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Special Issue "Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector"

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

Deadline for manuscript submissions: 15 June 2023 | Viewed by 3866

Special Issue Editors

Dr. Yinan Li
E-Mail Website
Guest Editor
Xi’an Institute of Space Radio Technology, China Academy of Space Technology, Xi’an 710100, China
Interests: spaceborne microwave radiometer system; calibration
Dr. Xiaobin Yin
E-Mail Website
Guest Editor
College of Marine Technology, Ocean University of China, Qingdao 266100, China
Interests: microwave ocean remote sensing; sea surface salinity; tropical cyclone; sea surface wind
Special Issues, Collections and Topics in MDPI journals
Dr. Qingxia Li
E-Mail Website
Guest Editor
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: microwave remote sensing; antenna array and signal processing
Dr. Shubo Liu
E-Mail Website
Guest Editor
Xi’an Institute of Space Radio Technology, China Academy of Space Technology, Xi’an 710100, China
Interests: microwave radiation and scattering; sea surface winds; whitecap
Dr. Shiyang Tang
E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi'an 710071, China
Interests: synthetic aperture radar (SAR); ground moving target indication (GMTI); radar imaging; real-time SAR processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to organize a Special Issue on the microwave passive remote sensing of sea surface temperature (SST), salinity (SSS), and wind vector (SSW) in the Remote Sensing journal of MDPI. SST, SSS, and SSW are extremely important geophysical parameters in the ocean–atmosphere system and play key roles in understanding climate variation, weather prediction, and air–sea interactions. Passive microwave radiometers with large wavelength and strong penetration have been applied to monitor the open ocean for several decades and provide a large amount of valuable marine environmental information data. However, there are still challenges related to measuring these parameters with high accuracy and spatial resolution, especially in cold-water and coastal regions.

This Special Issue is focused on the latest developments in passive microwave remote sensing of the sea surface temperature, salinity, and wind vector. We welcome papers exploring the areas of microwave passive sensors, theory, and models of microwave radiation in field and laboratory experiments, including but not limited to the following topics:

  • Design and optimization of microwave sensors for measuring SST, SSS, and SSW;
  • Investigations of microwave radiation model of sea surface and retrieval algorithm;
  • Improvements of SST, SSS, and SSW products in cold-water and coastal areas;
  • The influence of rain and RFI on remote sensing measurements and the correction method;
  • New technology and methods aiming to enhance the measurement of SST, SSS, and SSW.

Dr. Yinan Li
Dr. Xiaobin Yin
Dr. Qingxia Li
Dr. Shubo Liu
Dr. Shiyang Tang
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 2500 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

  • Sea surface salinity
  • Sea surface temperature
  • Sea surface wind
  • Radiative transfer model
  • Cold-water region
  • Coastal region
  • Microwave radiometer

Published Papers (4 papers)

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Research

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Article
An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving
Remote Sens. 2022, 14(19), 5043; https://doi.org/10.3390/rs14195043 - 09 Oct 2022
Viewed by 524
Abstract
Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection [...] Read more.
Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer. Full article
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Article
The Dynamic Sea Clutter Simulation of Shore-Based Radar Based on Stokes Waves
Remote Sens. 2022, 14(16), 3915; https://doi.org/10.3390/rs14163915 - 12 Aug 2022
Viewed by 453
Abstract
The sea clutter model based on the physical sea surface can simulate radar echo at different times and positions and is more suitable for describing dynamic sea clutter than the traditional models based on statistical significance. However, when applying the physical surface model [...] Read more.
The sea clutter model based on the physical sea surface can simulate radar echo at different times and positions and is more suitable for describing dynamic sea clutter than the traditional models based on statistical significance. However, when applying the physical surface model to shore-based radar, the effects of wave nonlinearity, breaking wave, shadow, and radar footprint size must be considered. In this paper, a dynamic sea clutter simulation scheme based on a nonlinear wave is proposed that uses random Stokes waves instead of linear superposition waves to simulate the nonlinear dynamic sea surface and then calculates echo in the form of scattering cells. In this process, the relationship between wind speed and the nonlinear factor of the Stokes wave is derived, a simple model of shadow modulation is provided, and a method for appending the sea clutter spikes formed by breaking waves is developed. The experimental results show that the simulated sea clutter and the real measured clutter have good consistency in intensity, amplitude statistical distribution, Doppler spectrum, and spatiotemporal correlation. The proposed scheme is suitable for the sea clutter simulation of shore-based radar and can also adjust the relevant parameters to extend to other types of sea clutter simulation. Full article
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Article
Quantitative Measurement of Radio Frequency Interference for SMOS Mission
Remote Sens. 2022, 14(7), 1669; https://doi.org/10.3390/rs14071669 - 30 Mar 2022
Viewed by 760
Abstract
The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by [...] Read more.
The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by RFI impeding ocean salinity retrieval, especially in coastal areas such as the SCS (South China Sea), where the observed data has been discarded massively. At present, there is no way to eliminate the RFI impact on the retrieved salinity, other than by detecting and shutting down the emissions from the sources. However, it may be effective in a scientific sense if RFI can be quantified and applied to the salinity retrieval process. Therefore, this study proposes an RFI measuring method that can investigate contamination in both prominent and moderate respects, aroused either by on-site emissions or nearby continents. Based on the proposed method, two levels of hierarchical RFI maps of the SCS region, including the separated one and the merged one, are presented and discussed, indicating more severe contamination in northern and western SCS. Moreover, to verify the generalization of the method on open oceans far from continents, an area in the middle central Pacific is selected and tested. The result shows few or no RFI in this unattended region, which is consistent with the authors’ knowledge. This study presents the concept of the “RFI map” to describe the contamination, which will hopefully help researchers comprehend the RFI state intuitively and assist in ocean salinity retrieval statistically. Full article
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Technical Note
Non-Uniform Synthetic Aperture Radiometer Image Reconstruction Based on Deep Convolutional Neural Network
Remote Sens. 2022, 14(10), 2359; https://doi.org/10.3390/rs14102359 - 13 May 2022
Cited by 2 | Viewed by 726
Abstract
When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. [...] Read more.
When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. However, when using traditional methods for imaging, errors are usually introduced or some prior information is required. In this article, we propose a new IASR imaging method with deep convolution neural network (CNN). The frequency domain information is extracted through multiple convolutional layers, global pooling layers, and fully connected layers to achieve non-uniform synthetic aperture radiometer imaging. Through extensive numerical experiments, we demonstrate the performance of the proposed imaging method. Compared to traditional imaging methods such as the grid method and AFF method, the proposed method has advantages in image quality, computational efficiency, and noise suppression. Full article
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