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Cited by 22Advanced Image Fusion TechniquesSoil Moisture and Remote SensingRemote Sensing in Agriculture

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Spatiotemporal Fusion Network for Land Surface Temperature Based on a Conditional Variational Autoencoder

Chen, Yuncheng, Yang, Yingbao, Pan, Xin, Meng, Xiangjin, Hu, Jia·2022·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2022.3183114
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Abstract

High spatiotemporal resolution land surface temperature (LST) data are essential for dynamic monitoring and prediction in climate change research. Due to the limitations of remote sensing instruments, the current platforms have difficulty achieving a compromise between high spatial and temporal resolutions for LST products. In this study, we propose a spatiotemporal fusion network for land surface temperature based on a conditional variational autoencoder (CVAE-LSTFM). First, an improved network is designed based on the CVAE by reconstructing an encoder and a decoder. To generate fine LST images based on dense time series, a variational inference model is formulated to integrate coarse and fine LST image pairs in variational autoencoded latent space. In addition, a new compound loss function for the proposed training method is designed to reduce the effects of noise and outliers. Then, a pretraining mechanism is adopted to optimize the network training process, and the parameters can be transferred to the training network of the CVAE to accelerate network convergence. Finally, a novel weighting strategy that considers spatiotemporal variations in LST (LST consistency weighting) is employed to solve the spatiotemporal heterogeneity problem caused by the rapid changes in LST. The method is quantitatively tested and evaluated in the Heihe River Basin using FY-4A LST and MODIS LST from September 2019. Compared with two traditional models and two deep learning-based models, CVAE-L…

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Research works printed but not linked: 52

  1. 10.1016/j.scitotenv.2017.10.226 · DOI
  2. 10.1126/science.1160485 · DOI
  3. 10.1029/2010RG000345 · DOI · Paper page
  4. 10.1016/j.rse.2011.08.025 · DOI
  5. 10.1126/science.275.5299.502 · DOI
  6. 10.3390/rs70201798 · DOI
  7. 10.1109/TGRS.2016.2596290 · DOI
  8. 10.1109/MGRS.2021.3050782 · DOI
  9. 10.1109/TGRS.2006.872081 · DOI
  10. 10.1016/j.rse.2010.05.032 · DOI
  11. 10.1117/1.JRS.6.063569 · DOI
  12. 10.1080/17538947.2013.783131 · DOI
  13. 10.1109/LGRS.2012.2227930 · DOI
  14. 10.1016/j.rse.2014.09.013 · DOI
  15. 10.1109/36.763276 · DOI
  16. 10.1080/01431161.2015.1083633 · DOI
  17. 10.3390/s150924002 · DOI
  18. 10.1117/1.JRS.6.063507 · DOI
  19. 10.1039/C5EM00254K · DOI
  20. 10.1016/j.rse.2015.11.016 · DOI
  21. 10.1016/j.rse.2017.05.011 · DOI
  22. 10.1016/j.rse.2017.12.003 · DOI
  23. 10.1016/j.rse.2012.04.016 · DOI
  24. 10.1016/j.rse.2013.09.002 · DOI
  25. 10.11834/jrs.20211181 · DOI
  26. 10.1016/j.rse.2019.02.006 · DOI
  27. 10.1109/TSP.2006.881199 · DOI
  28. 10.1080/17538947.2016.1235621 · DOI
  29. 10.1109/TGRS.2012.2186638 · DOI
  30. 10.1109/JSTARS.2018.2797894 · DOI
  31. 10.3390/rs10071066 · DOI
  32. 10.3390/rs11242898 · DOI
  33. 10.1109/TGRS.2020.2999943 · DOI · Paper page
  34. 10.1007/978-3-319-46493-0_47 · DOI
  35. 10.1109/JSTARS.2017.2735443 · DOI · Paper page
  36. Ioffe, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 448
  37. Lopez, Information constraints on auto-encoding variational Bayes, 6114
  38. 10.1007/978-1-4612-4380-9_35 · DOI
  39. Wu, SRPGAN: Perceptual generative adversarial network for single image super resolution, 312
  40. 10.1016/j.neucom.2016.12.038 · DOI · Paper page
  41. 10.1109/ICASSP.2019.8683043 · DOI
  42. Cheng, 2002, Study on the sustainable development in Heihe River watershed from the view of ecological economics, J. Glaciol. Geocryol., 24, 335
  43. 10.1016/j.rse.2019.02.020 · DOI · Paper page
  44. 10.1016/j.jag.2018.04.006 · DOI
  45. Wenping, 2011, Validation of the MODIS land surface temperature products—A case study of the Heihe River Basin, Remote Sens. Technol. Appl., 26, 705
  46. 10.1029/2004JD005566 · DOI
  47. 10.2136/vzj2018.04.0072 · DOI
  48. 10.1109/78.134446 · DOI
  49. 10.1109/CVPR.2018.00068 · DOI · Paper page
  50. Ma, 2017, Review on validation of remotely sensed land surface temperature, Adv. Earth Sci., 32, 615
  51. Wu, 2014, Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example, J. Remote Sens., 18, 1011
  52. 10.1016/j.isprsjprs.2019.04.008 · DOI

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Metadata

Authors
Chen, Y., Yang, Y., Pan, X., Meng, X., & Hu, J.
Affiliations
Hohai University (CN)
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
60
Pages
1-13
ISSN
  • 0196-2892 (ISSN-L)
  • 1558-0644
DOI
10.1109/tgrs.2022.3183114
OpenAlex
W4285256423
Cited by
22
References
1
Citations by year
2026: 3 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 1
FWCI
1.77
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
Funders
General Programs of the National Natural Science Foundation of China (42071346)
Topics
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
land surface temperature, spatiotemporal fusion, conditional variational autoencoder, high spatial and temporal resolution, variational inference, compound loss function, pre-training, spatiotemporal heterogeneity, Fengyun-4A, MODIS, Heihe River Basin

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CoChat. (2026). Citation check: Spatiotemporal Fusion Network for Land Surface Temperature Based on a Conditional Variational Autoencoder. https://app.cochat.ai/papers/10.1109/tgrs.2022.3183114
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Chen, Y., Yang, Y., Pan, X., Meng, X., & Hu, J. (2022). Spatiotemporal Fusion Network for Land Surface Temperature Based on a Conditional Variational Autoencoder. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2022.3183114