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Special Issue "Spaceborne Radar Remote Sensing of Agricultural Canopies and Soil Moisture"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. George P Petropoulos

Department of Soil and Water Resources, Institute of Industrial and Forage Crops, Hellenic Agricultural Organization “Demeter” (former NAGREF), Directorate General of Agricultural Research, 1, Theofrastou St., 41335 Larisa, Greece
Website | E-Mail
Interests: earth observation; modeling; land surface interactions; soil moisture; evapotrasnpiration; land use/cover mapping & change detection; natural hazards; floods; wildfires; sensitivity analysis; soil vegetation atmosphere transfer modeling; operational products benchmarking
Guest Editor
Dr. Prashant K Srivastava

Remote Sensing Laboratory, Institute of Environment and Sustainable Development (IESD), Banaras Hindu University, Varanasi 221005, India
Website | E-Mail
Phone: +91-7571927744
Interests: microwave active and passive; optical/IR; hydrology; soil moisture; sensitivity and uncertainty analysis; artificial intelligence; geospatial technology; classification methods; simulation and modelling
Guest Editor
Prof. Rajendra Prasad

Department of Physics, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India
Website | E-Mail
Interests: microwave soil moisture; spatial disaggregation; machine learning techniques; crop remote sensing
Guest Editor
Dr. Tanvir Islam

NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Website | E-Mail
Interests: microwave remote sensing; radiometer calibration; retrieval algorithms; radiative transfer theory; data assimilation; mesoscale modelling; cloud and precipitation system; artificial intelligence in geosciences
Guest Editor
Dr. Dileep Kumar Gupta

Remote Sensing Laboratory, Institute of Environment and Sustainable Development (IESD), Banaras Hindu University, Varanasi 221005, India
Website | E-Mail
Interests: radar & radiometer; remote sensing of crops; soil moisture; spatial downscaling
Guest Editor
Dr. Manika Gupta

Department of Geology, University of Delhi, Delhi 110007, India
Website | E-Mail
Interests: land surface modelling; remote sensing; agricultural water management; optical/IR & microwave remote sensing

Special Issue Information

Dear Colleagues,

Spaceborne radar observations have been demonstrated for the monitoring of vegetation dynamics, retrieval of crop growth variables, and soil moisture in many studies. There are several spaceborne radar sensors which are operated at different frequencies bands, such as Ku band (Quicksat and Scatsat-1), X-band (TerraSAR-X and COSMO SkyMed), C-band (Sentinel-1 and Radarsat-2), and L-band (ALOS PALSAR and SAOCOM). As a well-settled science, the amount of backscattering depends on the wavelength and polarization of the microwave signal and shape and size of the scatter. Spaceborne sensors are operated in the wide range of microwave frequencies, and different polarizations may be useful to gather the valuable information of agriculture and soil surface studies for different purposes. The most important advantage of radar remote sensing is the ability to establish a great understanding of vegetation backscatter from agricultural crops at a field level due to its high-resolution capability compared to passive microwave sensors. It may be very useful for improved soil moisture retrieval from agricultural fields, agricultural crop monitoring, and for the study of drought water stress. The different applications of spaceborne radar data in the field of agricultural and soil moisture are considered here, such as water resource management, drought/flood monitoring, irrigation management, and hydrological studies. 

This Special Issue focuses on state-of-the-art research in spaceborne radar remote sensing related to agriculture and soil moisture applications. Contributions are invited for agriculture and soil moisture applications using different spaceborne radar sensors in various technical aspects, such as a wide range of frequencies (from Ku-band to L-band) and polarizations, new processing techniques, scattering theory, retrieval approaches, field experiments, data fusion, and assimilation. Contributions are also accepted relating to the operational use of spaceborne radar observations for decision-making, and the services provided to farmers for agricultural growth. The submissions can cover but need not be limited to the following topics:

  • Spaceborne radar remote sensing of agriculture and soil moisture retrieval;
  • Advances in polarimetric spaceborne radar applications of the agriculture and soil moisture;
  • The retrieval methods for soil moisture using spaceborne radar remote sensing;
  • The modeling for the retrieval of crop growth variables using spaceborne radar remote sensing;
  • Spaceborne radar remote sensing for agro-hydrological modeling;
  • Spaceborne radar remote sensing for irrigation scheduling;
  • Spaceborne radar remote sensing for drought/flood monitoringl
  • Related topics to the agricultural hydrology and water resources modeling using spaceborne radar remote sensing;
  • Any other related topic.

Dr. George P Petropoulos
Dr. Prashant K Srivastava
Prof. Rajendra Prasad
Dr. Tanvir Islam
Dr. Dileep Kumar Gupta
Dr. Manika Gupta
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 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. Sensors 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 1800 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.

Published Papers (1 paper)

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Research

Open AccessArticle
Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks
Sensors 2019, 19(14), 3209; https://doi.org/10.3390/s19143209
Received: 7 June 2019 / Revised: 15 July 2019 / Accepted: 18 July 2019 / Published: 21 July 2019
PDF Full-text (1560 KB) | HTML Full-text | XML Full-text
Abstract
The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the [...] Read more.
The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization. Full article
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