Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

By Xiaolin Zhu, Fangyi Cai, Jiaqi Tian, Trecia Kay-Ann Williams. Remote Sensing, 2018

459 citations recorded by the source corpus.

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.

Subfields: Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/248cbcccf183c51f8bb7e63b487d149c902d592a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Matteo Matteucci
2. Sudhir Kumar Singh
3. Ram Lakhan Ray
4. Sara Comai
5. Rajendra P. Sishodia
6. Ava Vali

In Media Technology

1. Matteo Matteucci
2. Sara Comai
3. Ava Vali

Citations in the corpus

• UAV & satellite synergies for optical remote sensing applications: A literature review (2021, unknown): “Spatial-temporal (Zhu et al., 2018): Pioneering studies demonstrated the contribution of spatial-temporal fusion, especially the Spatial Temporal Adaptive Re fl ectance Fusion Model algorithm (Gao et al., 2006).”
• Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “The work in [172], published in 2018, is a review on the state-of-the-art of spatio-temporal multi-modal data fusion studies.”
• Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “To take advantage of a wide variety of publicly available satellite data, several data fusion approaches have been proposed to combine high/moderate spatial resolution data with high temporal resolution data (and vice-versa) to generate high spatial–temporal resolution data products [121,122].”