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Cited by 5Advanced Image Fusion TechniquesRemote-Sensing Image ClassificationSoil Moisture and Remote Sensing

MoCoLSK: Modality-Conditioned High-Resolution Downscaling for Land Surface Temperature

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Dai, Qun, Yuan, Chunyang, Dai, Yimian, Li, Yuxuan, Li, Xiang, Ni, Kang, Xu, Jianhui, Shu, Xiangbo +1 more·2025·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2025.3547945
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Abstract

Land surface temperature (LST) is a critical parameter for environmental studies, but directly obtaining high spatial resolution LST data remains challenging due to the spatiotemporal tradeoff in satellite remote sensing. Guided LST downscaling has emerged as an alternative solution to overcome these limitations, but current methods often neglect spatial nonstationarity, and there is a lack of an open-source ecosystem for deep learning methods. In this article, we propose the modality-conditioned large selective kernel (MoCoLSK) network, a novel architecture that dynamically fuses multimodal data through modality-conditioned projections. MoCoLSK achieves a confluence of dynamic receptive field adjustment and multimodal feature fusion, leading to enhanced LST prediction accuracy. Furthermore, we establish the GrokLST Project, a comprehensive open-source ecosystem featuring the GrokLST dataset, a high-resolution (HR) benchmark, and the GrokLST toolkit, an open-source PyTorch-based toolkit encapsulating MoCoLSK alongside 40+ state-of-the-art approaches. Extensive experimental results validate MoCoLSK’s effectiveness in capturing complex dependencies and subtle variations within multispectral data, outperforming existing methods in LST downscaling. Our code, dataset, and toolkit are available athttps://github.com/GrokCV/GrokLST.

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  • A comprehensive public benchmark dataset for LST downscaling with large spatial extent for calibration and evaluation remains scarce, hindering fair and comprehensive comparison among models.

    “a comprehensive benchmark dataset tailored for this task remains scarce.”

    From PGDM: Physically guided diffusion model for land surface temperature downscaling (2025) · cites this paper as Dai et al., 2025

  • The recently released GrokLST dataset is limited to the Heihe River Basin and has a maximum upscaling factor of only 8.

    “The recent released GrokLST dataset (Dai et al., 2025) is a pioneering attempt, yet it is limited to the Heihe River Basin and the maximum upscaling factor is only 8.”

    From PGDM: Physically guided diffusion model for land surface temperature downscaling (2025) · cites this paper as Dai et al., 2025

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Cited by

Sorted by citations

Authors
Dai, Q., Yuan, C., Dai, Y., Li, Y., Li, X., Ni, K., Xu, J., Shu, X., & Yang, J.
Affiliations
  • Nanjing University of Science and Technology (CN)
  • Nanjing University of Posts and Telecommunications (CN)
  • Nankai University (CN)
  • Guangdong Academy of Sciences (CN)
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025
Volume
63
Pages
1-17
ISSN
  • 0196-2892 (ISSN-L)
  • 1558-0644
DOI
10.1109/tgrs.2025.3547945
OpenAlex
W4408145413
Cited by
5
References
1
Citations by year
2026: 3 · 2025: 2
FWCI
1.79
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62301261, 62206134, 62101280, 62222207, 62332010, 62427808, U24A20330, 62361166670)
  • China Postdoctoral Science Foundation (2021M701727, 2023M731781)
  • Guangdong Academy of Sciences (GDAS)’ Project of Science and Technology Development (2023GDASZH-2023010101)
Topics
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
Keywords
land surface temperature, LST downscaling, high spatial resolution, multimodal fusion, dynamic receptive field, spatial non-stationarity, multispectral data, open-source software toolkit

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