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Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With

Shengjie Liu1, Siqin Wang1, Lu Zhang2

  • 1Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA.

IEEE Transactions on Geoscience and Remote Sensing : a Publication of the IEEE Geoscience and Remote Sensing Society
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

We developed DELAG, a deep learning method, to reconstruct high-resolution land surface temperature (LST) in urban areas using Landsat data. This method improves data availability and accuracy, even in cloudy conditions.

Keywords:
Gaussian processesLandsatannual temperature cycledeep ensemble learningland surface temperaturereconstructionuncertainty quantification

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Area of Science:

  • Earth and Environmental Sciences
  • Remote Sensing
  • Climate Science

Background:

  • High spatiotemporal resolution land surface temperature (LST) is crucial for urban environmental studies.
  • Landsat data offers high spatial resolution but suffers from infrequent revisits and cloud interference, limiting its utility in complex urban landscapes.

Purpose of the Study:

  • To develop and validate DELAG, a deep ensemble learning method for reconstructing Landsat LST in complex urban areas.
  • To enhance LST data availability using Landsat's cross-track capabilities and dual-satellite operation.
  • To assess the reliability and uncertainty of reconstructed LST and its application in estimating near-surface air temperature.

Main Methods:

  • Implemented DELAG, a deep ensemble learning approach integrating annual temperature cycles and Gaussian processes.
  • Utilized Landsat data, enhanced by cross-track characteristics and dual-satellite operation, to increase data frequency to 4 scenes every 16 days.
  • Validated DELAG in New York City, London, and Hong Kong under varying cloud cover conditions.

Main Results:

  • DELAG successfully reconstructed Landsat LST in complex urban areas with low RMSE values (0.73-0.96 K clear-sky, 0.84-1.62 K cloudy).
  • The method provides uncertainty quantification, enhancing the reliability of LST reconstruction.
  • Reconstructed LST yielded accurate near-surface air temperature estimates (RMSE = 1.48-2.11 K), comparable to clear-sky LST results.

Conclusions:

  • DELAG offers a novel and practical solution for high-resolution Landsat LST reconstruction in complex urban environments.
  • The enhanced data availability and accuracy address limitations of traditional LST data acquisition.
  • This method advances the potential for studying complex climate events and improving air temperature estimations at high spatiotemporal resolutions.