Luo et al. (2026) Improving the Spatiotemporal Resolution of Satellite Remote Sensing Precipitation in Complex Terrain—Based on the Random Forest Method

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xin Luo, Jinzhi Liao, Hao Wang, Tao Zhang, Qiangyu Zeng, Tiantian Yu, Zhihua Li
• DOI: 10.1109/jstars.2026.3674178

Research Groups

Not available in the provided text.

Short Summary

This paper focuses on enhancing the spatiotemporal resolution of satellite-derived precipitation data, specifically in complex terrain, by employing the Random Forest machine learning method.

Objective

• The principal objective is to improve the spatiotemporal resolution of satellite remote sensing precipitation data, particularly addressing challenges in complex terrain.

Study Configuration

• Spatial Scale: Complex terrain (specific geographical extent not provided).
• Temporal Scale: Spatiotemporal resolution improvement (specific temporal granularity not provided).

Methodology and Data

• Models used: Random Forest method.
• Data sources: Satellite remote sensing precipitation.

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Luo2026Improving,
  author = {Luo, Xin and Liao, Jinzhi and Wang, Hao and Zhang, Tao and Zeng, Qiangyu and Yu, Tiantian and Li, Zhihua},
  title = {Improving the Spatiotemporal Resolution of Satellite Remote Sensing Precipitation in Complex Terrain—Based on the Random Forest Method},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3674178},
  url = {https://doi.org/10.1109/jstars.2026.3674178}
}