENVISAT ASAR images","volume":"5","author":"Brekke","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/17538940902918723","article-title":"Comparison between radarsat-1 SAR different data modes for oil spill detection by a fractal box counting algorithm","volume":"2","author":"Marghany","year":"2009","journal-title":"Int. J. Digit. Earth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2012.11.019","article-title":"SAR imaging of ocean surface oil seep trajectories induced by near inertial oscillation","volume":"130","author":"Li","year":"2013","journal-title":"Remote Sens. Environ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1109\/JSTARS.2012.2182760","article-title":"Ship and oil-spill detection using the degree of polarization in linear and hybrid\/compact dual-pol SAR","volume":"5","author":"Shirvany","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/17538947.2012.695404","article-title":"Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China","volume":"6","author":"Lu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, X., Guo, J., Guo, M., Hu, X., Tang, C., Wang, C., and Xing, Q. (2014). Modelling of oil spill trajectory for 2011 Penglai 19-3 coastal drilling field, China. Appl. Math. Model.","DOI":"10.1016\/j.apm.2014.10.063"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/LGRS.2011.2168598","article-title":"Remote-Sensing image denoising using partial differential equations and auxiliary images as priors","volume":"3","author":"Liu","year":"2012","journal-title":"IEEE. Geosci. Remote Sens. Lett"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1016\/j.optlaseng.2013.02.001","article-title":"Restoration of multispectral images by total variation with auxiliary image","volume":"7","author":"Liu","year":"2013","journal-title":"Opt. Lasers Eng"},{"key":"ref_33","unstructured":"(2011). 2011 Bohai Bay Oil Spill Accident Investigation Report by the Joint Investigation Team, The China State Oceanic Administration. in Chinese."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/JSTARS.2012.2201249","article-title":"Segmentation of oil spill images with illumination-reflectance based adaptive level set model","volume":"5","author":"Ganta","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"083553","DOI":"10.1117\/1.JRS.8.083553","article-title":"Adaptive stochastic minimization for measuring marine oil spill extent in synthetic aperture radar images","volume":"8","author":"Moctezuma","year":"2014","journal-title":"J. Appl. Remote Sens"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/JSTARS.2012.2186630","article-title":"A statistical approach for automatic detection of ocean disturbance features from SAR images","volume":"5","author":"Chaudhuri","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_37","unstructured":"Blumberg, A.F., and Mellor, G.L. (1987). Three-Dimensional Coastal Ocean Models, American Geophysical Union."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1007\/s11434-012-5355-0","article-title":"Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem","volume":"58","author":"Guo","year":"2013","journal-title":"Chin. Sci. Bull"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1016\/S0025-326X(02)00178-9","article-title":"Vertical mixing of oil droplets by breaking waves","volume":"44","author":"Tkalich","year":"2002","journal-title":"Mar. Pollut. Bull"},{"key":"ref_40","first-page":"502","article-title":"Numerical model research on Emergency Warning & Predicting system of ocean oil spill: I. Research on predicting of ocean dynamical factors","volume":"5","author":"Mu","year":"2011","journal-title":"Mar. Sci. Bull"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Denham, M., Cort\u00e9s, A., Margalef, T., and Luque, E. (2008, January June). Applying a dynamic data driven genetic algorithm to improve forest fire spread prediction. Krak\u00f3w, Poland.","DOI":"10.1007\/978-3-540-69389-5_6"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Madey, G.R., Barab\u00e1si, A., Chawla, N.V., Gonzalez, M., Hachen, D., Lantz, B., Pawling, A., Schoenharl, T., Szab\u00f3, G., and Wang, P. (2007, January 27\u201330). Enhanced situational awareness: Application of DDDAS concepts to emergency and disaster management. Beijing, China.","DOI":"10.1007\/978-3-540-72584-8_143"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Darema, F. (2005, January 22\u201325). Dynamic data driven applications systems: New capabilities for application simulations and measurements. Atlanta, GA, USA.","DOI":"10.1007\/11428848_79"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MCSE.2012.89","article-title":"DDDAS-Based Parallel Simulation of Threat Management for Urban Water Distribution Systems","volume":"16","author":"Wang","year":"2014","journal-title":"Comput. Sci. Eng"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1109\/JSTARS.2013.2244061","article-title":"Oil spill mapping and measurement in the Gulf of Mexico with Textural Classifier Neural Network Algorithm (TCNNA)","volume":"6","author":"MacDonald","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4705","DOI":"10.1080\/01431160801891770","article-title":"Dark formation detection using neural networks","volume":"29","author":"Topouzelis","year":"2008","journal-title":"Int. J. Remote Sens"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"C09013","DOI":"10.1029\/2010JC006837","article-title":"Evaluation of trajectory modeling in different dynamic regions using normalized cumulative Lagrangian separation","volume":"116","author":"Liu","year":"2011","journal-title":"J. Geophys. Res.: Oceans"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1002\/2013JC009710","article-title":"Evaluation of altimetry-derived surface current products using Lagrangian drifter trajectories in the eastern Gulf of Mexico","volume":"119","author":"Liu","year":"2014","journal-title":"J. Geophys. Res.: Oceans"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s10236-012-0576-y","article-title":"Observation-based evaluation of surface wave effects on currents and trajectory forecasts","volume":"62","author":"Christensen","year":"2012","journal-title":"Ocean Dyn"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/6\/7105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:47:11Z","timestamp":1760215631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/6\/7105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,29]]},"references-count":49,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2015,6]]}},"alternative-id":["rs70607105"],"URL":"https:\/\/doi.org\/10.3390\/rs70607105","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2015,5,29]]}}}