Special Issue "Land Surface Phenology "
Deadline for manuscript submissions: closed (30 September 2019).
Interests: land surface phenology; Earth observation; biophysical variables; agriculture
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Special Issue in Remote Sensing: Recent Advances in Satellite Derived Global Land Product Validation
Interests: satellite imagery; phenology; canopy evaporation; atmospheric carbon dioxide
Interests: image processing; Remote Sensing; machine learning,remote sensing,image classification, high resolution image
Land surface phenology refers to the type of products that seek to quantify and summarize the dynamics of the vegetated land surface at temporal scales from annual to seasonal. A common example would be the nominal starting and ending dates for growing seasons, estimated from time series of the normalized difference vegetation index. Over the last decade, there has been significant advances in data availability, image analysis and processing techniques that resulted in accurate characterization of Land Surface Phenology (LSP), from local to global scales. LSP information from satellites is a key variable to demonstrate the response of terrestrial ecosystem to climatic and anthropogenic changes. Moreover, LSP information is increasingly used to distinguish vegetation type and measure crop productivity. The recent launch of new satellite sensors, such as the Sentinel series, can provide the opportunity for improved characterization of LSP and may develop applications that were not possible with available datasets. Despite this, its validation with ground measurements is still challenging due to miss-match in both spatial and temporal scales between the two measurements, distribution of ground measurements and spatial heterogeneity of vegetation types in a satellite sensor pixel.
In this Special Issue, we are inviting submission including, but not limited to,
- Development of new or refined LSP products
- Development and/or production of high resolution (~30 m) LSP products
- Multi-sensor data integration for LSP estimation, particularly integration across optical-IR, microwave, and radar data.
- Characterization of LSP at local to global scales to understand spatial temporal trends and their drivers.
- Validation of LSP using range of in situ data, including observations, citizen science, and phenology cameras.
- Inter comparison of LSP from different sensors and products
- Application of LSP information
- LSP for identification of land use/land cover, changes in land use/land cover, and detection of invasive species.
Prof. Jadu Dash
Dr. Matthew Jones
Dr. Victor Rodriguez-Galiano
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 papers will be 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 2200 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.
- Land Surface Phenology
- Optical-infrared, microwave, radar
- Phenology Validation
- Data Integration
- Vegetation Index