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Cited by 137Advanced Image Fusion TechniquesRemote Sensing in AgricultureSoil Moisture and Remote Sensing

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Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network

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Yin, Zhixiang, Wu, Penghai, Foody, Giles M., Wu, Yanlan, Liu, Zihan, Du, Yun, Ling, Feng·2021·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2020.2999943
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Abstract

Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated t…

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Metadata

Authors
Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., & Ling, F.
Affiliations
  • Anhui University (CN)
  • University of Nottingham (GB)
  • Nanjing University (CN)
  • Innovation Academy for Precision Measurement Science and Technology, CAS (CN)
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-02
Volume
59
Issue
2
Pages
1808-1822
ISSN
  • 0196-2892 (ISSN-L)
  • 1558-0644
DOI
10.1109/tgrs.2020.2999943
OpenAlex
W3034422010
Cited by
137
References
0
Citations by year
2026: 18 · 2025: 24 · 2024: 22 · 2023: 24 · 2022: 27 · 2021: 19
FWCI
9.01
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (41501376)
  • Strategic Priority Research Program of Chinese Academy of Sciences (XDA 2003030201)
  • Innovation Group Project of Hubei Natural Science Foundation (2019CFA019)
  • open fund for Discipline Construction, Institute of Physical Science, and Information Technology at Anhui University
Topics
  • Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
Keywords
land surface temperature, spatiotemporal fusion, multi-scale convolutional neural network, Landsat, MODIS, ESTARFM

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