Aravinth et al. (2025) A Deep Learning Model for Integrating Landsat-8 and Sentinel 2 Satellite Images to Improve the Spatiotemporal Fusion Network for Drought Monitoring

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-11-26
• Authors: J Aravinth, R. Anand
• DOI: 10.1109/jstars.2025.3637223

Research Groups

[Information not available in the provided text.]

Short Summary

This paper proposes a deep learning model to integrate Landsat-8 and Sentinel-2 satellite images, aiming to improve spatiotemporal fusion networks for enhanced drought monitoring.

Objective

• To develop and apply a deep learning model for integrating Landsat-8 and Sentinel-2 satellite images to improve the spatiotemporal fusion network for drought monitoring.

Study Configuration

• Spatial Scale: Regional to global, based on satellite imagery capabilities.
• Temporal Scale: Continuous monitoring for drought assessment, leveraging spatiotemporal fusion.

Methodology and Data

• Models used: Deep Learning Model, Spatiotemporal Fusion Network.
• Data sources: Landsat-8 satellite images, Sentinel-2 satellite images.

Main Results

[Information not available in the provided text.]

Contributions

• Introduction of a deep learning approach for integrating multi-source satellite imagery (Landsat-8 and Sentinel-2) to enhance spatiotemporal fusion.
• Improvement of existing spatiotemporal fusion networks specifically for drought monitoring applications.

Funding

[Information not available in the provided text.]

Citation

@article{Aravinth2025Deep,
  author = {Aravinth, J and Anand, R.},
  title = {A Deep Learning Model for Integrating Landsat-8 and Sentinel 2 Satellite Images to Improve the Spatiotemporal Fusion Network for Drought Monitoring},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3637223},
  url = {https://doi.org/10.1109/jstars.2025.3637223}
}