用卫星数据和机器学习预测鱼类捕捞量，助力可持续渔业
Estimation of Fish Catch Using Sentinel-2, 3 and XGBoost-Kernel-Based Kernel Ridge Regression
- 结合哨兵卫星影像与XGBoost-KRR模型，捕捉海洋与鱼类分布的非线性关系
- 在双传感器上均实现最高相关性与最低误差，验证方法有效性
- 适合关注海洋生态、渔业管理和可持续发展目标的研究者
海洋环境因素如海表温度和上层海洋动力对鱼类分布有显著影响。为保障全球粮食安全，需量化这些关联。本研究利用哨兵-2 MSI和哨兵-3 OLCI多光谱影像，采用极端梯度提升（XGBoost）核化的核岭回归（KRR）技术估算鱼类捕捞量。模型评估显示，XGBoost-KRR框架在两个传感器上均达到最强相关性和最低预测误差，表明其更优的非线性关系捕捉能力。尽管哨兵-2 MSI能解析更细粒度的空间变异，强调局部生态互动；哨兵-3 OLCI则呈现更平滑的光谱响应，对应较差的空间分辨率。该方法支持可持续生态系统管理，强化基于卫星的渔业评估，推动实现可持续发展目标2（零饥饿）和14（水下生物）。
原文摘要 · Abstract (English)
Oceanographic factors, such as sea surface temperature and upper-ocean dynamics, have a significant impact on fish distribution. Maintaining fisheries that contribute to global food security requires quantifying these connections. This study uses multispectral images from Sentinel-2 MSI and Sentinel-3 OLCI to estimate fish catch using an Extreme Gradient Boosting (XGBoost)-kernelized Kernel Ridge Regression (KRR) technique. According to model evaluation, the XGBoost-KRR framework achieves the strongest correlation and the lowest prediction error across both sensors, suggesting improved capacity to capture nonlinear ocean-fish connections. While Sentinel-2 MSI resolves finer-scale spatial variability, emphasizing localized ecological interactions, Sentinel-3 OLCI displays smoother spectral responses associated with poorer spatial resolution. By supporting sustainable ecosystem management and strengthening satellite-based fisheries assessment, the proposed approach advances SDGs 2 (Zero Hunger) and 14 (Life Below Water).
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