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Synthetic Aperture Radar Remote Sensing for Geophysical and Biophysical Parameters Retrieval

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 3053

Special Issue Editors

Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun 248001, India
Interests: SAR; PolSAR; PolInSAR; SAR tomography; mathematical and physical modeling of microwave scattering and SAR remote sensing
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
Interests: satellite remote sensing (SAR and optical) of vegetation; process-based modeling of vegetation productions; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Indian Institute of Science Education and Research (IISER), Bhopal 462066, India
Interests: dynamics and morphology of rivers; effect of drainage congestion on flood inundation; application of remote sensing to monitor soil moisture and stream flow
Department of Informatics, University of Petroleum and Energy Studies (UPES), Dehradun 248001, India
Interests: SAR and PolSAR for advanced deep learning model; image processing; segmentation; land use and land cover classification; evolutionary computing and artificial intelligence
Department of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology Indore, Indore 453552, India
Interests: SAR remote sensing; multi-sensor fusion methods; change detection algorithms
Special Issues, Collections and Topics in MDPI journals
Research Geographer-15, Western Geographic Science Center, The U.S. Geological Survey (USGS), 2255 North Gemini Drive, Flagstaff, AZ 86001-1637, USA
Interests: hyperspectral remote sensing; global croplands; remote sensing science; food security; water security; big data; machine learning; artificial intelligence; cloud computing; agriculture; land use/land cover
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scientific community has widely used synthetic aperture radar (SAR) remote sensing for large-scale land use/land cover mapping and retrieval of bio- and geophysical parameters over the past few decades. Microwaves used in SAR remote sensing are sensitive to the structural and dielectric properties of the target objects. The active imaging nature of the SAR sensor enables it to receive information at any time, and the long wavelength of the microwave band of the electromagnetic spectrum enables it to provide information in any weather (even when the atmosphere is entirely cloud-covered). The development of polarimetric modeling approaches ensures accurate scattering information retrieval from different scatters within the resolution SAR cell. Interferometric SAR has shown the ability to have precise elevation at the level of a few meters and ground subsidence on the mm scale. The advantage of subsurface penetration and other unique capabilities of SAR data have been extensively used in solid earth, ecosystem, and cryosphere applications. Because of the increasing demand for SAR data, many space agencies have developed and launched advanced SAR sensors and provide tools and software for data processing for the convenience of students and researchers. Several state-of-the-art SAR sensors are planned for future missions, taking into account user and scientific objectives as well as the data requirements of a variety of thematic applications. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission is unique and the first dual-frequency spaceborne mission acquiring data from L-band (24-cm) as well as S-band (12-cm) polarimetric SAR for Earth observation that will be launched in 2024. Similarly, to meet the demand for long-wavelength SAR data with a high canopy penetration capability, the European Space Agency’s (ESA) P-band Biomass mission is planned to be dedicated to the biomass of tropical forests. These SAR missions will complement and supplement many optical remote sensing data now acquired by hundreds of satellites in hyperspectral, hyperspatial, and advanced multispectral modes. As new advanced sensors are being developed that can meet the demands of thematic applications along with many other types of information, new algorithms are also being developed by the scientific community to meet this need. These combinations of sensors, tools, and techniques offer many new opportunities to advance planet science from satellite remote sensing.

This Special Issue invites the submission of manuscripts that contribute to SAR, PolSAR, and InSAR model development for thematic solid earth, ecosystem, and cryosphere applications for the retrieval of geophysical and biophysical parameters using advanced airborne and spaceborne SAR remote sensing techniques, and that make a significant contribution to data processing. Papers that use SAR data in conjunction with optical remote sensing data from hyperspectral, hyperspatial, and advanced multispectral remote sensing to demonstrate advances in planet science are also welcome.

The focus of this Special Issue will be on advanced polarimetric and interferometric SAR remote sensing techniques, data processing, and potential thematic applications. However, papers that use SAR data along with optical remote sensing are also welcome, especially when real advances in planet science through utilizing data from multiple sensor platforms are demonstrated. A tentative list of topics on which manuscripts can be submitted is as follows:

  • Retrieval of forest parameters using SAR remote sensing;
  • Mapping and monitoring of agricultural crops;
  • Soil moisture retrieval by implementing modeling approaches on SAR data;
  • Scattering-based characterization of manmade and natural features using polarimetric decomposition models;
  • Machine learning and deep learning models for SAR backscatter-based classification of land use and land cover;
  • Monitoring of volcanic eruptions and lava flow using PolSAR and InSAR;
  • Mapping and monitoring of cultural and natural world heritage sites using spaceborne SAR data;
  • Retrieval of geo-/biophysical parameters and classification using SAR data in combination with other remote sensing data;
  • Snow parameter retrieval and mapping and monitoring of glacier surfaces using SAR data;
  • Utilization of SAR data for geological applications;
  • Land subsidence mapping and monitoring using temporal InSAR data;
  • Polarimetric modeling for geological, geomorphological, and structural parameters of the lunar surface.

Dr. Shashi Kumar
Dr. Rahul Raj
Dr. Gaurav Kumar
Dr. Anil Kumar
Dr. Unmesh Khati
Dr. Prasad S. Thenkabail
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 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 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

  • PolSAR
  • InSAR
  • PolInSAR
  • SAR tomography
  • geo-/biophysical parameters
  • land use and land cover classification
  • solid earth
  • ecosystem
  • cryosphere
  • NISAR

Published Papers (1 paper)

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Research

31 pages, 80291 KiB  
Article
High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series
Remote Sens. 2023, 15(9), 2282; https://doi.org/10.3390/rs15092282 - 26 Apr 2023
Cited by 4 | Viewed by 1984
Abstract
The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas [...] Read more.
The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3. Full article
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