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REVIEW 4 major objections 6 minor 75 references

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

T0 review · 4 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This paper claims that AVHRR's coarse 4-km Arctic land surface temperature record can be downscaled to 1 km for 42 years with a learned guided super-resolution model, retaining the accuracy of the original product.

desk verdict A genuinely new and openly released 42-year 1-km pan-Arctic LST dataset, but the paper overclaims the 1-km fidelity because the super-resolution transfer from monthly MODIS to daily AVHRR is not validated against any independent high-resolution reference. read the letter →

arxiv 2511.17134 v1 pith:J2HHZIHU submitted 2025-11-21 cs.LG

Sonia Dupuis , Nando Metzger , Konrad Schindler , Frank Göttsche , Stefan Wunderle This is my paper
classification cs.LG
keywords landsurfacetemperatureAVHRRMODISguidedsuper-resolutionArcticpermafrostclimatedatarecordanisotropicdiffusion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents a new 42-year (1981–2023) pan-Arctic land surface temperature dataset at 1-km resolution, obtained twice daily from coarse 4-km AVHRR satellite data. The downscaling is done by a guided super-resolution algorithm trained on MODIS LST data, with static maps of land cover, elevation, and vegetation height as guides. The authors argue this dataset fills a critical gap: it extends high-resolution LST coverage back before MODIS, enabling studies of permafrost, air-temperature reconstruction, and ice-sheet processes over four decades. Model evaluation on MODIS scenes shows a mean absolute error of 1.15 °C and RMSE of 2.30 °C, and in-situ validation accuracy is similar to the original 4-km product.

What carries the argument

The central object is DADA, a deep anisotropic diffusion–adjustment algorithm for guided super-resolution. It uses a U-Net with ResNet-50 backbone as feature extractor and is trained to map coarsened (×5) MODIS LST patches to native-resolution patches, using a three-channel static guide composed of land cover, digital elevation, and canopy height. The same trained model is then applied to AVHRR GAC scenes during inference, with overlapping patches averaged to avoid stitching artifacts.

What would settle it

Take actual AVHRR GAC scenes over heterogeneous Arctic terrain, downscale them with the published model, and compare against independent fine-resolution LST from Landsat or ASTER, or dense in-situ radiometer grids, at the same overpass times; if the 1-km product's errors are no better than the 4-km source, or its local variance matches bicubic interpolation rather than edge-preserving structure, the transfer assumption fails.

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Extended reading notes

Core claim

A single learned mapping—trained on coarsened MODIS monthly-mean LST patches paired with native-resolution MODIS patches, guided by static land-surface descriptors—can be applied to AVHRR GAC daily scenes to produce 1-km LST with errors comparable to the source product. This yields a twice-daily, 1-km, pan-Arctic LST time series spanning 1981–2023, making it the first long-term high-resolution record of its kind for the region. The authors treat the dataset as a direct contribution to climate monitoring, permafrost modeling, and continuity with future thermal infrared missions.

Load-bearing premise

The mapping is learned from coarsened MODIS monthly-mean LST and then applied to AVHRR daily scenes, so the whole dataset rests on the premise that these two image types are interchangeable enough for the learned fine-scale detail to transfer across sensors, overpass times, and compositing.

Editorial extensions

If this is right

  • Extends 1-km land surface temperature coverage back to 1981, filling the pre-MODIS gap and meeting the 30-year record requirement for climate-trend detection.
  • Supports permafrost thermal-state modeling, near-surface air temperature reconstruction, and Greenland Ice Sheet surface mass balance assessment at a previously unavailable spatial detail.
  • Provides a reusable training pipeline and training data that could be adapted to future thermal infrared satellite missions for data record continuity.
  • Allows study of Arctic winter warming events and fine-scale temperature variability at a circumpolar scale over four decades.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the guide is static and the model is trained on monthly-mean MODIS, rapid thermal events such as snowmelt fronts or thaw slumps on historical daily AVHRR scenes may be smoothed; this could be tested by comparing local gradient statistics with coincident Landsat/ASTER overpasses.
  • The transferability assumption implies the same model could downscale other coarse historical thermal-infrared sensors, provided a similar cross-sensor validation is performed—an extension the paper does not fully demonstrate.
  • The evaluation uses coarsened MODIS, not actual AVHRR scenes, so an independent fine-resolution reference on true AVHRR data would be the decisive next test; without it, the added 1-km detail could reflect learned MODIS textures rather than real AVHRR thermal structure.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents a 42-year (1981–2023), twice-daily, pan-Arctic land surface temperature (LST) dataset at 0.01° resolution, obtained by downscaling the existing 0.05° AVHRR GAC LST product with the DADA guided super-resolution algorithm. DADA is trained on ESA CCI LST data (IRCDR and Aqua-MODIS monthly means), using ×5-coarsened inputs and native-resolution targets, with a three-channel static guide (land cover, DEM, canopy height). The trained model is then applied to instantaneous AVHRR GAC LST scenes. The paper reports model-level MAE of 1.150 °C and RMSE of 2.297 °C on synthetic MODIS evaluation scenes, in-situ validation statistics for the final AVHRR product, and a qualitative intercomparison with the LSA SAF EDLST product. The dataset, training data, and code are publicly released.

Significance. If the 1-km AVHRR product is credible, it would fill an important gap: a four-decade, circumpolar, 1-km LST record extending before the MODIS era would directly support permafrost, T2M reconstruction, and ice-sheet studies. The paper's strengths include the release of the dataset (BORIS portal), the training scenes and auxiliary data (Zenodo), and the code (GitHub), as well as the systematic training/evaluation pipeline and the sensitivity analysis around ResNet depth and hyperparameters. The DADA framework is already published, and this paper applies it operationally. However, the central validation issue—the absence of an independent high-resolution LST reference for the actual AVHRR downscaled scenes—means that the added sub-GAC information content of the product is not yet established. The reported synthetic-domain gain over bicubic interpolation is small (0.094 °C MAE), and the transfer from monthly-mean MODIS/CCI training data to instantaneous AVHRR GAC scenes from different sensors is the load-bearing assumption. The paper is a useful dataset contribution, but its main claim needs stronger independent validation.

major comments (4)
  1. [DADA model evaluation / Table 1] The quantitative evaluation in Table 1 is performed on synthetic ×5-coarsened ESA CCI LST scenes, not on actual AVHRR GAC data. The central claim is that the 1-km AVHRR product contains meaningful sub-GAC information, yet no evaluation compares the AVHRR SR output against an independent high-resolution LST reference. The in-situ validation (Figures 7–8) is at homogeneous sites, where the authors themselves note the 1-km and 4-km products are expected to agree. The EDLST comparison (Figures 9–11) is qualitative and limited to 2020. I recommend adding a direct validation of the AVHRR SR product, e.g., comparison with MODIS LST at 1 km during the overlapping period, or with Landsat/ASTER LST for selected scenes, reporting MAE/RMSE against the high-resolution reference and against bicubic interpolation and the original GAC product.
  2. [Methods / DADA model training] The model is trained on monthly-mean ESA CCI LST (IRCDR and Aqua-MODIS) with 0.05° coarsened inputs and 0.01° targets, then applied to twice-daily instantaneous AVHRR GAC LST from a different sensor, retrieval algorithm (GSW vs UOL/GSW), cloud mask, and overpass time. The paper does not test whether the learned mapping transfers across these differences. The 'source' in Figure 3 is coarsened MODIS, not AVHRR. Because the synthetic-domain gain over bicubic is only 0.094 °C MAE (Table 1), the transfer question is load-bearing. Please provide evidence—for example, co-located AVHRR and MODIS scenes over an overlap period, or an explicit argument that the relevant gradients are preserved between monthly-mean MODIS and instantaneous AVHRR—or temper the claims accordingly.
  3. [Technical Validation / Validation against in situ measurements] The in-situ validation at SURFRAD, KIT, ARM, BSRN, and LAW stations shows that the 1-km AVHRR SR product has accuracy similar to the original GAC product. As the text states, this is expected because the stations are in relatively homogeneous areas. This does not validate the spatial detail claimed by the 1-km product; it only confirms that the downscaling does not destroy the large-scale accuracy. The paper needs a validation that is sensitive to sub-GAC structure—ideally at heterogeneous sites (coastlines, mountainous terrain, land-cover boundaries) using a high-resolution reference or a spatial-structure metric such as gradient/edge preservation against a fine-resolution LST source.
  4. [Usage Notes / Limitations] The limitations paragraph acknowledges the static land-cover guide and the ice-sheet problem, but does not mention the sensor-transfer gap described above. Given that the manuscript itself lists limitations, the omission of this central assumption is notable. Please add an explicit discussion of the transfer risk and its implications for the interpretation of the 1-km product, particularly for pre-2000 periods where no MODIS-based cross-check is possible.
minor comments (6)
  1. [Table 1] The header says 'Mean MAE and MSE' but the second column is labeled 'RMSE' and reports root mean square error. Please correct the header to 'MAE and RMSE'.
  2. [Figure 6] The histogram panels display unlabeled values such as '= 0.000' and '= 0.914'. Please label these as the mean and standard deviation (or indicate that they are μ and σ), and ensure the axis text is legible.
  3. [Intercomparison with EDLST from LSA SAF] There are typos and incomplete sentences: 'prodcut' should be 'product'; 'The differences are centered around 0 °C and in three areas of interest' appears to be missing a clause. Also, the comparison would benefit from a brief description of how the different cloud masks and compositing strategies affect the difference distributions.
  4. [Bibliography] References [42] and [69] are the same Pérez-Planells et al. paper. Please merge or renumber.
  5. [Data Record] The text says the dataset covers '1982 to 2023' at 0.01°, while the abstract and Data coverage table start in 1981 (NOAA-7 from 1981-08-24). Please clarify the exact start date of the released product.
  6. [Methods / Inference on AVHRR data] The inference stage uses patches of 1920×1920 with a stride of 1480/1792, and overlapping patches are averaged. Please state explicitly whether this averaging is performed before or after applying the cloud mask, and whether the cloud mask is applied to the SR output or inherited from the GAC scenes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the AVHRR 1-km product is an independent transfer of a learned MODIS mapping, and reported evaluation gaps are external-validity concerns rather than self-referential derivations.

full rationale

No circular step is present. The DADA model is trained on pairs of synthetically ×5-coarsened MODIS monthly-mean LST and native MODIS LST, but the product it is used to create is AVHRR GAC LST at 0.05°, which is a different sensor, retrieval, overpass time, and compositing period; the 1-km AVHRR output therefore does not reduce by construction to the MODIS training target. Table 1's MAE/RMSE are internal model diagnostics on MODIS evaluation scenes, not validation of the AVHRR product, and the paper separately validates the final AVHRR SR product against in situ measurements (Technical Validation) and qualitatively against EDLST. The choice of DADA is motivated by the authors' prior comparison [35], a normal self-citation for method selection, but it is not a uniqueness theorem, and the current paper also independently compares DADA to bicubic and source baselines in Table 1. The principal weakness is a transfer/validation gap: no independent high-resolution AVHRR reference confirms that sub-GAC detail is real, and the Usage Notes explicitly acknowledge static-guide and ice-sheet limitations. That is a correctness/limitation issue, not a circular step under the definitions used here.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a learned mapping fitted to MODIS plus static guide layers; no new physical entities are introduced. The largest burden is the sensor- and time-transfer assumption (coarsened MODIS as AVHRR proxy) and the static-guide assumption over 42 years.

free parameters (2)
  • DADA U-Net ResNet-50 network weights = not reported
    The downscaling map is entirely learned from ESA CCI LST training scenes; all spatial detail and errors of the final product depend on these fitted weights.
  • Training hyperparameters (ResNet-50, lr_step=150, sampler_length=400, patch size 240) = selected via evaluation runs (Table 1)
    These settings were chosen by comparing MODIS evaluation MAE/RMSE and are needed to reproduce the reported performance.
assumptions (4)
  • domain assumption ×5-coarsened MODIS monthly-mean LST is an adequate proxy for AVHRR GAC LST.
    Methods 'DADA model training' uses coarsened MODIS as the source because the resolution ratio is 5, equating two different sensors with different PSF, noise, overpass time, and compositing.
  • domain assumption Static guide data (2005 land cover, Copernicus GLO-90 DEM, GEDI canopy height) are representative over 1981–2023.
    The paper itself flags this in Usage Notes: 'The land cover map used for the downscaling is static and does not account for the land cover changes over time.'
  • domain assumption A model trained on monthly-mean MODIS LST transfers to daily instantaneous AVHRR overpasses.
    Training scenes are monthly means from ESA CCI LST; inference is on daily AVHRR GAC scenes with varying local overpass times. This transfer is not directly tested.
  • domain assumption The input pan-Arctic AVHRR GAC LST product (Dupuis et al., 2024) has sufficient accuracy as the base for downscaling.
    The downscaled product inherits all errors of the GAC retrieval; validation of the SR product at homogeneous sites shows similar accuracy to the GAC product, not improvement.

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Cite this review

Pith. "Pith review of Four decades of circumpolar super-resolved satellite land surface temperature data." pith.science (2026). https://pith.science/paper/J2HHZIHU

@misc{pith2026251117134,
  author       = {Pith},
  title        = {Pith review of: Four decades of circumpolar super-resolved satellite land surface temperature data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J2HHZIHU}},
  note         = {Machine review of arXiv:2511.17134}
}
read the original 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

Figures reproduced from arXiv: 2511.17134 by the authors.

Figure 1
Figure 1. Downscaling workflow of the pan-Arctic AVHRR LST dataset. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of selected scenes for the training of the super-resolution algorithm. The spatial distribution of training, [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Overview of the algorithm. The source is coarsened MODIS data, and the target image is the original MODIS [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Evaluation example for an evaluation scene in Northern Siberia in June 2018 (local equatorial crossing time = [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Difference maps for the eight evaluation scenes (original MODIS LST - inferred MODIS LST). Blue denotes [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Histograms of residuals (original MODIS LST - inferred MODIS LST) for geographical patches used for model [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Enhanced (1 km) AVHRR LST versus in situ LST at (a) Bondville (BND), (b) Desert Rock (DRA), (c) Fort Peck [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Enhanced (1 km) AVHRR LST versus in situ LST at (a) the KIT Campus Nord (Germany), (b) Hyytiälä, (c) [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Comparison of the EDLST product and the AVHRR SR LST dataset from the MetOp-A satellite during daytime. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Comparison of the EDLST product and the AVHRR SR LST dataset from the MetOp-B satellite during nighttime. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Histograms of differences of Land SAF EDLST product minus the AVHRR SR LST from the MetOp-A satellite [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

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Works this paper leans on

75 extracted references · 38 canonical work pages

  1. [1]

    Bojinski, S. et al.: The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy,Bulletin of the American Meteorological Society,95, 1431–1443, (2014), 10.1175/BAMS-D-13-00047.1

    work page doi:10.1175/bams-d-13-00047.1 2014

  2. [2]

    Hulley, G. C. et al. Land Surface Temperature, in:Taking the Temperature of the Earth, edited by Glynn C. Hulley and Darren Ghent, Elsevier, 57–127, (2019), 10.1016/B978-0-12-814458- 9.00003-4

    work page doi:10.1016/b978-0-12-814458- 2019

  3. [3]

    and Göttsche, F.-M.: Combined modelling of annual and diurnal land surface temperature cycles,Remote Sensing of Environment,299, 113892, (2023), 10.1016/j.rse.2023.113892

    Pérez-Planells, L. and Göttsche, F.-M.: Combined modelling of annual and diurnal land surface temperature cycles,Remote Sensing of Environment,299, 113892, (2023), 10.1016/j.rse.2023.113892

    arXiv 2023

  4. [4]

    Li, Z.-L. et al.: Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications,Reviews of Geophysics,61, e2022RG000777, (2023), 10.1029/2022RG000777

    work page doi:10.1029/2022rg000777 2023

  5. [5]

    Summary for Policy- makers,Arctic Monitoring and Assessment Programme (AMAP), Tromsø, Norway, 16 pp, (2021)

    AMAP: Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy- makers,Arctic Monitoring and Assessment Programme (AMAP), Tromsø, Norway, 16 pp, (2021)

    2021

  6. [6]

    et al.: A central arctic extreme aerosol event triggered by a warm air-mass intrusion,Nat Commun,13(1), 5290, (2022), 10.1038/s41467-022-32872-2

    Dada, L. et al.: A central arctic extreme aerosol event triggered by a warm air-mass intrusion,Nat Commun,13(1), 5290, (2022), 10.1038/s41467-022-32872-2

    work page doi:10.1038/s41467-022-32872-2 2022

  7. [7]

    et al.: The Arctic has warmed nearly four times faster than the globe since 1979,Nat

    Rantanen, M. et al.: The Arctic has warmed nearly four times faster than the globe since 1979,Nat. Commun. Earth Environ.,3, 168, (2022), 10.1038/s43247-022-00498-3

    doi:10.1038/s43247-022-00498-3 1979

  8. [8]

    Biskaborn, B. K. et al.: Permafrost is warming at a global scale,Nature Communications,10, 264, (2019), 10.1038/s41467-018-08240-4

    work page doi:10.1038/s41467-018-08240-4 2019

Show all 75 references
  1. [9]

    et al.: Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia,The Cryosphere,11, 1441–1463, (2017), 10.5194/tc-11-1441-2017

    Westermann, S. et al.: Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia,The Cryosphere,11, 1441–1463, (2017), 10.5194/tc-11-1441-2017

    2017 doi

  2. [10]

    et al.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1km2 scale,Earth-Science Reviews,193, 299, (2019), 10.1016/j.earscirev.2019.04.02

    Obu, J. et al.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1km2 scale,Earth-Science Reviews,193, 299, (2019), 10.1016/j.earscirev.2019.04.02

    2019 doi

  3. [11]

    and Nitze, I.: Permafrost Monitoring from Space,Surveys in Geophysics, (2023), 10.1007/s10712-023-09770-3

    Bartsch, A., Strozzi, T. and Nitze, I.: Permafrost Monitoring from Space,Surveys in Geophysics, (2023), 10.1007/s10712-023-09770-3

    2023 doi

  4. [12]

    et al.: Deriving Arctic 2m air temperatures over snow and ice from satellite surface temperature measurements,The Cryosphere,15, 3035–3057, (2021), 10.5194/tc-15-3035- 2021

    Nielsen-Englyst, P. et al.: Deriving Arctic 2m air temperatures over snow and ice from satellite surface temperature measurements,The Cryosphere,15, 3035–3057, (2021), 10.5194/tc-15-3035- 2021

    2021 doi

  5. [13]

    et al.: A new Level 4 multi-sensor ice surface temperature product for the Greenland Ice Sheet,The Cryosphere,16, 3703–3721, (2022), 10.5194/tc-16-3703-2022

    Karagali, I. et al.: A new Level 4 multi-sensor ice surface temperature product for the Greenland Ice Sheet,The Cryosphere,16, 3703–3721, (2022), 10.5194/tc-16-3703-2022

    2022 doi

  6. [14]

    T., Scherler, D

    Gök, D. T., Scherler, D. and Wulf, H.: Land surface temperature trends derived from Landsat imagery in the Swiss Alps,The Cryosphere,18, 5259–5276, (2024), 10.5194/tc-18-5259-2024

    2024 doi

  7. [15]

    1523, World Meteorological Organization, Geneva, Switzerland, (2010)

    World Meteorological Organization, Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC, Technical Report GOOS-184, GTOS-76, WMO-TD/No. 1523, World Meteorological Organization, Geneva, Switzerland, (2010)

    2010

  8. [16]

    Intergovernmental Panel on Climate Change (IPCC), Annex VII: Glossary,In: Climate Change 2021: The Physical Science Basis, (2021),https://www.ipcc.ch/report/ar6/wg1/ downloads/report/IPCC_AR6_WGI_AnnexVII.pdf 21

    2021

  9. [17]

    Li, J.-H., Li, Z.-L., Liu, X. and Duan, S.-B.: A global historical twice-daily (daytime and night- time) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021,Earth System Science Data,15, 2189–2212, (2023), 10.519...

    1981 doi

  10. [18]

    et al.: A global long-term (1981–2000) land surface temperature product for NOAA A VHRR,Earth System Science Data,12, 3247–3268, (2020), 10.5194/essd-12-3247-2020

    Ma, J. et al.: A global long-term (1981–2000) land surface temperature product for NOAA A VHRR,Earth System Science Data,12, 3247–3268, (2020), 10.5194/essd-12-3247-2020

    1981 doi

  11. [19]

    EUMETSAT, A VHRR Fundamental Data Record – Release 1 – Multimission, (2023), 10.15770/EUM_SEC_CLM_0060

    2023 doi

  12. [20]

    Dupuis, S., Göttsche, F.-M. and Wunderle, S.: Temporal stability of a new 40-year daily A VHRR land surface temperature dataset for the pan-Arctic region,The Cryosphere,18, 6027–6059, (2024), 10.5194/tc-18-6027-2024

    2024 doi

  13. [21]

    M., Romanovsky, V

    Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V . E. and Boike, J.: Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic,Nature Communications,9, 5423, (2018), 10.1038/s41467-018-07663-3

    2018 doi

  14. [22]

    Mildrexler, D. J. et al.: Thermal anomalies detect critical global land surface changes,J. Appl. Meteorol. Clim.,57, 391–411, (2018), 10.1175/JAMC-D-17-0093.1

    2018 doi

  15. [23]

    et al.: Changes in Winter Warming Events in the Nordic Arctic Region, Journal of Climate,29(17), 6223–6244, (2016), 10.1175/JCLI-D-15-0763.1

    Vikhamar-Schuler, D. et al.: Changes in Winter Warming Events in the Nordic Arctic Region, Journal of Climate,29(17), 6223–6244, (2016), 10.1175/JCLI-D-15-0763.1

    2016 doi

  16. [24]

    et al.: CCI+ Phase 2 – New ECVs Permafrost D1.1 User Requirement Document, Version 3.0, (2023)

    Bartsch, A. et al.: CCI+ Phase 2 – New ECVs Permafrost D1.1 User Requirement Document, Version 3.0, (2023)

    2023

  17. [25]

    Wu, P. et al.: Spatially Continuous and High-Resolution Land Surface Temperature Product Gen- eration: A review of reconstruction and spatiotemporal fusion techniques,IEEE Geoscience and Remote Sensing Magazine,9, 112–137, (2021), 10.1109/MGRS.2021.3050782

    2021

  18. [26]

    Yoo, C. et al.: Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest,International Journal of Applied Earth Observa- tion and Geoinformation,110, 102827, (2022), 10.1016/j.jag.2022.102827

    2022

  19. [27]

    P., Anderson, M

    Agam, N., Kustas, W. P., Anderson, M. C., Li, F. and Neale, C. M. U.: A vegetation index based technique for spatial sharpening of thermal imagery,Remote Sensing of Environment,107, 545– 558, (2007), 10.1016/j.rse.2006.10.006

    2007 doi

  20. [28]

    Wu, M., Niu, Z., Wang, C., Wu, C. and Wang, L.: Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model,Journal of Applied Remote Sensing,6, 063507, (2012), 10.1117/1.JRS.6.063507

    2012 doi

  21. [29]

    and Ke, C.: Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products,Sensors,7, 3043–3057, (2007), 10.3390/s7123043

    Liu, Y ., Yamaguchi, Y . and Ke, C.: Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products,Sensors,7, 3043–3057, (2007), 10.3390/s7123043

    2007 doi

  22. [30]

    et al.: Deep learning for downscaling remote sensing images: Fusion and super-resolution,IEEE Geoscience and Remote Sensing Magazine,10, 202–255, (2022), 10.1109/MGRS.2022.3171836

    Sdraka, M. et al.: Deep learning for downscaling remote sensing images: Fusion and super-resolution,IEEE Geoscience and Remote Sensing Magazine,10, 202–255, (2022), 10.1109/MGRS.2022.3171836

    2022

  23. [31]

    et al.: Learning Graph Regularisation for Guided Super-Resolution,CVPR, 1969–1978, (2022), 10.1109/CVPR52688.2022.00202

    de Lutio, R. et al.: Learning Graph Regularisation for Guided Super-Resolution,CVPR, 1969–1978, (2022), 10.1109/CVPR52688.2022.00202

    1969

  24. [32]

    and Ji, X.: Guided depth map super-resolution: A survey, ACM Comput

    Zhong, Z., Liu, X., Jiang, J., Zhao, D. and Ji, X.: Guided depth map super-resolution: A survey, ACM Comput. Surv.,55, (2023), 10.1145/3584860. 22

    2023 doi

  25. [33]

    et al.: GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance,ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,X-G-2025, Dubai, (2025)

    Karaman, K. et al.: GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance,ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,X-G-2025, Dubai, (2025)

    2025

  26. [34]

    de Lutio, R., D’Aronco, S., Wegner, J. D. and Schindler, K.: Guided Super-Resolution As Pixel-to- Pixel Transformation,ICCV, 8828–8836, (2019), 10.1109/ICCV .2019.00892

    2019

  27. [35]

    Dupuis, S., Wunderle, S. and Göttsche, F.: Spatial Downscaling Frameworks for Satel- lite Based Land Surface Temperature to Support Permafrost Modelling, inIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 209–212, (2024), 10.1109/IGARSS53475.2024.10641241

    2024

  28. [36]

    Metzger, N., Caye Daudt, R., and Schindler, K.: Guided Depth Super-Resolution by Deep Anisotropic Diffusion,CVPR, 18237–18246, (2023), 10.1109/CVPR52729.2023.01749

    2023

  29. [37]

    and Malik, J.: Scale-space and edge detection using anisotropic diffusion,IEEE Trans- actions on Pattern Analysis and Machine Intelligence,12, 629–639, (1990), 10.1109/34.56205

    Perona, P. and Malik, J.: Scale-space and edge detection using anisotropic diffusion,IEEE Trans- actions on Pattern Analysis and Machine Intelligence,12, 629–639, (1990), 10.1109/34.56205

    1990 doi

  30. [38]

    Caye Daudt, R., Le Saux, B., Boulch, A., and Gousseau, Y .: Guided Anisotropic Diffusion and It- erative Learning for Weakly Supervised Change Detection,CVPR Workshops, Long Beach, (2019)

    2019

  31. [39]

    Luojus, K. et al.: ESA Snow CCI: Snow Water Equivalent (SWE) Level 3C Daily Global Climate Research Data Package (1979–2022), Version 3.1,NERC EDS Centre for Environmental Data Analysis, (2024), 10.5285/9d9bfc488ec54b1297eca2c9662f9c81

    1979 doi

  32. [40]

    Luojus, K. et al.: ESA Snow Climate Change Initiative (Snow_CCI): Snow Water Equivalent (SWE) level 3C daily global climate research data package (1979–2020), version 2.0, NERC EDS Centre for Environmental Data Analysis, (2022), 10.5285/4647cc9ad3c044439d6c643208d3c494

    1979 doi

  33. [41]

    et al.: Algorithm Theoretical Basis Document: WP2.1 - DEL-LST-CCI-D2.2-ATBD, ESA Climate Office, (2023)

    Ghent, D. et al.: Algorithm Theoretical Basis Document: WP2.1 - DEL-LST-CCI-D2.2-ATBD, ESA Climate Office, (2023)

    2023

  34. [43]

    and Dozier, J.: A generalized split-window algorithm for retrieving land-surface tempera- ture from space,IEEE T

    Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving land-surface tempera- ture from space,IEEE T. Geosci. Remote,34, 892–905, (1996), 10.1109/36.508406

    1996 doi

  35. [44]

    Prata, A. F.: Land Surface Temperature Measurement from Space: AATSR Algorithm Theoret- ical Basis Document, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Aspendale, Australia, (2002)

    2002

  36. [45]

    et al.: TanDEM-X: The New Global DEM Takes Shape,IEEE Geoscience and Remote Sensing Magazine,2, 8–23, (2014), 10.1109/MGRS.2014.2318895

    Zink, M. et al.: TanDEM-X: The New Global DEM Takes Shape,IEEE Geoscience and Remote Sensing Magazine,2, 8–23, (2014), 10.1109/MGRS.2014.2318895

    2014

  37. [46]

    Copernicus Digital Elevation Model (DEM) GLO-90, (2024),https://doi.org/10.5270/ ESA-c5d3d65

    2024

  38. [47]

    et al.: A high-resolution canopy height model of the Earth,Nature Ecology & Evolution, 7, 1778–1789, (2023), 10.1038/s41559-023-02206-6

    Lang, N. et al.: A high-resolution canopy height model of the Earth,Nature Ecology & Evolution, 7, 1778–1789, (2023), 10.1038/s41559-023-02206-6

    2023 doi

  39. [48]

    et al.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data,5, 180214, (2018), 10.1038/sdata.2018.214

    Beck, H. et al.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data,5, 180214, (2018), 10.1038/sdata.2018.214. 23

    2018 doi

  40. [49]

    PyTorch, PyTorch: An open source machine learning framework, (2025),https://pytorch. org/

    2025

  41. [50]

    Stewart, A. J. et al., TorchGeo: Deep Learning With Geospatial Data,ACM Transactions on Spatial Algorithms and Systems, (2024), 10.1145/3707459

    2024 doi

  42. [51]

    and van de Weijer, J.: Planckian jitter: enhancing the color quality of self-supervised visual representations,arXiv preprint arXiv:2202.07993, (2022)

    Zini, S., Buzzelli, M., Twardowski, B. and van de Weijer, J.: Planckian jitter: enhancing the color quality of self-supervised visual representations,arXiv preprint arXiv:2202.07993, (2022)

    2022 arXiv

  43. [52]

    M., Bradbury, K

    Huang, B., Reichman, D., Collins, L. M., Bradbury, K. and Malof, J. M.: Dense labeling of large re- mote sensing imagery with convolutional neural networks: a simple and faster alternative to stitch- ing output label maps,CoRR,abs/1805.12219, (2018),http://arxiv.org/abs/1805.12219

    2018 arXiv

  44. [53]

    and Zhang, L.: STFDiff: Remote sensing im- age spatiotemporal fusion with diffusion models,Information Fusion,111, 102505, (2024), 10.1016/j.inffus.2024.102505

    Huang, H., He, W., Zhang, H., Xia, Y . and Zhang, L.: STFDiff: Remote sensing im- age spatiotemporal fusion with diffusion models,Information Fusion,111, 102505, (2024), 10.1016/j.inffus.2024.102505

    2024

  45. [54]

    int/en/projects/lst/

    Pérez-Planells, L., Martin, M., and Ghent, D.: Product Validation and Intercomparison Report (PVIR),Consortium CCI LST, WP4 – DEL4.1, LST-CCI-D4.1-PVIR, Version 3.2, Karlsruhe Insti- tute of Technology (KIT) and University of Leicester (ULeic), (2023),https://climate.esa. int/...

    2023

  46. [55]

    and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python,Journal of Open Research Software,5(1), 10, (2017), 10.5334/jors.148

    Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python,Journal of Open Research Software,5(1), 10, (2017), 10.5334/jors.148

    2017 doi

  47. [56]

    Golmant, N.; contributors,torchgeo.datasets.geo.py – TorchGeo GitHub repository,https://github.com/noahgolmant/torchgeo/blob/ c8eb26b1ae317dba9627e6f58a5ba607030f9c0a/torchgeo/datasets/geo.py, Accessed: 2025-05, (2025)

    2025

  48. [57]

    and Wunderle, S.: Enhanced pan-Arctic A VHRR land surface temper- ature dataset (1 km spatial resolution), BORIS, University of Bern, (2025), 10.48620/92273

    Dupuis, S., Göttsche, F.-M. and Wunderle, S.: Enhanced pan-Arctic A VHRR land surface temper- ature dataset (1 km spatial resolution), BORIS, University of Bern, (2025), 10.48620/92273

    2025 doi

  49. [58]

    Dupuis, S.: MODIS Land surface temperature and auxiliary data for the training of a guided super- resolution algorithm, Zenodo, (2025), 10.5281/zenodo.17341544

    2025 doi

  50. [59]

    et al.: Circumarctic land cover diversity considering wetness gradients,Hydrology and Earth System Sciences,28(11), 2421–2481, (2024), 10.5194/hess-28-2421-2024

    Bartsch, A. et al.: Circumarctic land cover diversity considering wetness gradients,Hydrology and Earth System Sciences,28(11), 2421–2481, (2024), 10.5194/hess-28-2421-2024

    2024 doi

  51. [60]

    S., Trigo, I

    Göttsche, F.-M., Olesen, F. S., Trigo, I. F., Bork-Unkelbach, A. and Martin, M. A.: Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ mea- surements in Africa,Remote Sens.,8(5), 410, (2016), 10.3390/rs8050410

    2016 doi

  52. [61]

    Martin, M. A. et al.: Comprehensive in situ validation of five satellite land surface temperature data sets over multiple stations and years,Remote Sens.,11, 479, (2019), 10.3390/rs11050479

    2019 doi

  53. [62]

    Maturilli, M.: Basic and other measurements of radiation at station Ny-Ålesund (2006-05 et seq), Alfred Wegener Institute - Research Unit Potsdam, (2020), 10.1594/PANGAEA.914927

    2006 doi

  54. [63]

    J.; Halliwell, D.: Basic measurements of radiation at station Alert (2004-08 - 2014-06), AeroCan, Wilcox, (2021), 10.1594/PANGAEA.932867

    Cox, C. J.; Halliwell, D.: Basic measurements of radiation at station Alert (2004-08 - 2014-06), AeroCan, Wilcox, (2021), 10.1594/PANGAEA.932867

    2004 doi

  55. [64]

    Petersburg, (2023), 10.1594/PANGAEA.959213

    Kustov, V .: Basic and other measurements of radiation at station Tiksi (2010-06 et seq), Arctic and Antarctic Research Institute, St. Petersburg, (2023), 10.1594/PANGAEA.959213. 24

    2010 doi

  56. [65]

    Petersburg, (2018), 10.1594/PANGAEA.892295

    Kustov, V .: Basic and other measurements of radiation at station Cape Baranova (2016-09), Arctic and Antarctic Research Institute, St. Petersburg, (2018), 10.1594/PANGAEA.892295

    2016 doi

  57. [66]

    Surface Radiation Budget (SURFRAD) network, (2024),https://gml.noaa.gov/dv/data/ index.php?parameter_name=Surface%2BRadiation

    2024

  58. [67]

    Karlsruhe Institute of Technology (KIT) network, (2024),https://www.imk-asf.kit.edu/ english/skl_surfacetemperature.php

    2024

  59. [68]

    Atmospheric Radiation Measurement Climate Research Facility US Department of Energy (ARM) site at the North Slope of Alaska (NSA), (2024),https://arm.gov/capabilities/ observatories/nsa

    2024

  60. [69]

    and Göttsche, F.-M.: Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa,Remote Sensing,15(13), 3281, (2023), 10.3390/rs15133281

    Pérez-Planells, L., Ghent, D., Ermida, S., Martin, M. and Göttsche, F.-M.: Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa,Remote Sensing,15(13), 3281, (2023), 10.3390/rs15133281

    2023 doi

  61. [70]

    et al.: Validation of A VHRR land surface temperature with MODIS and in situ LST—A TIMELINE thematic processor,Remote Sens.,13(17), 3473, (2021), 10.3390/rs13173473

    Reiners, P. et al.: Validation of A VHRR land surface temperature with MODIS and in situ LST—A TIMELINE thematic processor,Remote Sens.,13(17), 3473, (2021), 10.3390/rs13173473

    2021 doi

  62. [71]

    Trigo, I. et al.: Algorithm Theoretical Basis Document for Land Surface Tempera- ture (LST) Product, Instituto Português do Mar e da Atmosfera (IPMA), Technical Re- port SAF/LAND/IM/ATBD_ELST/1.2, Issue 2, Last revised: 02 June 2017, (2017), https://nextcloud.lsasvcs.ipma.pt/s...

    2017

  63. [72]

    W., Huang, H.-L., Li, J

    Borbas, E., Seemann, S. W., Huang, H.-L., Li, J. and Menzel, W. P.: Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity,Proc. 14th In- ternational ATOVS Study Conference (IASC-XIV), Beijing, China, 763–770, (20...

    2005

  64. [73]

    et al.:MODTRAN4 Version 2 User’s Manual, Air Force Research Laboratory, Space Vehicles Directorate, Air Force Material Command, Hanscom AFB, MA, Technical Report, (2000)

    Berk, A. et al.:MODTRAN4 Version 2 User’s Manual, Air Force Research Laboratory, Space Vehicles Directorate, Air Force Material Command, Hanscom AFB, MA, Technical Report, (2000)

    2000

  65. [74]

    Karlsson, K. G. et al.: CLARA-A3: The third edition of the A VHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023,Earth Sys. Sci. Data,15(11), 4901–4926, (2023), 10.5194/essd-15-4901-2023

    1979 doi

  66. [75]

    N., and Taylor, K

    Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1),Geoscientific Model Development,10, 4619–4646, (2017), 10.5194/gmd-10-4619-2017

    2017 doi

  67. [76]

    J., Aldred, F

    Good, E. J., Aldred, F. M., Ghent, D. J., Veal, K. L. and Jimenez, C.: An Analysis of the Stabil- ity and Trends in theLST cci Land Surface Temperature Datasets over Europe,Earth Space Sci., (2022), 10.1029/2022ea002317. 25

    2022 doi

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