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

Yin, Zhixiang; Wu, Penghai; Foody, Giles M.; Wu, Yanlan; Liu, Zihan; Du, Yun; Ling, Feng

Abstract

© 1980-2012 IEEE. 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 temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) < 1.40 °C and average structural similarity (SSIM) > 0.971].

Citation

Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., & Ling, F. (2021). Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1808-1822. https://doi.org/10.1109/TGRS.2020.2999943

• Journal Article Type: Article
• Acceptance Date: May 30, 2020
• Online Publication Date: Jun 12, 2020
• Publication Date: Feb 1, 2021
• Deposit Date: Jun 23, 2020
• Publicly Available Date: Jun 24, 2020
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 2
• Pages: 1808-1822
• DOI: https://doi.org/10.1109/TGRS.2020.2999943
• Keywords: Electrical and Electronic Engineering; General Earth and Planetary Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/4646018
• Publisher URL: https://ieeexplore.ieee.org/document/9115890
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.