Special Issue "Remote Sensing of Drought Monitoring"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 August 2019).
Interests: drought and vegetation monitoring; remote sensing; agricultural development; food security, and climate change/variability at national and international levels
Special Issues and Collections in MDPI journals
Interests: drought monitoring and early warning; land use/land cver characterization; land surface phenology; ecological and natural resource applications
Drought is a complex and recurring natural disaster that occurs throughout the world and often has negative impacts on many sectors of society. Drought monitoring is challenging given the complex spatio-temporal dimensions of drought and its severity. Traditionally, drought monitoring has relied mainly upon climate-based indicators and indices such as the Standardized Precipitation Index and the Palmer Drought Severity Index. These climate-based indicators have proven useful for many applications. However, the spatial variability in drought conditions depicted in the associated maps are at a relatively broad scale, and often contain limited information about local-scale variations in drought severity across the landscape. In addition, climate-based drought indices maps may have a limited value because they provide a generalized spatial view of drought conditions and variations across large areas. Thus, improved and effective drought monitoring approaches are critical for supporting early warning systems and pro-active drought planning.
In the past few decades, satellite-based remote sensing has provided relatively high spatial resolution (i.e., local to synoptic scale) and high temporal resolution (i.e., hours to days) observations of the Earth. Remotely sensed imagery provides spatial continuous spectral measures across large areas that reflect both atmospheric and land surface characteristics. As a result, remote sensing data has been increasingly used for large-area drought monitoring. For example, several satellite-derived vegetation indices have been developed to monitor drought from local to global scales. Researchers are making progress in developing better drought monitoring tools to assess drought-related vegetation stress and evaluating with ground observations. In recent years, hybrid drought indices that integrate climate, satellite, and environmental data have been developed. In addition, remote sensing data collected by several recent satellite-based instruments have also been used to estimate several key variables related to drought that include land surface temperature, evapotranspiration, soil moisture, and precipitation. Satellite-based microwave and radar instruments are also increasingly being used for soil moisture and precipitation estimation.
Currently, an increasing number of new and/or more sophisticated remote sensing techniques have been used for estimating vegetation drought stress, evapotranspiration, soil moisture, ground water fluxes, and precipitation. As a result, the demand for the development of operational drought monitoring and early warning system (EWS) using these new technologies is growing in many parts of the world. Improved operational EWS may need more sophisticated analysis and modeling techniques, as well as improved scientific knowledge from the basic research. This Special Issue of Remote Sensing discusses recent advances in drought monitoring and prediction, presenting case studies conducted all over the world. Among the topics to be discussed are:
- New and improved remote sensing-based drought indices that could help in identifying, classifying, and communicating drought conditions
- Earth observations that include satellite, climate, oceanic, and biophysical data for efficient drought analysis and improved seasonal prediction
- Improved modelling techniques to combine or integrate drought indices based on various drought indicators
- Satellite-based soil moisture and evapotranspiration estimation
- Remote sensing-based precipitation estimation and evaluation
- Data mining and GIS applications to drought monitoring and prediction
- Building Drought Early Warning Systems (DEWSs) integrating remote sensing data
- Use of remote sensing data and applications for food security
Original research on these topics will be welcome for this Special Issue.Dr. Tsegaye Tadesse
Prof. Brian D Wardlow
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 2400 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.
- Drought monitoring and prediction
- Hybrid drought indices
- Satellite-derived Climate data
- Vegetation monitoring
- Satellite-derived Evapotranspiration
- Soil moisture and groundwater estimation
- Drought impact and Food security