30-meter Land Surface Temperature from Landsat via Progressive Self-Training Downscaling

TL;DR

This paper introduces a progressive self-training framework to downscale Landsat land surface temperature data from 100 m to 30 m resolution, enhancing spatial detail without relying on fine-scale ground truth.

Contribution

It presents a novel multi-stage deep learning approach that progressively refines thermal details, outperforming existing products in accuracy and spatial coherence.

Findings

Achieves approximately 0.4 K lower MAE and RMSE than official products.

Successfully reconstructs fine-scale thermal patterns and preserves spatial heterogeneity.

Validated with in situ measurements and satellite data, confirming reliability.

Abstract

Land surface temperature (LST) is a critical parameter for characterizing surface energy balance and hydrothermal processes. While Landsat provides invaluable LST observations at medium spatial resolution for over 40 years, its native spatial resolution of thermal bands (e.g., 100 m) remains insufficient compared to its 30 m optical bands, failing to meet the demands of fine-scale studies. To address this issues, this study proposes a progressive self-training framework for downscaling Landsat LST to 30 m without relying on fine-scale ground truth, while maintaining minimal data dependence. The framework progressively optimizes a cross-modal fusion network to refine thermal details in a coarse-to-fine manner, characterized by one pre-training and two fine-tuning stages. Spatial validation against SDGSAT-1 30 m LST and temporal validation using in situ measurements confirm its reliability and accuracy, with both station-averaged MAE and RMSE outperforming the official cubic product by approximately 0.4 K. Further performance comparison experiments demonstrate that the proposed framework consistently reconstructs coherent fine-scale thermal patterns while preserving spatial heterogeneity. Multi spatial resolution evaluations and ablation studies verify the effectiveness of the proposed strategy and network design. Overall, the framework provides a stable pathway for enhancing the spatial resolution of Landsat LST, providing fine-resolution data support for fine-scale surface process studies and localized environmental monitoring.