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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 3100

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Prof. Dr. Serge Reboul

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Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Maison de la Recherche Blaise Pascal BP 719, CEDEX, 62228 Calais, France
Interests: signal processing; information fusion; GNSS; radar; GNSS reflectometry
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Special Issue Information

Dear Colleagues,

The GNSS signal is a source of opportunity for several remote sensing applications, such as GNSS reflectometry and radio occultation. This signal of high quality is broadcast on several frequency bands by several satellite constellations. The global covering of the GNSS system allows remote sensing observations everywhere in the world.

This Special Issue focuses on signal processing methods used to extract from the GNSS signal the parameters to process remote sensing observations (SNR, phase, Doppler). GNSS observations have a low signal-to-noise ratio. This is why a number of research works focus on the joint use of the signals of the bi-static radar system and on the joint use of the different bandwidths and constellations of the GNSS system.

In this context, applicative or methodological contributions to this Special Issue may include:

Open loop phase processing;
Assisted tracking;
Coherence of phase measurement;
Precise pseudo-range estimation;
Carrier-to-noise estimation;
Modern and multiband GNSS signal processing.

Prof. Dr. Serge Reboul
Guest Editor

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

GNSS reflectometry
signal processing
radio occultation
sensor fusion
signal-to-noise ratio
radar

Published Papers (4 papers)

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Research

Open AccessArticle

Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar

and

Remote Sens. 2023, 15(18), 4519; https://doi.org/10.3390/rs15184519 - 14 Sep 2023

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Abstract

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment. Full article

(This article belongs to the Special Issue New Advances in GNSS-R Signal Processing)

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Open AccessArticle

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

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Remote Sens. 2023, 15(13), 3218; https://doi.org/10.3390/rs15133218 - 21 Jun 2023

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Abstract

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When

r_{m} = 0.600

and

r_{n} = 0.700

, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology. Full article

(This article belongs to the Special Issue New Advances in GNSS-R Signal Processing)

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Open AccessArticle

Improving Spaceborne GNSS-R Algal Bloom Detection with Meteorological Data

Yinqing Zhen

and

Qingyun Yan

Remote Sens. 2023, 15(12), 3122; https://doi.org/10.3390/rs15123122 - 15 Jun 2023

Viewed by 459

Abstract

Algal bloom has become a serious environmental problem caused by the overgrowth of plankton in many waterbodies, and effective remote sensing methods for monitoring it are urgently needed. Global navigation satellite system-reflectometry (GNSS-R) has been developed rapidly in recent years, which offers a new perspective on algal bloom detection. When algal bloom emerges, the water surface will turn smoother, which can be detected by GNSS-R. In addition, meteorological parameters, such as temperature, wind speed and solar radiation, are generally regarded as the key factors in the formation of algal bloom. In this article, a new algal bloom detection method aided by machine learning and auxiliary meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation European Reanalysis (ERA-5) data with the application of the random under sampling boost (RUSBoost) algorithm. Experiments were carried out for Taihu Lake, China, over the period of August 2018 to May 2022. During the evaluation stage, the test true positive rate (TPR) of 81.9%, true negative rate (TNR) of 82.9%, overall accuracy (OA) of 82.9% and the area under (receiver operating characteristic) curve (AUC) of 0.88 were achieved, with all the GNSS-R observables and meteorological factors being involved. Meanwhile, the contribution of each meteorological factor and the error sources were assessed, and the results indicate that temperature and solar radiation play a prominent role among other meteorological factors in this research. This work demonstrates the capability of CYGNSS as an effective tool for algal bloom detection and the inclusion of meteorological data for further enhanced performance. Full article

(This article belongs to the Special Issue New Advances in GNSS-R Signal Processing)

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Open AccessArticle

Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS

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Remote Sens. 2022, 14(22), 5687; https://doi.org/10.3390/rs14225687 - 11 Nov 2022

Viewed by 1277

Abstract

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters. Full article

(This article belongs to the Special Issue New Advances in GNSS-R Signal Processing)

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