remotesensing-logo

Journal Browser

Journal Browser

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: closed (15 August 2024) | Viewed by 8657

Special Issue Editors


E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
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

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.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

30 pages, 10615 KiB  
Article
Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data
by Dev Dinesh, Shashi Kumar and Sameer Saran
Remote Sens. 2024, 16(18), 3539; https://doi.org/10.3390/rs16183539 - 23 Sep 2024
Viewed by 2101
Abstract
Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools [...] Read more.
Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications. Full article
Show Figures

Figure 1

23 pages, 20834 KiB  
Article
Inferring the Variability of Dielectric Constant on the Moon from Mini-RF S-Band Observations
by Shashwat Shukla, Gerald Wesley Patterson, Abhisek Maiti, Shashi Kumar and Nicholas Dutton
Remote Sens. 2024, 16(17), 3208; https://doi.org/10.3390/rs16173208 - 30 Aug 2024
Cited by 1 | Viewed by 1009
Abstract
The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for [...] Read more.
The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith’s physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R2 value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole–Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon. Full article
Show Figures

Figure 1

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
by David Mengen, Thomas Jagdhuber, Anna Balenzano, Francesco Mattia, Harry Vereecken and Carsten Montzka
Remote Sens. 2023, 15(9), 2282; https://doi.org/10.3390/rs15092282 - 26 Apr 2023
Cited by 8 | Viewed by 3756
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
Show Figures

Figure 1

Back to TopTop