EGU24-4925, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-4925
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Remote Sensing Retriving of Dissolved Oxygen Concentration in Aquaculture Ponds in the Guangdong-Hong Kong-Macao Greater Bay Area

Keming Mao1 and Xiankun Yang2
Keming Mao and Xiankun Yang
  • 1Guangzhou University, School of geography and remote sensing, China (aklmkm@e.gzhu.edu.cn)
  • 2Guangzhou University, School of geography and remote sensing, China (yangxk@gzhu.edu.cn)

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.

How to cite: Mao, K. and Yang, X.: Remote Sensing Retriving of Dissolved Oxygen Concentration in Aquaculture Ponds in the Guangdong-Hong Kong-Macao Greater Bay Area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4925, https://doi.org/10.5194/egusphere-egu24-4925, 2024.