Estimation of Fish Catch Using Sentinel-2, 3 and XGBoost-Kernel-Based Kernel Ridge Regression

TL;DR

This paper presents a novel satellite image-based method using XGBoost and Kernel Ridge Regression to accurately estimate fish catch, capturing nonlinear ocean-fish relationships and supporting sustainable fisheries management.

Contribution

It introduces a combined XGBoost-KRR approach applied to Sentinel satellite data for improved fish catch estimation, highlighting the method's effectiveness over traditional models.

Findings

XGBoost-KRR achieves higher correlation with observed fish catch.

Sentinel-2 provides finer spatial detail for localized ecological insights.

Sentinel-3 offers broader spectral responses despite lower spatial resolution.

Abstract

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).