Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L⁻¹) and reaching lows in winter (43.5 μg·L⁻¹); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA.

Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R² = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments.

China has been a traditional aquaculture powerhouse, contributing over one-third of the global production. The Guangdong-Hong Kong-Macao Greater Bay Area stands out as a primary region for aquaculture. Pond aquaculture is a significant method employed in this area, benefiting from its geographical advantages with a widespread and numerous distribution of fish ponds within the Greater Bay Area. The primary production units for aquaculture ponds are predominantly household-based, decentralized, and lack a significant intensive production effect. Aquaculture personnel often rely on experiential judgment to assess water quality. In recent years, increased human factors and a lack of effective management in the aquaculture pond industry have exacerbated water pollution issues. This has resulted in a growing severity of water pollution problems, with governmental departments unable to conduct large-scale monitoring of aquaculture pond water quality. Dissolved oxygen serves as a crucial indicator reflecting the water quality of these aquaculture ponds. Only dissolved oxygen concentrations within suitable ranges can facilitate the growth of aquatic products; concentrations that are either too high or too low can adversely affect aquatic product growth. This study utilized Landsat 8/9 OLI satellite images, employing atmospheric correction based on Rayleigh reflectance. It combined machine learning and water body index methods to establish a dissolved oxygen Support Vector Regression (SVR) inversion model (R2=0.67). This model determined the trends in dissolved oxygen concentration changes and spatial distribution patterns in aquaculture ponds within the Greater Bay Area over the past decade. The results indicate that from 2013 to 2023, there was a marginal decrease of 0.04% in dissolved oxygen concentration. Concentrations decreased during 2014-2016 and 2018-2020, while they increased in other years. Seasonally, concentrations were higher in spring and autumn and lower in summer and winter. Summer exhibited the lowest dissolved oxygen concentration throughout the year, with the smallest concentration difference and relatively concentrated numerical distribution. Dissolved oxygen concentrations varied significantly in other seasons. Aquaculture ponds in low latitude coastal areas generally had lower dissolved oxygen concentrations, while those in northern mountainous and upstream river regions had relatively higher dissolved oxygen concentrations.