Li et al. (2026) A Method for Sea Surface Temperature Retrieval Based on XGBoost Optimized by the Improved Sparrow Search Algorithm

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Kun Li, Yichao Li, Yonggang Qian, Hang Zhao, Yongan Xue
• DOI: 10.1109/tgrs.2026.3668416

Research Groups

[Information not available in the provided text.]

Short Summary

This paper introduces a novel method for sea surface temperature retrieval, leveraging an XGBoost model whose performance is optimized by an improved Sparrow Search Algorithm.

Objective

• To develop and evaluate a new method for retrieving sea surface temperature (SST) using an XGBoost model optimized by an improved Sparrow Search Algorithm.

Study Configuration

• Spatial Scale: [Information not available in the provided text.]
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: XGBoost (Extreme Gradient Boosting), Improved Sparrow Search Algorithm (ISSA) for optimizing the XGBoost model.
• Data sources: [Information not available in the provided text, but typically involves satellite remote sensing data or in-situ measurements for sea surface temperature retrieval.]

Main Results

[Information not available in the provided text.]

Contributions

• Proposes a novel approach for sea surface temperature retrieval by integrating XGBoost with an improved Sparrow Search Algorithm.
• Introduces an enhanced optimization technique (Improved Sparrow Search Algorithm) to improve the accuracy or efficiency of XGBoost in SST estimation.

Funding

[Information not available in the provided text.]

Citation

@article{Li2026Method,
  author = {Li, Kun and Li, Yichao and Qian, Yonggang and Zhao, Hang and Xue, Yongan},
  title = {A Method for Sea Surface Temperature Retrieval Based on XGBoost Optimized by the Improved Sparrow Search Algorithm},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3668416},
  url = {https://doi.org/10.1109/tgrs.2026.3668416}
}