Time Series Analysis in Remote Sensing: Algorithm Development and Applications
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 30662
Special Issue Editors
Interests: quantitative remote sensing; algorithm development; environmental modeling; phenology
Special Issues, Collections and Topics in MDPI journals
Interests: geophysical image processing; image classification; land cover; soil; remote sensing; vegetation
Special Issues, Collections and Topics in MDPI journals
Interests: agriculture; quantitative remote sensing; chlorophyll fluorescence; phenology
Special Issues, Collections and Topics in MDPI journals
Interests: time-series remote sensing; agricultural remote sensing; environmental remote sensing; vegetation/crop phenology; crop growth modelling
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; spatial data analysis; data fusion; vegetation phenology
Special Issues, Collections and Topics in MDPI journals
Interests: plant phenology; climate change ecology; vegetation remote sensing; alpine ecosystem; global change ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, planet Earth has been encountering extensive environmental changes caused either by the climate or human beings, such as the shrinking of inland lakes, the expansion of croplands, deforestation, desertification, and urbanization. Understanding these environmental changes, as well as ensuring that they are monitered in a timely fashion, is crucial for supporting our planet’s sustainable development.
Remote sensing provides us with a practical tool to monitor and quantify global changes. In particular, many archived satellite data have become freely available, which makes it possible to track the history of environmental changes in the long term. For example, the Landsat series data covering nearly the last 50 years have been made publicly accessible by the USA. MODIS also provides us daily observations of the globe from 2000 to now. Nevertheless, it is usually a difficult task to analyze a large amount of all available satellite data in terms of time-series observations. The analysis on time series data is much more challenging than just comparing several satellite imageries derived in different periods. Time series analysis usually needs to make computations on hundreds, or thousands, of remote sensing datasets, which must be calibrated, harmonized, filtered and/or interpolated to a frequent interval before mechanic analysis. Additionally, the satellite observation data are getting bigger and bigger every day, which poses an extra challenge to fully exploit the wealth of the latest information. Thanks to the rapid advancement of mathematic methods (e.g., machine learning, deep learning, and data assimilation) and cloud computation platforms (e.g., Google Earth Engine) in recent years, we have got some new opportunities to improve the analysis and applications of remote sensing time series data. However, there is still a clear need to share approaches and new ideas on time series analysis in remote sensing toward applications to all aspects of geosciences.
Consequently, a Special Issue entitled “Time Series Analysis in Remote Sensing: Algorithm Development and Applications” is being planned by the international journal, Remote Sensing, to address the technical challenges for time series analysis in remote sensing sciences and to demonstrate successful applications of remote sensing time series data in all aspects of geosciences.
We solicit your contributions in this field to our Remote Sensing Special Issue. Research or review articles with respect (but not necessarily restricted) to the following topics are welcome if remote sensing time series data are used: Data fusion, Classification algorithm, Machine learning, Filtering algorithm, Cloud computation, Cropland monitoring, Urbanization, Vegetation dynamics, Deforestation, Land surface phenology, Land use/cover mapping.
Dr. Wei Yang
Dr. Xuehong Chen
Dr. Cong Wang
Dr. Ruyin Cao
Dr. Xiaolin Zhu
Prof. Dr. Miaogen Shen
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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.
Keywords
- Data fusion, Classification algorithm
- Machine learning
- Filtering algorithm
- Cloud computation
- Cropland monitoring
- Urbanization
- Vegetation dynamics
- Deforestation
- Land surface phenology
- Land use/cover mapping
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