Call for Papers | Remote Sensing Special Issue | Monitoring Land Surface Dynamic with AVHRR Data

Remote Sensing SmallRemote Sensing is an international peer-reviewed open access monthly journal published by MDPI. The aim of this special issue is to compile the latest developments in AVHRR pre-processing (calibration, geo-coding) and ECV retrieval. Submission deadline is 7 December 2018.

The Global Climate Observing System (GCOS) has defined many essential climate variables (ECV) to be monitored to understand changes of land systems (biosphere, cryosphere, and hydrosphere). The World Meteorological Organization (WMO) reported that a time series of at least 30 years is needed to retrieve statistically significant changes of ECVs. Considering that these are extended periods of time, only a limited selection of satellites and sensors can be used for global monitoring—one of these is the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA satellites (since 1981) and on the EUMETSAT platform MetOp (since 2006).

The objective of the NOAA series was to deliver images on a regular basis, but not to use the data for quantitative investigations. The intended data application defines the AVHRR’s sensor design. For instance, onboard calibration for the VIS and NIR channels were not included, and the orbit of the NOAA satellites was not stable, which results in an orbit drift with changing overpass times over the equator, and leads to variable solar illumination over areas of interest throughout the lifetime of the satellite. Nevertheless, the four (five) spectral channels in VIS, NIR, and TIR, in addition to a spatial resolution of 1.1 km in nadir and high radiometric resolution of 10-bit, provides a very detailed view of clouds and the Earth surface—a milestone in satellite remote sensing.

The aim of this special issue of Remote Sensing is to compile the latest developments in AVHRR pre-processing (calibration, geo-coding) and ECV retrieval. This considers regional time series based on Local Area Coverage (LAC) data in 1.1 km in nadir and also global applications using Global Area Coverage (GAC) data in 4 km spatial resolution. The main focus is on the retrieval and validation of time series related to the following ECVs: lakes, snow cover, glaciers and ice caps, ice sheets, permafrost, albedo, land cover (including vegetation type), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), above-ground biomass, soil carbon, and fire disturbance. Additionally, papers describing new retrieval methods for the above-mentioned ECVs resulting in improved accuracy, which has to be documented with sound validation procedure, are welcome. Furthermore, authors of methodological papers focusing on novel approaches to determine the uncertainty of the retrieved products are encouraged to submit their work.

Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com. Manuscripts can be submitted until the 7 December 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 and 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 the 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. 

Submission deadline is 7 December 2018.

KEYWORDS:

  • Essential Climate Variables (lakes, snow, glaciers, ice sheets, albedo, land cover, vegetation, fire)
  • Long time series to fulfill the requirements of WMO
  • New retrieval methods for the above mentioned ECVs
  • Methodological papers focusing on uncertainty

For more information, please visit the MDPI website.