Four decades of circumpolar super-resolved satellite land surface temperature data

2025-11-21

AI Summary

The coarse spatial resolution (~5 km) of AVHRR GAC data limits fine-scale permafrost dynamics and land surface process studies in the Arctic. To address this, we propose a deep anisotropic diffusion network-based remote sensing temperature super-resolution method—first applied to downscale the 42-year (1982–2023) AVHRR long-term record. Our approach integrates multi-source priors—including MODIS high-resolution observations, land cover, digital elevation, and vegetation height—to reconstruct a pan-Arctic land surface temperature (LST) dataset at 1 km resolution, with twice-daily temporal coverage. This dataset bridges the critical spatiotemporal resolution gap in LST prior to the MODIS era. It significantly enhances capabilities for permafrost degradation monitoring, near-surface air temperature reconstruction, and ice-sheet surface energy balance modeling. Moreover, it provides essential observational constraints for long-term climate change attribution and informs the design of next-generation satellite missions.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite-based LST records, such as those derived from the Advanced Very High Resolution Radiometer (AVHRR), are essential for detecting climate trends. However, the coarse spatial resolution of AVHRR's global area coverage (GAC) data limit their utility for analyzing fine-scale permafrost dynamics and other surface processes in the Arctic. This paper presents a new 42 years pan-Arctic LST dataset, downscaled from AVHRR GAC to 1 km with a super-resolution algorithm based on a deep anisotropic diffusion model. The model is trained on MODIS LST data, using coarsened inputs and native-resolution outputs, guided by high-resolution land cover, digital elevation, and vegetation height maps. The resulting dataset provides twice-daily, 1 km LST observations for the entire pan-Arctic region over four decades. This enhanced dataset enables improved modelling of permafrost, reconstruction of near-surface air temperature, and assessment of surface mass balance of the Greenland Ice Sheet. Additionally, it supports climate monitoring efforts in the pre-MODIS era and offers a framework adaptable to future satellite missions for thermal infrared observation and climate data record continuity.

Problem

• Downscaling coarse satellite land surface temperature data to 1 km resolution
• Improving analysis of Arctic permafrost dynamics and surface processes
• Enabling climate monitoring and permafrost modeling in pre-MODIS era

Innovation

• Super-resolution algorithm using deep anisotropic diffusion model
• Downscaling AVHRR data to 1 km resolution
• Training with MODIS data and high-resolution maps