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

• URL: http://arxiv.org/abs/2602.08511v1
• Date: Mon, 09 Feb 2026 11:02:57 GMT
• Title: Estimation of Fish Catch Using Sentinel-2, 3 and XGBoost-Kernel-Based Kernel Ridge Regression
• Authors: Kanu Mohammed, Vaishnavi Joshi, Pranjali Diliprao Patil, Sandipan Mondal, Ming-An Lee, Subhadip Dey,
• Abstract summary: This study uses multispectral images from Sentinel-2 MSI and Sentinel-3 OLCI to estimate fish catch.<n>The proposed approach advances SDGs 2 (Zero Hunger) and 14 (Life Below Water)
• Score: 0.7433903349647366
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

Related papers

• FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture [8.150520348578087]
  FinSight-Net is an efficient and physics-aware fish detection framework for aquaculture environments.<n>We show that FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%.<n>In particular, on UW-BlurredFish, FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%.
  arXiv  Detail & Related papers  (2026-02-23T02:12:47Z)
• OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation [55.978228064498865]
  We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation.<n>Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies.
  arXiv  Detail & Related papers  (2026-01-12T10:20:43Z)
• Improving Underwater Acoustic Classification Through Learnable Gabor Filter Convolution and Attention Mechanisms [1.1024591739346294]
  This paper introduces GSE ResNeXt, a deep learning architecture integrating learnable Gabor convolutional layers with a ResNeXt backbone.<n>The model is evaluated on three classification tasks of increasing complexity.<n>Results show that, GSE ResNeXt consistently outperforms baseline models like Xception, ResNet, and MobileNetV2.
  arXiv  Detail & Related papers  (2025-12-09T02:39:47Z)
• FishDetector-R1: Unified MLLM-Based Framework with Reinforcement Fine-Tuning for Weakly Supervised Fish Detection, Segmentation, and Counting [12.040327353059945]
  We introduce FishDetector-R1, a unified MLLM-based framework for fish detection, segmentation, and counting under weak supervision.<n>On the DeepFish dataset, our framework achieves substantial gains over baselines, improving AP by 20% and mIoU by 10%, while reducing MAE by 30% and GAME by 35%.
  arXiv  Detail & Related papers  (2025-12-01T06:23:56Z)
• Chlorophyll-a Mapping and Prediction in the Mar Menor Lagoon Using C2RCC-Processed Sentinel 2 Imagery [3.4298729855744026]
  Monitoring chlorophyll-a (Chl-a) is essential to anticipate harmful algal blooms and guide mitigation.<n>Satellite-based approaches provide a more comprehensive view, enabling scalable, long-term, and transferable monitoring.<n>This study develops a reliable methodology to predict and map Chl-a across the water column of the Mar Menor.
  arXiv  Detail & Related papers  (2025-10-10T14:20:25Z)
• D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction [73.61056394880733]
  3D Gaussian Splatting (3DGS) enables real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations.<n>We identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage.<n>We propose a unified framework D$2$GS, comprising two key components: a Depth-and-Density Guided Dropout strategy, and a Distance-Aware Fidelity Enhancement module.
  arXiv  Detail & Related papers  (2025-10-09T17:59:49Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• No Mesh, No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images [45.20850219249498]
  Effective reef monitoring requires the quantification of coral growth via accurate volumetric and surface area estimates.<n>We propose a novel, lightweight, and scalable learning framework that addresses this challenge by predicting the 3D volume and surface area of coral-like objects from 2D multi-view RGB images.<n>This framework paves the way for efficient and scalable coral geometry estimation directly from a sparse set of images, with potential applications in coral growth analysis and reef monitoring.
  arXiv  Detail & Related papers  (2025-09-14T08:52:01Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO)<n>In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery.<n>We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• Improving Water Quality Time-Series Prediction in Hong Kong using Sentinel-2 MSI Data and Google Earth Engine Cloud Computing [2.186901738997927]
  This study develops time-series models to predict chlorophyll-a (Chl-a), suspended solids (SS), and turbidity using Sentinel-2 satellite data and Google Earth Engine (GEE) in the coastal regions of Hong Kong.
  arXiv  Detail & Related papers  (2024-08-26T04:31:55Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.