{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T22:13:21Z","timestamp":1780611201116,"version":"3.54.1"},"reference-count":62,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T00:00:00Z","timestamp":1600646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["NO. XDA19030302"],"award-info":[{"award-number":["NO. XDA19030302"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41971314"],"award-info":[{"award-number":["No. 41971314"]}],"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>Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam\u2019s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.<\/jats:p>","DOI":"10.3390\/rs12183086","type":"journal-article","created":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T08:18:01Z","timestamp":1600676281000},"page":"3086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhe","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juhua","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingzhicheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyan","family":"Yu","sequence":"additional","affiliation":[{"name":"Plant and Environmental Sciences, New Mexico State University, Skeen Hall Room N127 Box 30003 MSC 3Q, Las Cruces, NM 88003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lirong","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stiller, D., Ottinger, M., and Leinenkugel, P. (2019). Spatio-Temporal Patterns of Coastal Aquaculture Derived from Sentinel-1 Time Series Data and the Full Landsat Archive. Remote Sens., 11.","DOI":"10.3390\/rs11141707"},{"key":"ref_2","unstructured":"Food and Agriculture Organization (FAO) (2016). The State of World Fisheries and Aquaculture 2016, FAO."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.agsy.2019.02.011","article-title":"To cluster or not to cluster farmers? Influences on network interactions, risk perceptions, and adoption of aquaculture practices","volume":"173","author":"Joffre","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"772","DOI":"10.3389\/fmars.2019.00772","article-title":"Site Suitability for Finfish Marine Aquaculture in the Central Mediterranean Sea","volume":"6","author":"Porporato","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.ocecoaman.2015.10.015","article-title":"Aquaculture: Relevance, distribution, impacts and spatial assessments\u2014A review","volume":"119","author":"Ottinger","year":"2016","journal-title":"Ocean Coast. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.ocecoaman.2012.10.006","article-title":"Sustainable shrimp farming in Bangladesh: A quest for an Integrated Coastal Zone Management","volume":"71","author":"Afroz","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/0305-750X(96)00033-2","article-title":"Shrimp Aquaculture Development and the Environment: People, Mangroves and Fisheries on the Gulf of Fonseca, Honduras","volume":"24","author":"Dewalt","year":"1996","journal-title":"World Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ocecoaman.2013.04.008","article-title":"Use of degraded coastal wetland in an integrated mangrove\u2013aquaculture system: A case study from the South China Sea","volume":"85","author":"Peng","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1016\/S0025-326X(03)00282-0","article-title":"A synthesis of dominant ecological processes in intensive shrimp ponds and adjacent coastal environments in NE Australia","volume":"46","author":"Burford","year":"2003","journal-title":"Mar. Pollut. Bull."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2016.12.016","article-title":"Assessment and analysis of the chlorophyll- a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002\u20132012)","volume":"190","author":"Loisel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scitotenv.2018.10.319","article-title":"Nutrient dynamics and eutrophication assessment in the tropical river system of Saigon\u2014Dongnai (southern Vietnam)","volume":"653","author":"Nguyen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/A:1008070400208","article-title":"Ecological engineering in aquaculture: Use of seaweeds for removing nutrients from intensive mariculture","volume":"11","author":"Troell","year":"1999","journal-title":"J. Appl. Phycol."},{"key":"ref_13","first-page":"101902","article-title":"Rapid expansion of coastal aquaculture ponds in China from Landsat observations during 1984\u20132016","volume":"82","author":"Ren","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"734666","DOI":"10.1016\/j.aquaculture.2019.734666","article-title":"Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone","volume":"520","author":"Duan","year":"2020","journal-title":"Aquaculture"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.ocecoaman.2008.06.002","article-title":"The performance of satellite images in mapping aquacultures","volume":"51","author":"Alexandridis","year":"2008","journal-title":"Ocean Coast. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.apgeog.2013.07.003","article-title":"Monitoring land cover dynamics in the Yellow River Delta from 1995 to 2010 based on Landsat 5 TM","volume":"44","author":"Ottinger","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/01431160701250374","article-title":"Auto-extraction technique-based digital classification of saltpans and aquaculture plots using satellite data","volume":"29","author":"Sridhar","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, Y., Deng, J., Ye, Z., Gan, M., Wang, K., Wu, J., Yang, W., and Xiao, G. (2019). Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features. Sustainability, 11.","DOI":"10.3390\/su11030637"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, F., Liu, K., Tang, H., Liu, L., and Liu, H. (2018). Analyzing Trends of Dike-Ponds between 1978 and 2016 Using Multi-Source Remote Sensing Images in Shunde District of South China. Sustainability, 10.","DOI":"10.3390\/su10103504"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.marpolbul.2017.05.056","article-title":"Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia","volume":"131","author":"Proisy","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/s11769-017-0926-2","article-title":"Remote Monitoring of Expansion of Aquaculture Ponds Along Coastal Region of the Yellow River Delta from 1983 to 2015","volume":"28","author":"Ren","year":"2018","journal-title":"Chin. Geogr. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10661-013-3360-7","article-title":"An object-based image analysis approach for aquaculture ponds precise mapping and monitoring: A case study of Tam Giang-Cau Hai Lagoon, Vietnam","volume":"186","author":"Virdis","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4470","DOI":"10.3390\/rs5094470","article-title":"Extraction of Coastline in Aquaculture Coast from Multispectral Remote Sensing Images: Object-Based Region Growing Integrating Edge Detection","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1080\/01431161.2019.1706009","article-title":"Research on a novel extraction method using Deep Learning based on GF-2 images for aquaculture areas","volume":"41","author":"Cheng","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J. (2019). Extracting Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure. Remote Sens., 11.","DOI":"10.3390\/rs11172053"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.08.121","article-title":"An Extreme Learning Machine based on Cellular Automata of edge detection for remote sensing images","volume":"198","author":"Han","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_27","first-page":"504","article-title":"Monitoring and impact assessment of shrimp farming in the East Coast of Thailand using remote sensing and GIS","volume":"33","author":"Hazarika","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.ocecoaman.2011.07.013","article-title":"Assessment of aquaculture impact on mangroves of Mahanadi delta (Orissa), East coast of India using remote sensing and GIS","volume":"54","author":"Pattanaik","year":"2011","journal-title":"Ocean Coast. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ottinger, M., Clauss, K., and Kuenzer, C. (2017). Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data. Remote Sens., 9.","DOI":"10.3390\/rs9050440"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2018.03.015","article-title":"Monitoring thirty years of small water reservoirs proliferation in the southern Brazilian Amazon with Landsat time series","volume":"145","author":"Arvor","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ecss.2018.08.016","article-title":"Spatial and temporal changes in mangrove cover across the protected and unprotected forests of India","volume":"213","author":"Jayanthi","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_32","first-page":"101910","article-title":"River network delineation from Sentinel-1 SAR data","volume":"83","author":"Obida","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/s10661-019-7903-4","article-title":"Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin","volume":"191","author":"Agnihotri","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ruzza, G., Guerriero, L., Grelle, G., Guadagno, F.M., and Revellino, P. (2019). Multi-Method Tracking of Monsoon Floods Using Sentinel-1 Imagery. Water, 11.","DOI":"10.3390\/w11112289"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1016\/j.rse.2011.04.039","article-title":"The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods","volume":"115","author":"Schumann","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135563","DOI":"10.1016\/j.scitotenv.2019.135563","article-title":"Seasonal cycles of lakes on the Tibetan Plateau detected by Sentinel-1 SAR data","volume":"703","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_37","first-page":"101930","article-title":"Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea","volume":"83","author":"Ahmad","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"134757","DOI":"10.1016\/j.scitotenv.2019.134757","article-title":"Improving multi-technique monitoring using Sentinel-1 and Cosmo-SkyMed data and upgrading groundwater model capabilities","volume":"703","author":"Ezquerro","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/JSTARS.2012.2190136","article-title":"Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1","volume":"5","author":"Hornacek","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ottinger, M., Clauss, K., and Kuenzer, C. (2018). Opportunities and challenges for the estimation of aquaculture production based on earth observation data. Remote Sens., 10.","DOI":"10.3390\/rs10071076"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Prasad, K., Ottinger, M., Wei, C., and Leinenkugel, P. (2019). Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11030357"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"Singha","year":"2019","journal-title":"Sci. Data"},{"key":"ref_43","unstructured":"Food and Agriculture Organzation (FAO) (2018). The State of World Fisheries and Aquaculture 2018, FAO."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106273","DOI":"10.1016\/j.ecss.2019.106273","article-title":"Changes in mangrove vegetation, aquaculture and paddy cultivation in the Mekong Delta: A study from Ben Tre Province, southern Vietnam","volume":"226","author":"Veettil","year":"2019","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ecoser.2015.04.007","article-title":"How remote sensing supports mangrove ecosystem service valuation: A case study in Ca Mau province, Vietnam","volume":"14","author":"Vo","year":"2015","journal-title":"Ecosyst. Serv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.ecss.2018.12.021","article-title":"Mangroves of Vietnam: Historical development, current state of research and future threats","volume":"218","author":"Veettil","year":"2019","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s11852-017-0513-9","article-title":"Spatiotemporal changes and fragmentation of mangroves and its effects on fish diversity in Ca Mau Province (Vietnam)","volume":"21","author":"Tran","year":"2017","journal-title":"J. Coast. Conserv."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1007\/s11707-018-0711-2","article-title":"Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin","volume":"12","author":"Perrou","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_50","unstructured":"Google Developers (2019, February 06). Sentinel-1 Algorithms. Google Earth Engine API. Available online: http:\/\/develpoers.google.com\/earth-engine\/Sentinel1."},{"key":"ref_51","first-page":"5","article-title":"Study on new method for water area information extraction based on Sentinel\u20141 data","volume":"50","author":"Jia","year":"2019","journal-title":"Yangtze River"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/322261.322267","article-title":"Connected component labeling using quadtrees","volume":"28","author":"Samet","year":"1981","journal-title":"JACM"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/0031-3203(94)00116-4","article-title":"Statistical geometrical features for texture classification","volume":"28","author":"Chen","year":"1995","journal-title":"Pattern Recognit."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2020.03.020","article-title":"National wetland mapping in China: A new product resulting from objectbased and hierarchical classification of Landsat 8 OLI images","volume":"164","author":"Mao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","first-page":"32","article-title":"State of the art of compactness and circularity measures","volume":"4","author":"Montero","year":"2009","journal-title":"Int. Math. Forum"},{"key":"ref_56","first-page":"12","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/0734-189X(84)90197-X","article-title":"Segmentation of a high-resolution urban scene using texture operators","volume":"25","author":"Conners","year":"1984","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_58","first-page":"10","article-title":"Growing stock volume from multi-temporal landsat imagery through google earth engine","volume":"83","author":"Chiesi","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ocecoaman.2013.02.019","article-title":"Mangrove forest and artisanal fishery in the southern part of the Gulf of California, Mexico","volume":"83","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/S0025-326X(03)00107-3","article-title":"Shrimp aquaculture development and the environment in the Gulf of California ecoregion","volume":"46","author":"Paezosuna","year":"2003","journal-title":"Mar. Pollut. Bull."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.marpolbul.2003.08.025","article-title":"Water and sediment quality, partial mass budget and effluent N loading in coastal brackishwater shrimp farms in Bangladesh","volume":"48","author":"Islam","year":"2004","journal-title":"Mar. Pollut. Bull."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3086\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:54Z","timestamp":1760177514000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,21]]},"references-count":62,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12183086"],"URL":"https:\/\/doi.org\/10.3390\/rs12183086","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,21]]}}}