{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T12:49:23Z","timestamp":1791377363541,"version":"4.3.2"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"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>Asia dominates the world\u2019s aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.<\/jats:p>","DOI":"10.3390\/rs14010153","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T23:29:07Z","timestamp":1640906947000},"page":"153","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7336-1283","authenticated-orcid":false,"given":"Marco","family":"Ottinger","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6181-0187","authenticated-orcid":false,"given":"Felix","family":"Bachofer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliane","family":"Huth","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"ref_1","unstructured":"FAO Fishery and Aquaculture Statistics (2021). 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