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Coastal aquaculture ponds (CAPs) are of great importance for food security, ecosystem health, and greenhouse emissions. However, existing data often lacks geographic information on both national and international scale spatial data. Satellite remote sensing makes large-scale monitoring of CAPs more efficient. This is especially true when using synthetic aperture radar (SAR) data, which can provide all-day acquisition as a high-quality data source for large-scale CAP mapping. In this study, focusing on CAPs in China and Vietnam, a novel method of large-scale CAPs mapping using Sentinel-1 images was developed. Moreover, high-resolution (10 m) CAP datasets in 2015 and 2020 were produced, and spatiotemporal changes and drivers were analyzed. The key results are as follows: (1) the overall accuracy of the mapping method proposed was >90%. (2) In 2020, CAP area in China was 8774.93 km2 (69.43%) and it was 3864.10 km2 (30.57%) in Vietnam. (3) CAPs showed an opposite trend in China and Vietnam. The CAP area decreased by 891.29 km2 with a change rate of about −9.22% in China, while the CAP area increased by 920.35 km2 with a change rate of about 31.26% in Vietnam. China had made unprecedented ecofriendly efforts to restrict expansion for improving the ecological environment. Vietnam focused more on increasing production than on environmental protection, leading to significant expansion. Our results offer some new insights on satellite remote sensing and its use in coastal eco-environmental management and contribute to Sustainable Development Goals 2, 6 and 14. • An approach for large-scale mapping coastal aquaculture ponds using Sentinel-1. • High-resolution (10 m) distribution maps of CAPs were obtained. • Change trend of CAPs from 2015 to 2020 in China and Vietnam was opposite. • Different policies were the key factor driving for CAPs changes. [ABSTRACT FROM AUTHOR]

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's 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. [ABSTRACT FROM AUTHOR]