The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions
Open accessAbstract
Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.
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Self-citationA flexible spatiotemporal method for fusing satellite images with different resolutions
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Plant phenology and global climate change: Current progresses and challenges
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Self-citationSpatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network
Yin, Z., Wu, P., Foody, G. M., Wu, Y., Liu, Z., Du, Y., & Ling, F. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2999943 · Cited by 137
Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives
Mao, Q., Peng, J., & Wang, Y. (2021) · Remote Sensing · doi:10.3390/rs13071306 · Cited by 74
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Metadata
- Authors
- Zhu, X., Cai, F., Tian, J., & Williams, T.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-03-29
- Volume
- 10
- Issue
- 4
- Pages
- 527
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2795018073
- Cited by
- 523
- References
- 69
- FWCI
- 26.68
- Type
- journal article
- Language
- en
- Open access
- gold
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