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

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 at https://github.com/GrokCV/GrokLST.

Yimian Dai

Associate Professor

For more information, please visit my personal website at https://yimian.grokcv.ai/.