SMSR-CV — Thermal Guided Super-Resolution for Real-time SMAP Downscaling

Code repository for:

Rafique, Hamza and Muhammad, Abubakr, Jawairia A. Ahmad, “Thermal Guided Super Resolution for Real-time SMAP Downscaling”, IEEE Geoscience and Remote Sensing Letters (GRSL), 2026.

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Dataset availability

The dataset used in this work will be released upon publication of the associated IEEE GRSL article.

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Environment setup (Conda)

This repo provides a Conda environment file: requirements.yml.

Create and activate the environment:

conda env create -f requirements.yml
conda activate geo

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Configuration

Experiments are controlled via YAML (default: configs/config.yaml).

Key fields (high level):

• paths.*: dataset locations and output/checkpoint locations
• training.*: epochs, batch size, LR, early stopping, etc.
• data.*: coarse/fine resolution, scaling factor, number of input channels
• model.*: architecture selection

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Run training / testing (via main.py)

Train

python main.py --stage train --config configs/config.yaml

Test

python main.py --stage test --config configs/config.yaml

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Ablations and validations

• For Ablations and validations See: ablation.py, ablation_v2.py, and validations.py.

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Results (spatial + ISMN validations)

Spatial results

ISMN validations

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Contact

• For questions or collaborations, please contact the the authors via LinkdIn or email.

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Citation

If you use this code, please cite:

@article{rafique2026thermal_smap_sr,
  title   = {Thermal Guided Super Resolution for Real-time SMAP Downscaling},
  author  = {Rafique, Hamza and Muhammad, Abubakr and Ahmad, Jawairia A.},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  year    = {2026},
  note    = {Code: https://github.com/LUMS-WIT/SMSR-CV}
}