Paper Citation Record · LEDGER

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.09872.

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2507.09872 v1

Coverage vector

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation 8d340197-1acb-4cdd-a558-6d8cc50df43d · outbound

This paper cites A new ther- mal fusion method to downscale land surface temperature to finer spatial resolution using sentinel-msi and landsat-oli/tirs imagery.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction A new ther- mal fusion method to downscale land surface temperature to finer spatial resolution using sentinel-msi and landsat-oli/tirs imagery

Reference 1

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Observation 4d7fcd9a-c3fd-4782-87dc-edcec42f8fea · outbound

This paper cites Compar- ison of diurnal variation of land surface temperature from goes-16 abi and modis instruments.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Compar- ison of diurnal variation of land surface temperature from goes-16 abi and modis instruments

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.234164Z

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Observation 65951acd-9e10-45bf-bd20-4a0b5830e18d · outbound

This paper cites Reconstructing historical climate fields with deep learning.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Reconstructing historical climate fields with deep learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.215751Z

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Observation d80cfce4-4401-42a3-806c-753017301eea · outbound

This paper cites Estimating the optimal broadband emissivity spectral range for calculating surface longwave net radiation.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Estimating the optimal broadband emissivity spectral range for calculating surface longwave net radiation

Reference 4

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Observation 1664acb4-8603-4d7c-9c8e-c4361346b48a · outbound

This paper cites Reconstruction of hourly all-weather land surface temperature by integrating reanaly- sis data and thermal infrared data from geostationary satel- lites (rtg).

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Reconstruction of hourly all-weather land surface temperature by integrating reanaly- sis data and thermal infrared data from geostationary satel- lites (rtg)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.173139Z

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Observation b7ba366f-3660-4c79-831c-fe5650b64ee5 · outbound

This paper cites an unresolved cited work.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Unresolved cited work

Reference 6

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Observation 9f0e2dbb-ed0b-4336-8a61-16b6df03d560 · outbound

This paper cites Representation of hetero- geneity effects in earth system modeling: Experience from land surface modeling.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Representation of hetero- geneity effects in earth system modeling: Experience from land surface modeling

Reference 7

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Observation 8ecd352d-a3a8-473a-acd9-8a72a6451aef · outbound

This paper cites Global surface temperature change.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Global surface temperature change

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.105676Z

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Observation 3851b9fb-7d62-4290-825e-2adf1c8886c3 · outbound

This paper cites Spatial-temporal super-resolution of satellite im- agery via conditional pixel synthesis.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Spatial-temporal super-resolution of satellite im- agery via conditional pixel synthesis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.073734Z

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Observation a6b0566f-dd31-4e4f-a5b7-8280233b700a · outbound

This paper cites Denoising and inpainting of sea surface temperature image with adversarial physical model loss.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Denoising and inpainting of sea surface temperature image with adversarial physical model loss

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:27.036295Z

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Observation 7e67a65c-d435-4d64-bd47-1eac38ff54c2 · outbound

This paper cites The next landsat satellite: The landsat data continuity mission.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction The next landsat satellite: The landsat data continuity mission

Reference 11

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Observation 3702c529-4bb5-486e-8c10-993e6df3b183 · outbound

This paper cites Advances in methodology and generation of all-weather land surface temperature products from polar-orbiting and geostationary satellites: A comprehensive review.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Advances in methodology and generation of all-weather land surface temperature products from polar-orbiting and geostationary satellites: A comprehensive review

Reference 12

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Observation b98c67e1-c807-433e-aa75-e42224dc5e30 · outbound

This paper cites Artificial intelligence reconstructs missing climate informa- tion.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Artificial intelligence reconstructs missing climate informa- tion

Reference 13

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Observation cfd78f55-a217-4870-a32a-60abc827e113 · outbound

This paper cites Spatial and temporal dis- tribution of clouds observed by modis onboard the terra and aqua satellites.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Spatial and temporal dis- tribution of clouds observed by modis onboard the terra and aqua satellites

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.929090Z

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Observation d4353446-425a-4440-a4f5-e6a0eaeed7a7 · outbound

This paper cites Uncertainty estimation method and landsat 7 global validation for the landsat surface temperature product.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Uncertainty estimation method and landsat 7 global validation for the landsat surface temperature product

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.899454Z

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Observation 7e92709d-7d84-46de-ac55-3e903e301a6b · outbound

This paper cites Lfsr: Low-resolution filling then super-resolution re- construction framework for gapless all-weather modis-like land surface temperature generation.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Lfsr: Low-resolution filling then super-resolution re- construction framework for gapless all-weather modis-like land surface temperature generation

Reference 16

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Observation 874024ab-9791-4119-99b3-8f61793c6cb7 · outbound

This paper cites Evaluation of machine learning algorithms in spatial down- scaling of modis land surface temperature.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Evaluation of machine learning algorithms in spatial down- scaling of modis land surface temperature

Reference 17

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Observation 66012272-bde2-4a31-9b1a-0108f132b1ac · outbound

This paper cites Climatenerf: Extreme weather synthesis in neural radiance field.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Climatenerf: Extreme weather synthesis in neural radiance field

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.822840Z

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Observation 899cab24-bf62-4f4b-847b-f4df60295b40 · outbound

This paper cites Satellite remote sensing of global land sur- face temperature: Definition, methods, products, and appli- cations.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Satellite remote sensing of global land sur- face temperature: Definition, methods, products, and appli- cations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.798694Z

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Observation 2d0c92a8-3b39-4293-9af1-6a828e98efda · outbound

This paper cites Deep feature gaussian processes for single-scene aerosol optical depth reconstruction.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Deep feature gaussian processes for single-scene aerosol optical depth reconstruction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.766634Z

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Observation 2ceb3901-6b9c-4dfb-8817-d5c1c3894eff · outbound

This paper cites Spatial vari- ability of diurnal temperature range and its associations with local climate zone, neighborhood environment and mortality in los angeles.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Spatial vari- ability of diurnal temperature range and its associations with local climate zone, neighborhood environment and mortality in los angeles

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.745774Z

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Observation 644be737-37ab-4f46-9ee6-1e22b9f77c71 · outbound

This paper cites Daily land surface temperature reconstruction in landsat cross-track areas us- ing deep ensemble learning with uncertainty quantification.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Daily land surface temperature reconstruction in landsat cross-track areas us- ing deep ensemble learning with uncertainty quantification

Reference 22

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Observation b9254eee-f4b4-4217-a312-37e9f6a9bfec · outbound

This paper cites Optically enhanced super- resolution of sea surface temperature using deep learning.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Optically enhanced super- resolution of sea surface temperature using deep learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.722569Z

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Observation 253f21fd-e0c2-4cae-bb18-fbcd63522cb7 · outbound

This paper cites Generation of modis-like land surface tem- peratures under all-weather conditions based on a data fu- sion approach.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Generation of modis-like land surface tem- peratures under all-weather conditions based on a data fu- sion approach

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.698244Z

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Observation 40d7c938-174e-4add-a15b-f3ffe9a5c82d · outbound

This paper cites PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models

Reference 25

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Observation fd66438d-6532-4a9b-86cf-a246dcbcee4d · outbound

This paper cites Deep learning to represent subgrid processes in climate mod- els.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Deep learning to represent subgrid processes in climate mod- els

Reference 26

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Observation 455d1f1e-a46a-40fc-b24b-a4c315f86b98 · outbound

This paper cites Im- proving land surface temperature estimation in cloud cover scenarios using graph-based propagation.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Im- proving land surface temperature estimation in cloud cover scenarios using graph-based propagation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.654717Z

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Observation 5b7361a7-7d0d-439c-aa93-9a4792c4ed29 · outbound

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Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Unresolved cited work

Reference 28

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Observation 362d0d89-906c-4ea7-a06c-20747c30f3a5 · outbound

This paper cites Evaluation of modis land surface tem- perature data to estimate air temperature in different ecosys- tems over africa.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Evaluation of modis land surface tem- perature data to estimate air temperature in different ecosys- tems over africa

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.517924Z

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Observation b4f2a188-923a-4437-a8ce-9e86db43cfe1 · outbound

This paper cites Environmental cooling provided by urban trees under extreme heat and cold waves in us cities.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Environmental cooling provided by urban trees under extreme heat and cold waves in us cities

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.273721Z

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Observation 5bbdf417-01aa-4286-abf0-7fb13444aa72 · outbound

This paper cites Artificial intelligence achieves easy-to-adapt nonlinear global temper- ature reconstructions using minimal local data.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Artificial intelligence achieves easy-to-adapt nonlinear global temper- ature reconstructions using minimal local data

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:26.102741Z

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Observation a1d60b2e-c119-4806-822f-0a377c690f34 · outbound

This paper cites Satellite observations and malaria: new opportunities for research and applications.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Satellite observations and malaria: new opportunities for research and applications

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:25.919557Z

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Observation abe1a2b5-dd67-4b6e-9591-e95a9fa368bc · outbound

This paper cites Spatially continuous and high- resolution land surface temperature product generation: A review of reconstruction and spatiotemporal fusion tech- niques.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Spatially continuous and high- resolution land surface temperature product generation: A review of reconstruction and spatiotemporal fusion tech- niques

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:25.592333Z

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Observation 09653657-f282-4c19-86e3-3fcd2600490c · outbound

This paper cites Generative image inpainting with con- textual attention.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Generative image inpainting with con- textual attention

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:25.389024Z

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Observation a3f71669-ae48-4f71-b334-dde7b3f19dfd · outbound

This paper cites A global seamless 1 km resolution daily land surface temperature dataset (2003–2020).

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:25.201373Z

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Observation bff25c4b-991f-4056-acf0-1b6816d1651a · outbound

This paper cites A practical reanalysis data and thermal infrared re- mote sensing data merging (rtm) method for reconstruction of a 1-km all-weather land surface temperature.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction A practical reanalysis data and thermal infrared re- mote sensing data merging (rtm) method for reconstruction of a 1-km all-weather land surface temperature

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:24.966987Z

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Observation c9593f89-6e91-46ed-80bb-366561e0ad72 · outbound

This paper cites Hourly mapping of sur- face air temperature by blending geostationary datasets from the two-satellite system of goes-r series.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Hourly mapping of sur- face air temperature by blending geostationary datasets from the two-satellite system of goes-r series

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:24.744025Z

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Observation 185281cd-ecf1-49d0-a400-dc4a6f2410c9 · outbound

This paper cites Reconstruction of land surface temperature under cloudy conditions from landsat 8 data using annual temperature cycle model.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction Reconstruction of land surface temperature under cloudy conditions from landsat 8 data using annual temperature cycle model

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:49:24.484780Z

Source-reported events for the cited work

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Observation 3aafe210-5e56-42d9-b348-ebb5c439855c · outbound

This paper cites On the Foundations of Earth and Climate Foundation Models.

Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction On the Foundations of Earth and Climate Foundation Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:49:23.943247Z

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