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

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

Deadline for manuscript submissions: closed (29 December 2023) | Viewed by 18535

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Special Issue Editors

Dr. Yinan Li

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Guest Editor

Xi’an Institute of Space Radio Technology, China Academy of Space Technology, Xi’an 710100, China
Interests: spaceborne microwave radiometer system; calibration
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaobin Yin

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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

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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

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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

Prof. Dr. Shiyang Tang

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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 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

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

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Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector (Second Edition) in Remote Sensing (1 article)

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16 pages, 7127 KiB

Open AccessArticle

An Effective Onboard Cold-Sky Calibration Strategy for Spaceborne L-Band Synthetic Aperture Radiometers

by Jingjing Ren, Huan Zhang, Zhongkai Wen, Yan Li and Qingjun Zhang

Remote Sens. 2024, 16(6), 971; https://doi.org/10.3390/rs16060971 - 10 Mar 2024

Viewed by 942

Abstract

The L band frequency shows high sensitivity to sea surface salinity. More stable brightness temperature (TB) measurements are required for L-band radiometers to reduce salinity retrieval errors than for high-frequency radiometers. Due to the complexity of L-band synthetic aperture radiometers, a carefully selected cold-sky target should be viewed using an L-band synthetic aperture radiometer for the purpose of absolute TB calibration since the celestial sky is relatively well characterized and stable in the L band. A novel, effective cold-sky calibration strategy is presented in this paper. The strategy of cold-sky calibration of the synthetic aperture radiometer is applied when and where the antenna main lobe points to the ‘flat’ celestial sky, and the impact of each type of foreign source, such as the sun or moon, on visibility values should be minimized in the meantime. Additionally, antenna thermal stability is also considered, which can cause antenna deformation, and the antenna patterns are affected. A high-precision and high-fidelity simulator is built for the cold-sky calibration optimized strategy. The orbital beta angle is introduced to characterize the variation in space environment temperatures. A planet that is considered spherical in shape requires significantly less computation than an ellipsoid one in the simulator. The trade-off study results for the planet shape assumption in the cold-sky calibration simulator are presented. Finally, the calibration uncertainty and performance are assessed. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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26 pages, 7212 KiB

Open AccessArticle

Fusion Method of RFI Detection, Localization, and Suppression by Combining One-Dimensional and Two-Dimensional Synthetic Aperture Radiometers

by Liqiang Zhang, Rong Jin, Qingjun Zhang, Rui Wang, Huan Zhang and Zhongkai Wen

Remote Sens. 2024, 16(4), 667; https://doi.org/10.3390/rs16040667 - 13 Feb 2024

Cited by 1 | Viewed by 1018

Abstract

Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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18 pages, 4899 KiB

Open AccessArticle

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

by Zhongkai Wen, Huan Zhang, Weiping Shu, Liqiang Zhang, Lei Liu, Xiang Lu, Yashi Zhou, Jingjing Ren, Shuang Li and Qingjun Zhang

Remote Sens. 2023, 15(17), 4149; https://doi.org/10.3390/rs15174149 - 24 Aug 2023

Cited by 1 | Viewed by 1324

Abstract

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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19 pages, 614 KiB

Open AccessArticle

An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving

by Yumei Guo, Qiang Li, Linrang Zhang, Juan Zhang and Zhanye Chen

Remote Sens. 2022, 14(19), 5043; https://doi.org/10.3390/rs14195043 - 9 Oct 2022

Cited by 2 | Viewed by 1850

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 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

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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22 pages, 6375 KiB

Open AccessArticle

The Dynamic Sea Clutter Simulation of Shore-Based Radar Based on Stokes Waves

by Feng Luo, Yao Feng, Guisheng Liao and Linrang Zhang

Remote Sens. 2022, 14(16), 3915; https://doi.org/10.3390/rs14163915 - 12 Aug 2022

Cited by 3 | Viewed by 2493

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 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

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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18 pages, 5566 KiB

Open AccessArticle

Quantitative Measurement of Radio Frequency Interference for SMOS Mission

by Ming Xu, Hongping Li, Haihua Chen and Xiaobin Yin

Remote Sens. 2022, 14(7), 1669; https://doi.org/10.3390/rs14071669 - 30 Mar 2022

Cited by 2 | Viewed by 2185

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 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

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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26 pages, 10327 KiB

Open AccessTechnical Note

Spectrum Extension of a Real-Aperture Microwave Radiometer Using a Spectrum Extension Convolutional Neural Network for Spatial Resolution Enhancement

by Guanghui Zhao, Yuhang Huang, Chengwang Xiao, Zhiwei Chen and Wenjing Wang

Remote Sens. 2023, 15(24), 5775; https://doi.org/10.3390/rs15245775 - 18 Dec 2023

Viewed by 932

Abstract

Enhancing the spatial resolution of real-aperture microwave radiometers is an essential research topic. The accuracy of the numerical values of brightness temperatures (BTs) observed using microwave radiometers directly affects the precision of the retrieval of marine environmental parameters. Hence, ensuring the accuracy of the enhanced brightness temperature values is of paramount importance when striving to enhance spatial resolution. A spectrum extension (SE) method is proposed in this paper, which restores the suppressed high-frequency components in the scene BT spectrum through frequency domain transformation and calculations, specifically, dividing the observed BT spectrum by the conjugate of the antenna pattern spectrum and applying a Taylor approximation to suppress error amplification, thereby extending the observed BT spectrum. By using a convolutional neural network to correct errors in the calculated spectrum and then reconstructing the BT through inverse fast Fourier transform (IFFT), the enhanced BTs are obtained. Since the extended BT spectrum contains more high-frequency components, namely, the spectrum is closer to that of the original scene BT, the reconstructed BT not only achieves an enhancement in spatial resolution, but also an improvement in the accuracy of BT values. Both the results from simulated data and satellite-measured data processing illustrate that the SE method is able to enhance the spatial resolution of real-aperture microwave radiometers and concurrently improve the accuracy of BT values. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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18 pages, 5672 KiB

Open AccessTechnical Note

Visibility Extension of 1-D Aperture Synthesis by a Residual CNN for Spatial Resolution Enhancement

by Guanghui Zhao, Qingxia Li, Zhiwei Chen, Zhenyu Lei, Chengwang Xiao and Yuhang Huang

Remote Sens. 2023, 15(4), 941; https://doi.org/10.3390/rs15040941 - 8 Feb 2023

Cited by 1 | Viewed by 1290

Abstract

In order to improve the spatial resolution of a one-dimensional aperture synthesis (1-D AS) radiometer without increasing the size of the antenna array, the method of visibility extension (VE) is proposed in this article. In the VE method, prior information about the visibility distribution of various scenes is learnt by a residual convolutional neural network (ResCNN). Specifically, the relationship between the distribution of low-frequency visibility and that of high-frequency visibility is learnt. Then, the ResCNN is used to estimate the high-frequency visibility samples from the low-frequency visibility samples obtained by the AS system. Furthermore, the low- and high-frequency visibility samples are combined to reconstruct the brightness temperature image of the scene, to enhance the spatial resolution of AS. The simulation and experiment both demonstrate that the VE method can enhance the spatial resolution of 1-D AS. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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13 pages, 2802 KiB

Open AccessTechnical Note

Array Configuration Design for Mirrored Aperture Synthesis Radiometers Based on Dual-Polarization Measurements

by Hao Li, Gang Li, Haofeng Dou, Chengwang Xiao, Zhenyu Lei, Rongchuan Lv, Yinan Li, Yuanchao Wu and Guangnan Song

Remote Sens. 2023, 15(1), 167; https://doi.org/10.3390/rs15010167 - 28 Dec 2022

Viewed by 1358

Abstract

In mirrored aperture synthesis (MAS), the antenna array determines the rank of the transformation matrix connecting the cross-correlations to the cosine visibilities. However, the transformation matrix is rank-deficient, resulting in errors in the reconstructed brightness temperature (BT) image. In this paper, the signal propagations for the vertically polarized wave and horizontally polarized wave are analyzed. Then, the optimization model of the antenna array based on dual-polarization is established. The optimal array configurations are presented, with the corresponding transformation matrices being almost column full ranks. Simulation results demonstrate the validity of the proposed optimization model. Full article

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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16 pages, 3916 KiB

Open AccessTechnical Note

Non-Uniform Synthetic Aperture Radiometer Image Reconstruction Based on Deep Convolutional Neural Network

by Chengwang Xiao, Xi Wang, Haofeng Dou, Hao Li, Rongchuan Lv, Yuanchao Wu, Guangnan Song, Wenjin Wang and Ren Zhai

Remote Sens. 2022, 14(10), 2359; https://doi.org/10.3390/rs14102359 - 13 May 2022

Cited by 8 | Viewed by 2115

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. 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

(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)

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