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A modified flexible spatiotemporal data fusion model

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Abstract

Remote sensing spatiotemporal fusion models blend multi-source images of different spatial resolutions to create synthetic images with high resolution and frequency, contributing to time series research where high quality observations are not available with sufficient frequency. However, existing models are vulnerable to spatial heterogeneity and land cover changes, which are frequent in human-dominated regions. To obtain quality time series of satellite images in a human-dominated region, this study developed the Modified Flexible Spatial-temporal Data Fusion (MFSDAF) approach based on the Flexible Spatial-temporal Data Fusion (FSDAF) model by using the enhanced linear regression (ELR). Multiple experiments of various land cover change scenarios were conducted based on both actual and simulated satellite images, respectively. The proposed MFSDAF model was validated by using the correlation coefficient (r), relative root mean square error (RRMSE), and structural similarity (SSIM), and was then compared with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and FSDAF models. Results show that in the presence of significant land cover change, MFSDAF showed a maximum increase in r, RRMSE, and SSIM of 0.0313, 0.0109 and 0.049, respectively, compared to FSDAF, while ESTARFM performed best with less temporal difference of in the input images. In conditions of stable landscape changes, the three performance statistics indicated a small advantage of MFSDAF over FSDAF, but were 0.0286, 0.0102, 0.0317 higher than for ESTARFM, respectively. MFSDAF showed greater accuracy of capturing subtle changes and created high-precision images from both actual and simulated satellite images.

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Acknowledgements

This research received financial support by the National Natural Science Foundation of China (Grant Nos. 41601562 and 41761014), the National Key Research and Development Program of China (No. 2017YFC1502404), the China Institute of Water Resources and Hydropower Research Team Construction and Talent Development Project (No. JZ0145B752017), the Research Project for Young Teachers of Fujian Province (No. JAT160085).

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, College of Environment and Resources, Fuzhou University, Fuzhou, 350116, China

    Jia Tang, Jingyu Zeng, Rongrong Zhang, Yue Zeng, Zhanghua Xu & Qianfeng Wang

  2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Li Zhang & Qing Zhang

  3. College of Geography and Tourism, Anhui Normal University, Wuhu, 241000, China

    Jinghan Li

  4. College of Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Xingrong Li

  5. Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou, 350116, China

    Jie Zou

  6. Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD, 20740, USA

    Qianfeng Wang

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  1. Jia Tang
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  2. Jingyu Zeng
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  3. Li Zhang
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  5. Jinghan Li
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  7. Jie Zou
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  8. Yue Zeng
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  9. Zhanghua Xu
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  10. Qianfeng Wang
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  11. Qing Zhang
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Correspondence to Qianfeng Wang.

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Tang, J., Zeng, J., Zhang, L. et al. A modified flexible spatiotemporal data fusion model. Front. Earth Sci. 14, 601–614 (2020). https://doi.org/10.1007/s11707-019-0800-x

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