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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: 31 December 2024 | Viewed by 948

Special Issue Editors

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Guest Editor
Italian National Research Council, Florence, Italy
Interests: Microwave radiometry, Synthetic Aperture Radar, Observations of land surfaces: bare soils, agricultural vegetation, forests, snow cover (extension and quality); Retrieval algorithms of geophysical parameters from satellite and ground based sensors (radiometers and radar); Planning new satellite sensors. International experimental campaigns.

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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
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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

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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.


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

Published Papers (1 paper)

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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; - 26 Jan 2024
Viewed by 721
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
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