# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 5 Discussion  
The C-Band SAR imaging constellation of the European Sentinel-1 mission, with its continuously updated time series of all-weather high-resolution satellite imagery, is unique in radar remote sensing, representing one of the most significant data sources in Earth observation. The spatial and temporal resolution of the Sentinel-1 SAR sensor meets the requirements to recognize small-scale surface structures and individual objects on the Earth's surface. The majority of studies on pond aquaculture detection published up to now focus rather on local studies and are limited in spatial transferability to other regions. With the availability of essential time series information derived from high-spatial-resolution Sentinel-1 radar imagery in combination with Sentinel-2 optical time series information, as well as topographical and geometric features, our study has shown that we can effectively detect and map coastal aquaculture at the single-pond level for a continental scale. Since the countries in our study region are also representative of almost 90 percent of the world's aquaculture production, this new data set provides important information in the context of global food production and food security. The object filtering of the automatically extracted pond aquaculture objects, which were derived from satellite time series information with segmentation algorithm, has significantly enhanced our mapping results. It has been shown that post-filtering based on vector attributes in combination with natural water overlap analysis can effectively eliminate non-aquaculture polygons. The free and open access to the water-related features contained in the OSM database provided an essential data source for the overlap analysis during post-processing in order to eliminate non-aquaculture polygons that were not determined by geometrical parameters (especially rectangular river segments). The rectangular and compact shape of aquaculture ponds is the defining feature for the differentiation between aquaculture and other natural or artificial water bodies. Due to errors in the segmentation process of aquaculture ponds and varying shapes of aquaculture ponds, the determination of thresholds for the calculated shape-related features focused rather on optimally balancing the tradeoff between mapping accuracy and rule simplicity. The elimination of non-aquaculture polygons with individually adjusted threshold values for each coastal parcel or different coastal regions is too complex and could lead to a flawed distinction. Therefore, we applied global thresholds for the coastal Asian study region in order to exclude as many false positives (non-aquaculture polygons) as possible while preventing the exclusion of true aquaculture polygons at a minimum. A key advantage of SAR imaging consists of its permanent observation capabilities, since radar waves can operate independent from weather conditions, cloud coverage, and time of day. Cloud cover is a prevailing issue in optical remote sensing in the context of aquaculture mapping, which limits the ability to derive full-area coverages of aquaculture from high-resolution commercial or non-commercial optical sensors. The sensor's spatial resolution is an important criterion, because it influences the appearance of the pond objects' shapes and is thus crucial for the selection of appropriate segmentation parameters. Even at the relatively high resolution of Sentinel-1 with a pixel spacing of 10 m, artificial elongated and narrow structures (such as dams) between individual aquaculture ponds may sometimes not be visible in the satellite data, which can result in connected ponds after the image segmentation. In these cases, (connected) pond agglomerations may have larger perimeters, lowering the objects' compactness and thus impacting other shape metrics such as convex hull that rely on the perimeter.