Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Application of Microwave Remote Sensing in Earth’s Surface Observation
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Application of Microwave Remote Sensing in Earth’s Surface Observation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 4301

Special Issue Editor

Dr. Simonetta Paloscia

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Institute of Applied Physics, Florence, Italy
Interests: microwave remote sensing; soil moisture; vegetation biomass; snow water equivalent; SAR; microwave radiometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to take stock of the current state of knowledge on the interactions (emission and scattering) of microwaves with land surfaces (namely, bare rough soils, agricultural and forest vegetation, dry and wet snow cover, ocean and sea ice) for a quantitative estimate of geophysical parameters from the presently available and recent planned satellites. To do this, contributions could involve both experimental and theoretical studies concerning observations of the Earth’s surface using Radar (SAR and Scatterometers) and Microwave Radiometers. Also, the exploitation of more recent sensors such as GNSS-R satellites could be considered. Particular attention should be paid to the evaluation of the current state of the art in the accuracy of the retrieval of the parameters that characterize the Earth's surfaces and which can influence global changes.

Some potential topics of interest for this Special Issue are the potential of X band and lower frequencies in snow cover surveys in mountainous regions; capability of monitoring liquid water in wet snow; estimating vegetation biomass and sensitivity to soil moisture in dense forests. Suggestions for future satellites (including geostationary systems) are welcome. 

Dr. Paolo Pampaloni
Dr. Simonetta Paloscia
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

  • microwave radiometry
  • radar
  • earth observation
  • soil moisture
  • vegetation
  • snow
  • ocean
  • sea ice

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 policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

19 pages, 1782 KiB  
Article
Frequency-Constrained QR: Signal and Image Reconstruction
by Harrison Garrett and David G. Long
Remote Sens. 2025, 17(3), 464; https://doi.org/10.3390/rs17030464 - 29 Jan 2025
Viewed by 505
Abstract
Because a finite set of measurements is limited in the amount of spectral content it can represent, the reconstruction process from discrete samples is inherently band-limited. In the case of 1D sampling using ideal measurements, the maximum bandwidth of regular and irregular sampling [...] Read more.
Because a finite set of measurements is limited in the amount of spectral content it can represent, the reconstruction process from discrete samples is inherently band-limited. In the case of 1D sampling using ideal measurements, the maximum bandwidth of regular and irregular sampling is well known using Nyquist and Gröchenig sampling theorems and lemmas, respectively. However, determining the appropriate reconstruction bandwidth becomes difficult when considering 2D sampling geometries, samples with variable apertures, or signal to noise ratio limitations. Instead of determining the maximum bandwidth a priori, we derive an inverse method to simultaneously reconstruct a signal and determine its effective bandwidth. This inverse method is equivalent to incrementally computing a band-limited inverse using a frequency-constrained QR decomposition (FQR). Comparisons between reconstruction results using FQR and QR decompositions illustrate how FQR is less sensitive to noisy measurement errors, but it is more sensitive to high-frequency components. These methods are particularly useful in the reconstruction of remote sensing images from such as microwave radiometers and scatterometers. Full article
(This article belongs to the Special Issue Application of Microwave Remote Sensing in Earth’s Surface Observation)
►▼ Show Figures

Graphical abstract

14 pages, 5235 KiB  
Article
Mapping Extreme Wildfires Using a Critical Threshold in SMAP Soil Moisture
by Benjamin D. Goffin, Aashutosh Aryal, Quinton Deppert, Kenton W. Ross and Venkataraman Lakshmi
Remote Sens. 2024, 16(13), 2457; https://doi.org/10.3390/rs16132457 - 4 Jul 2024
Viewed by 1724
Abstract
This study analyzed the ground conditions that allowed some extreme wildfires in 2017 and 2023 to take such proportions and burn around 750,000 ha across Central Chile. Using publicly available satellite data, we examined the relationship between the burned areas from the Moderate [...] Read more.
This study analyzed the ground conditions that allowed some extreme wildfires in 2017 and 2023 to take such proportions and burn around 750,000 ha across Central Chile. Using publicly available satellite data, we examined the relationship between the burned areas from the Moderate Resolution Imaging Spectroradiometers (MODIS) and their antecedent soil moisture from the Soil Moisture Active Passive (SMAP) mission. We found that a small number of fires were responsible for disproportionately large burned areas and that these megafires (i.e., >10,000 ha) were more likely to exhibit relatively drier conditions in the months and days prior. Based on this, we tested various thresholds in low antecedent soil moisture to identify areas more prone to megafires. By differentiating the moisture conditions below and above 0.14 m3/m3, we were able to map all of the 2017 megafires, at least in part. Our classification balanced the success and errors in prediction, yielding 54.1% recall and 75.9% precision (well above the 56.3% baseline). For 2023, the burned areas could not be classified as accurately, due to differences in pre-fire conditions. Overall, our research provided new insights into the link between satellite-based soil moisture and extreme wildfire events. Among other things, this study demonstrated that certain critical thresholds in SMAP had predictive skill to identify conditions more conducive to megafires. Ultimately, this work can be expanded to other parts of the world in support of enhanced wildfire mitigation and management. Full article
(This article belongs to the Special Issue Application of Microwave Remote Sensing in Earth’s Surface Observation)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

17 pages, 7840 KiB  
Technical Note
Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data
by Xin Meng, Haihua Chen, Jun Liu, Kun Ni and Lele Li
Remote Sens. 2024, 16(3), 490; https://doi.org/10.3390/rs16030490 - 26 Jan 2024
Cited by 1 | Viewed by 1447
Abstract
The Arctic plays a crucial role in the intricate workings of the global climate system. With the rapid development of information technology, satellite remote sensing technology has emerged as the main method for sea ice surface temperature (IST) observation. To obtain Arctic IST, [...] Read more.
The Arctic plays a crucial role in the intricate workings of the global climate system. With the rapid development of information technology, satellite remote sensing technology has emerged as the main method for sea ice surface temperature (IST) observation. To obtain Arctic IST, we used the FengYun-3D Microwave Radiation Imager (FY-3D/MWRI) brightness temperature (Tb) data for IST inversion using multiple linear regressions. Measured data on IST parameters in the Arctic are difficult to obtain. We used the Moderate-Resolution Imaging Spectroradiometer (MODIS) MYD29 IST data as the baseline to obtain the coefficients for the MWRI IST inversion function. The relation between MWRI Tb data and MODIS MYD29 IST product was established and the microwave IST inversion equation was obtained for the months of January to December 2019. Based on the R2 results and the IST inversion results, we compared and analyzed the MWRI IST data from the months of January to April, November, and December with the Operation IceBridge KT19 IR Surface Temperature data and the Northern High Latitude Level 3 Sea and Sea Ice Surface Temperature (NHL L3 SST/IST). We found that compared MWRI IST with NHL L3 IST, the correlation coefficients (Corr) > 0.72, mean bias ranged from −1.82 °C to −0.67 °C, and the standard deviation (Std) ranged from 3.61 °C to 4.54 °C; comparing MWRI IST with KT19 IST, the Corr was 0.69, the bias was 0.51 °C, and the Std was 4.34 °C. The obtained error conforms to the precision requirement. From these results, we conclude that the FY-3D/MWRI Tb data are suitable for IST retrieval in the Arctic using multiple linear regressions. Full article
(This article belongs to the Special Issue Application of Microwave Remote Sensing in Earth’s Surface Observation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop