Special Issue "Fusion of High-Level Remote Sensing Products"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 29207
Interests: spatial and temporal statistics; spatio-temporal fusion of remote sensing products; up-scaling and downscaling of spatial information; continours monitoring using time series remote sensing data
Interests: thermal infrared remote sensing; atmospheric radiation and surface energy balance
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Thermal Remote Sensing for Monitoring Terrestrial Environment
Special Issue in Remote Sensing: Remote Sensing of the Earth’s Radiation Budget
Interests: forest disturbance mapping; the estimation of biogeophysical variables from satellite data; data fusion of satellite products; scaling effect and scale transformation of biogeophysical variables
With the advancement of remote sensing technology and the stimulus of strong application demands, the number of Earth observation (EO) satellites is increasing rapidly, producing big EO data. Moreover, various high-level products are generated from big EO data. Due to sensor malfunctions, cloud contamination, atmospheric effects, retrieval algorithm defects, etc., high-level remote sensing products derived from single sensors are suspected to have spatial incompleteness, temporal discontinuity, and inconsistent quality; on the other hand, the same product derived from multisensor observations might be inconsistent both in the values and physical meaning. However, products derived from different remote sensors or by different algorithms are somewhat complementary in accuracy and spatiotemporal completeness. Data fusion or integration can combine the advantages of different data sources (remote sensing, in situ observations, model outputs, etc.), thus providing continuous spatial and temporal coverage on the one hand and reducing the uncertainty of fused or integrated data on the other hand. In light of the merits mentioned above, data fusion or integration has become a promising means of obtaining high-quality spatiotemporally complete remote sensing products. With recent advances and innovations in computing, such as cloud computing and high-performance computing, computing power and storage capacity are no longer obstacles, and it is possible to generate high-level remote sensing products at the regional and global scales using data fusion or integration technologies. In this context, reviewing the achieved progress on data fusion or integration and looking forward to future development hold great relevance.
In this Special Issue, we will compile the state-of-the-art data fusion or integration methods that address various aspects of generating spatiotemporally complete, high-level remote sensing products. Potential topics include but are not limited to the following:
- Scale effects in remote sensing products;
- Uncertainty quantification methods for fusing remote sensing products;
- Downscaling and upscaling methods;
- Evaluation or validation of remote sensing products;
- Data reconstruction l statistics for spatiotemporal data;
- Integration of multisource data;
- Fusion of multiscale remote sensing products;
- Data assimilation
- Machine learning;
- Evaluation or validation of fused remote sensing products.
Dr. Jie Cheng
Dr. Xin Tao
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- downscaling and upscaling
- spatiotemporal geostatistics