Accurate mapping of Chinese coastal aquaculture ponds using biophysical parameters based on Sentinel-2 time series images

Abstract

Aquaculture plays a crucial role in the global food security and nutrition supply, where China accounts for the largest market share. Although there are some studies that focus on large-scale extraction of coastal aquaculture ponds from satellite images, they have often variable accuracies and encounter misclassification due to the similar geometric characteristics of various vivid water bodies. This paper proposes an efficient and novel method that integrates the spatial characteristics and three biophysical parameters (Chlorophyll-a, Trophic State Index, and Floating Algae Index) to map coastal aquaculture ponds at a national scale. These parameters are derived from bio-optical models based on the Google Earth Engine (GEE) cloud computing platform and time series of high-resolution Sentinel-2 images. Our proposed method effectively addresses the misclassification issue between the aquaculture ponds and rivers, lakes, reservoirs, and salt pans and achieves an overall accuracy of 91 % and a Kappa coefficient of 0.83 in the Chinese coastal zone. Our results indicate that the total area of Chinese coastal aquaculture ponds was 1,039,214 ha in 2019, mainly distributed in the Shandong and Guangdong provinces. The highest aquaculture intensity occurs within the 1 km coastal buffer zone, accounting for 22.4 % of the total area. Furthermore, more than half of the Chinese coastal aquaculture ponds are concentrated in the 0-5 km buffer zone. Our method is of general applicability and thus is suitable for large-scale aquaculture ponds mapping projects. Moreover, the biophysical parameters we employ can be considered as new indicators for the classification of various water bodies even with different aquaculture species.

Keywords: Aquaculture ponds; Chlorophyll-a; Google Earth Engine; Remote sensing; Time series.