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Cited by 22Urban Heat Island MitigationRemote Sensing in AgricultureSoil Moisture and Remote Sensing

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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities

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Kustura, Katja, Conti, David, Sammer, Matthias, Riffler, Michael·2025·Remote Sensing
DOI 10.3390/rs17020318
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Abstract

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes.

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Figure 4 . Figure 4. Scheme of the CNN architecture used for the LST gap-filling methodology.
Figure 4 . Figure 4. Scheme of the CNN architecture used for the LST gap-filling methodology.CC-BY · From the PDF
Figure 6 . Figure 6. (a) Close-up of grid artifacts in ECOSTRESS LST observation, shown in grayscale to enhance visibility. (b) The corresponding model prediction corrects the artifacts and increases spatial details. (Location: WGS84 coordinates 48.65398 • N, 16.31361 • E. Observation date and time: 4 June 2022, 16:39:50).
Figure 6 . Figure 6. (a) Close-up of grid artifacts in ECOSTRESS LST observation, shown in grayscale to enhance visibility. (b) The corresponding model prediction corrects the artifacts and increases spatial details. (Location: WGS84 coordinates 48.65398 • N, 16.31361 • E. Observation date and time: 4 June 2022, 16:39:50).CC-BY · From the PDF
Figure 7 provides further analysis of the LST distributions for the two prediction ensembles, displayed through histograms and bivariate histograms. Panels (a) and (c) compare the predicted with observed LST distributions for tiles 32TPT and 33UWP, respectively. While the two datasets do not align perfectly, the model generally captures the major temperature peaks in terms of both width and height, indicating reasonable agreement. The bivariate histograms in panels (b) and (d) further reveal the relationship between the predicted and the true LST values in 33TPT and 33UWP, respectively. Most data points fall along the diagonal, confirming strong overall agreement, though some outliers are present. These deviations may originate from systemic data issues such as unmasked clouds (thin linear artifacts in the histograms), or from the model's tendency to overestimate low LST values in alpine areas [a larger concentration of outliers present in panel (b)]. Remote Sens. 2025, 17, x FOR PEER REVIEW 19 of 32
Figure 7 provides further analysis of the LST distributions for the two prediction ensembles, displayed through histograms and bivariate histograms. Panels (a) and (c) compare the predicted with observed LST distributions for tiles 32TPT and 33UWP, respectively. While the two datasets do not align perfectly, the model generally captures the major temperature peaks in terms of both width and height, indicating reasonable agreement. The bivariate histograms in panels (b) and (d) further reveal the relationship between the predicted and the true LST values in 33TPT and 33UWP, respectively. Most data points fall along the diagonal, confirming strong overall agreement, though some outliers are present. These deviations may originate from systemic data issues such as unmasked clouds (thin linear artifacts in the histograms), or from the model's tendency to overestimate low LST values in alpine areas [a larger concentration of outliers present in panel (b)]. Remote Sens. 2025, 17, x FOR PEER REVIEW 19 of 32CC-BY · From the PDF
Figure 8 . Figure 8. Histograms for the disjoint (above) and joint (below) distribution of the LST for all pixels across all afternoon model predictions considered in Table 4. The colored bars in the bivariate histograms indicate pixel count. (a,b) Results for tile 32TPT; the MAE and r 2 are, respectively, 1.96 • C and 0.81. (c,d) Results for tile 33UWP; the MAE and r 2 are, respectively, 1.39 • C and 0.73.
Figure 8 . Figure 8. Histograms for the disjoint (above) and joint (below) distribution of the LST for all pixels across all afternoon model predictions considered in Table 4. The colored bars in the bivariate histograms indicate pixel count. (a,b) Results for tile 32TPT; the MAE and r 2 are, respectively, 1.96 • C and 0.81. (c,d) Results for tile 33UWP; the MAE and r 2 are, respectively, 1.39 • C and 0.73.CC-BY · From the PDF
Figure 9 . Figure 9. Examples of data quality issues for ECOSTRESS observations showing LST in • C. (a) Issues identified in a single observation: missing data due to masking by the ECOSTRESS-QC layer (background image visible); fringe-pattern sensor artifacts that remain even after the QC mask is applied; incorrect georeferencing (cf. position of the river in the ECOSTRESS observation and the background image, the displacement is further accentuated by the double arrow). (Location: WGS84 coordinates: 47.26646 • N, 11.38075 • E. Observation date and time: 11 June 2022, 14:49:19). (b) Summer observation before masking by the QC layer. A cloud is clearly identifiable by the negative temperature values, inconsistent with the season in which the observation was taken, as well as by the spatial extent which does not follow any spatial features in the area. (Location: WGS84 coordinates: 47.1852 • N, 16.8806 • E. Observation date and time: 9 July 2022, 03:02:29). (c) The same observation as in (b) after masking by the QC layer, demonstrating that the cloud was not sufficiently masked.
Figure 9 . Figure 9. Examples of data quality issues for ECOSTRESS observations showing LST in • C. (a) Issues identified in a single observation: missing data due to masking by the ECOSTRESS-QC layer (background image visible); fringe-pattern sensor artifacts that remain even after the QC mask is applied; incorrect georeferencing (cf. position of the river in the ECOSTRESS observation and the background image, the displacement is further accentuated by the double arrow). (Location: WGS84 coordinates: 47.26646 • N, 11.38075 • E. Observation date and time: 11 June 2022, 14:49:19). (b) Summer observation before masking by the QC layer. A cloud is clearly identifiable by the negative temperature values, inconsistent with the season in which the observation was taken, as well as by the spatial extent which does not follow any spatial features in the area. (Location: WGS84 coordinates: 47.1852 • N, 16.8806 • E. Observation date and time: 9 July 2022, 03:02:29). (c) The same observation as in (b) after masking by the QC layer, demonstrating that the cloud was not sufficiently masked.CC-BY · From the PDF

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Metadata

Authors
Kustura, K., Conti, D., Sammer, M., & Riffler, M.
Affiliations
GeoVille (Austria) (AT)
Corresponding author
Riffler, Michael
ORCID
Riffler, Michael
Venue
Remote Sensing
Publisher
MDPI AG
Published
2025-01-17
Volume
17
Issue
2
Pages
318
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs17020318
OpenAlex
W4406526076
Cited by
22
References
4
Citations by year
2026: 6 · 2025: 16
FWCI
5.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
European Space Agency (ESA)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
Keywords
land surface temperature, deep learning, convolutional neural networks, ECOSTRESS, Sentinel-2, multi-source data fusion, meteorological observations, sensor artifacts, diurnal variation, climate change adaptation, urban heat, land use and land cover change, heat stress monitoring

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CoChat. (2026). Citation check: Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities. https://app.cochat.ai/papers/10.3390/rs17020318
The paper
Kustura, K., Conti, D., Sammer, M., & Riffler, M. (2025). Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities. Remote Sensing. https://doi.org/10.3390/rs17020318