Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

Abstract

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.

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

“…The Sentinel-1 SAR GRD dataset was collected using the interference wide-band (IW) mapping mode, with a spatial resolution of 10 m, a width of 250 km and an average incidence angle of 30-45 • . Each Sentinel-1 image stored by the GEE platform, which had been preprocessed using the European Space Agency's (ESA) Sentinel-1 Toolbox including orbit restitution, thermal noise removal, terrain correction and radiometric calibration [39][40][41].…”

A review of Google Earth Engine application in mapping aquaculture ponds

“…Sentinel-1 mission, which is available in GEE provides large coverage and high spatial resolution SAR data [5]. Sun et al [22] used GEE to develop a national scale coastal aquaculture ponds map in Vietnam. To segment and classify aquaculture ponds, they utilised Sentinel-1 time series data and indices such as water index, texture, and geometric metrics acquired from radar backscatter.…”

Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series

“…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.…”