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

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.

Figures (11)

• Figure 1: Downscaling workflow of the pan-Arctic AVHRR LST dataset.
• Figure 2: Overview of selected scenes for the training of the super-resolution algorithm. The spatial distribution of training, validation and evaluation scenes are shown in a), the temporal span of all sets for both LST datasets is shown in b) and the count per season for the scenes belonging to the training set are shown in c).
• Figure 3: Overview of the algorithm. The source is coarsened MODIS data, and the target image is the original MODIS monthly mean LST information. Adapted from Metzger et al., (2023) Metzger2023.
• Figure 4: Evaluation example for an evaluation scene in Northern Siberia in June 2018 (local equatorial crossing time = 10:30 am). a) Shows the entire evaluation scene (original MODIS LST) with a spatial subset indicated by a black box, that is used in subsets b) to g). b) presents the original MODIS LST scene (the target). c) represents the bicubic interpolation and d) the coarsened MODIS scene (the source). e), f) and g) represent the evaluation results at 8000, 38000 and 150000 iterations, respectively.
• Figure 5: Difference maps for the eight evaluation scenes (original MODIS LST - inferred MODIS LST). Blue denotes underestimation and red overestimation of the LST.