Four decades of circumpolar super-resolved satellite land surface temperature data
Sonia Dupuis, Nando Metzger, Konrad Schindler, Frank G\"ottsche, Stefan Wunderle

TL;DR
This paper introduces a new 42-year high-resolution land surface temperature dataset for the Arctic, derived from AVHRR data using a deep learning super-resolution method, enabling detailed climate and permafrost studies.
Contribution
The paper presents a novel super-resolution algorithm that downscales 42 years of AVHRR LST data to 1 km resolution, improving climate monitoring and modeling in the Arctic.
Findings
Provides a 42-year, 1 km resolution LST dataset for the Arctic.
Enables better modeling of permafrost and surface processes.
Supports climate change assessment and future satellite data continuity.
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…
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Taxonomy
TopicsClimate change and permafrost · Urban Heat Island Mitigation · Soil Moisture and Remote Sensing
