# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
### Extracting Aquaculture Using Earth Observation  
Only high-resolution imagery enables the detection of even fine linear structural details of small-scale pond aquaculture objects with a size of less than 1 hectare. On the one hand, a large number of Earth observation satellites orbiting our planet with passive optical and active radar instruments with high spatial resolution are suitable for the detection and mapping of aquaculture areas, but many have limited temporal and spatial coverage. On  
Figure 2: Bar chart: Aquaculture production in inland waters and marine areas among Asian countries in 2019; Donut chart: Share of aquaculture production among continents and the top 5 global producers in 2019. Data source: [1].  
the other hand, the object detection of pond aquaculture requires temporarily dense time series in order to distinguish ponds from temporarily flooded areas (e.g., wetlands); this cannot be clearly differentiated with mono-temporal acquisitions. Access to data from high-resolution, continuous-monitoring missions such as Landsat or the European Sentinel-1 and -2 enables the processing and analysis of long-term time series [21]. However, cloud cover limits seamless data acquisition and data availability with passive satellite sensors and makes it difficult to identify aquaculture with multispectral optical sensors [22; 23; 24; 25; 26] as a result of the lack of available data in cloud-prone regions. Active radar sensors emit electromagnetic waves in the longer microwave ranges, which are hardly influenced by the atmosphere and thus enable weather-independent mapping of the Earth's surface [19]. Therefore, satellite-based SAR data has relevant advantages for mapping aquaculture in coastal regions regularly covered by clouds. The Sentinel-1 mission of the Copernicus program with the high-resolution Sentinel-1A and -1B satellites (including its successors Sentinel-1C/D, scheduled to be launched in 2022) is definitely a game changer: it is the first SAR-C-Band mission with open archive, providing free access to a global and temporally dense data coverage that is growing continuously (Approx. 10 terabytes per day). The access to vast amounts of freely available satellite remote sensing data and the development of high-performance and cloud computing infrastructures in recent years have significantly improved the efficiency of data processing and analysis, bringing Earth observation into a new era. This has led to the generation of new data, technologies, and products for different global-scale applications at a rapid pace [27; 28].  
Promising regional approaches exist for the automated extraction of land-based pond aquaculture with Landsat [29; 30], Sentinel-2 [31], and Sentinel-1 data [32; 33; 34; 35; 36]. However, there is no satellite-derived mapping for land-based pond aquaculture on a continental or global scale. A satellite-based quantitative assessment of relevant geoinformation on the spatial extent, distribution, and dynamics of pond aquaculture is essential for sustainable development, and management of aquaculture in coastal Asia is of utmost importance for global food security. Therefore, it is of particular interest to monitor and collect aquaculture and provide appropriate information that can be used for research in the fields of coastal environmental change, hydrology, and oceanography, among others. The main goals of this study are therefore the development of a methodology for the automatic, continental-scale mapping of coastal pond aquaculture for the entirety of Asia based on satellite remote sensing data, as well as the assessment and analysis of aquaculture areas at national and subnational level.