Guest Editors
Asso. Prof. Xiaobin Guan
Email: guanxb@whu.edu.cn
Affiliation: School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
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Research Interests: remote sensing; urban vegetation; vegetation index; spatial-temporal reconstruction; ecosystem carbon cycle; climate change
Asso. Prof. Xinghua Li
Email: lixinghua5540@whu.edu.cn
Affiliation: School of Remote Sensing Information Engineering, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
Homepage:
Research Interests: multitemporal remote sensing analysis and application, remote sensing image processing, deep learning, and sparse representation
Assistant Prof. Dong Chu
Email: chudong@ahnu.edu.cn
Affiliation: School of Geography and Tourism, Anhui Normal University, Wuhu, 241002, China
Homepage:
Research Interests: Time series remote sensing data processing and application, remote sensing image denoising
Summary
Remote sensing technology has undergone remarkable advancements over the past few decades, enabling the acquisition of large amounts of time series data from various open-access satellite sensors, such as Landsat, AHHRR, MODIS, and Sentinel. These datasets offer a wealth of information about land surface dynamics and human activities, making them indispensable for applications such as land cover mapping, change detection, land surface monitoring, and global change studies. However, fully unlocking the potential of time series remote sensing data presents significant challenges. Key obstacles include the negative impacts of data quality issues (e.g., noise, cloud cover, and missing data), as well as the difficulty of integrating multi-sensor datasets with differing spatial, temporal, and spectral resolutions. Moreover, developing advanced methods for processing, analyzing, and interpreting these datasets is essential for extracting meaningful information and supporting decision-making. Addressing these challenges is crucial for ensuring the usability and reliability of the time series remote sensing data for scientific and practical purposes.
The proposed special issue aims to share innovative methodologies, applications, and findings related to time series remote sensing data. The key focus areas include but are not limited to:
· Time series remote sensing data reconstruction and filtering
· Multi-sensor time series harmonization and fusion
· Land use and land cover mapping and datasets
· Time series classification, recognition, and change detection
· Vegetation monitoring, including phenology, disturbance, and productivity
· Machine learning and deep learning methods for time series remote sensing analysis
· Cloud computing for long-time series remote sensing data processing
· Applications of time series remote sensing data in related fields
Keywords
Time series remote sensing, Filtering and gap-filling, Spatial-temporal-spectral fusion, Vegetation dynamics and phenology, Land cover/use mapping, Change detection, Machine learning and deep learning, Cloud computing
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