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<p>Oyster farming provides substantial ecological and economic benefits but is often constrained by the challenges of selecting suitable sites in dynamic coastal environments. This study presents a tailored oyster suitability index (OSI) for the Zhujiang (Pearl) River Estuary (PRE), developed using Landsat satellite imagery and <i>in situ</i> observations collected from 2013 to 2023. Key environmental parameters, including sea surface temperature (SST), salinity, turbidity, and chlorophyll-<i>a</i> (Chl-<i>a</i>) concentration, were integrated for OSI retrieval. Optimal algorithms for each parameter were identified through evaluation using field measurements, yielding high accuracy, as evidenced by strong determination coefficients (<i>R</i><sup>2</sup>) and low root mean square error (RMSE): <i>R</i><sup>2</sup> = 0.98, RMSE = 0.74°C for SST; <i>R</i><sup>2</sup> = 0.94, RMSE = 0.50 for salinity; <i>R</i><sup>2</sup> = 0.95, RMSE = 1.21 mg/m<sup>3</sup> for Chl-<i>a</i>; <i>R</i><sup>2</sup> = 0.91, RMSE = 1.48 NTU for turbidity. The OSI revealed pronounced seasonal and spatial variability, with the highest suitability observed during winter and the lowest during summer. Validation results demonstrated strong alignment between OSI predictions and existing oyster farming zones. These findings underscore the value of remote sensing for scalable, near-real-time aquaculture site assessments. The OSI framework provides a robust decision-support tool for optimizing oyster cultivation, promoting sustainable aquaculture development in dynamic estuarine systems such as the PRE and beyond.</p>

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A remote sensing-driven oyster suitability index for marine ranching in the Zhujiang (Pearl) River estuary

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Qilin Chunpi,
  • Wenbo He,
  • Zifeng Mo,
  • Xinyan Li,
  • Jun Zhao

摘要

Oyster farming provides substantial ecological and economic benefits but is often constrained by the challenges of selecting suitable sites in dynamic coastal environments. This study presents a tailored oyster suitability index (OSI) for the Zhujiang (Pearl) River Estuary (PRE), developed using Landsat satellite imagery and in situ observations collected from 2013 to 2023. Key environmental parameters, including sea surface temperature (SST), salinity, turbidity, and chlorophyll-a (Chl-a) concentration, were integrated for OSI retrieval. Optimal algorithms for each parameter were identified through evaluation using field measurements, yielding high accuracy, as evidenced by strong determination coefficients (R2) and low root mean square error (RMSE): R2 = 0.98, RMSE = 0.74°C for SST; R2 = 0.94, RMSE = 0.50 for salinity; R2 = 0.95, RMSE = 1.21 mg/m3 for Chl-a; R2 = 0.91, RMSE = 1.48 NTU for turbidity. The OSI revealed pronounced seasonal and spatial variability, with the highest suitability observed during winter and the lowest during summer. Validation results demonstrated strong alignment between OSI predictions and existing oyster farming zones. These findings underscore the value of remote sensing for scalable, near-real-time aquaculture site assessments. The OSI framework provides a robust decision-support tool for optimizing oyster cultivation, promoting sustainable aquaculture development in dynamic estuarine systems such as the PRE and beyond.