Kuang et al. (2025) A High Spatiotemporal Resolution Soil Moisture Retrieval Approach Leveraging Deep Regression Networks and Multisource Remote Sensing Data

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-18
• Authors: Xiaofei Kuang, Li Wan, Shiyu Xiang, Pengliang Wei, Jiao Guo, Hanwen Yu
• DOI: 10.1109/jstars.2025.3646044

Research Groups

[Not available in the provided text.]

Short Summary

This paper presents a novel approach for retrieving soil moisture at high spatiotemporal resolution by leveraging deep regression networks and multisource remote sensing data.

Objective

• To develop an approach for high spatiotemporal resolution soil moisture retrieval.

Study Configuration

• Spatial Scale: High resolution (specifics not available in the provided text).
• Temporal Scale: High resolution (specifics not available in the provided text).

Methodology and Data

• Models used: Deep Regression Networks.
• Data sources: Multisource Remote Sensing Data (specifics not available in the provided text).

Main Results

[Not available in the provided text.]

Contributions

[Not available in the provided text.]

Funding

[Not available in the provided text.]

Citation

@article{Kuang2025High,
  author = {Kuang, Xiaofei and Wan, Li and Xiang, Shiyu and Wei, Pengliang and Guo, Jiao and Yu, Hanwen},
  title = {A High Spatiotemporal Resolution Soil Moisture Retrieval Approach Leveraging Deep Regression Networks and Multisource Remote Sensing Data},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3646044},
  url = {https://doi.org/10.1109/jstars.2025.3646044}
}