You are currently viewing a new version of our website. To view the old version click .

Soil Moisture Retrieval using Radar Remote Sensing Sensors

This special issue belongs to the section “Remote Sensing in Geology, Geomorphology and Hydrology“.

Special Issue Information

Dear Colleagues,

Soil moisture plays an essential role in the understanding of the continental water cycle. It is a key parameter in the separation of precipitation water between infiltration, runoff and evapotranspiration processes and in water management. In this context, active microwave remote sensing has shown a high potential to retrieve surface soil moisture through the use of SAR and other radar sensors (scatterometer, altimeter, GNSS-R, etc.). In the last few years, with the arrival of new sensors with important capacities in terms of spatial and temporal resolutions, it becomes possible to propose operational soil moisture products and to assimilate this parameter in water process modeling. This Special Issue has as principal objective to present the principal algorithms and methodologies around the use of active sensors (Sentinel1, Alos-2, TERRASAR-X, RADARSAT, ASCAT, CYGNSS, etc.) in the estimation and use of soil moisture. Different topics are considered:

  • Signal physics of radar measurement and backscattering modeling over soil surfaces
  • Inversion algorithms to estimate soil moisture
  • Roughness and vegetation effects of radar signal
  • Synergy between radar and other sensors for soil moisture retrieval
  • Assimilation of soil moisture products in process models

Dr. Mehrez Zribi
Dr. Nicolas Baghdadi
Dr. Clément Albergel
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 250 words) can be sent to the Editorial Office for assessment.

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

  • Soil moisture
  • Radar
  • Scattering modeling
  • Assimilation
  • Agriculture applications
  • Hydrology applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Published Papers

Get Alerted

Add your email address to receive forthcoming issues of this journal.

XFacebookLinkedIn
Remote Sens. - ISSN 2072-4292