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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 25866

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Special Issue Editors

Prof. Kegen Yu

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Guest Editor

School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221000, China
Interests: GNSS reflectometry; ground-based and satellite-based positioning; remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Joon Wayn Cheong

E-Mail Website
Guest Editor

The Australian Centre for Space Engineering ResearchEngineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: spaceborne GNSS-R; GNSS-R receiver design; signal processing

Dr. Sajad Tabibi

E-Mail Website
Guest Editor

Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
Interests: GNSS-R based snow depth measurement; GNSS-R based sea surface altimetry

Special Issue Information

Dear Colleagues,

Since the concept of GNSS bistatic radar was proposed in 1988, GNSS-R has made great progress in both theory and practice. So many ground-based and airborne experiments have been conducted by researchers in the world. Also, six-time launches of relevant satellites have been successfully accomplished. UK-DMC satellite was launched on 27 September 2003, UK TDS-1 was launched on 8 July 2014, U.S.’s CYGNSS was launched on 15 December 2016, Japan’s WNISAT-1R was launched on 14 July 2017, China’s Bufeng-1 A/B satellites was launched on 5 June 2019, and UK DOT-1 satellite was launched On the 5 July 2019. In particular, the receivers carried with TDS-1 and CYGNSS satellites have generated a large amount of data which have been pre-processed and widely used by public.

This special issue focuses on recent advances in ground-based, airborne and spaceborne GNSS-R. Although spaceborne GNSS-R has recently drawn significant attention, ground-based and airborne GNSS-R are also vital in a range of applications. Therefore, the special issue aims to report the current research and development outcomes in GNSS-R, especially focuses on the advanced methodologies and innovative practice. Meanwhile, it is also intended to encourage researchers and engineers to expand the application of GNSS-R, adequately exploiting the advantages and effectively dealing with the disadvantages of this emerging technology. Topics of interests of this special issue include, but not limited to:

Hardware, software, and simulator

Advanced data processing and calibration techniques

Land applications

Cryosphere applications

Ocean applications

Innovative ground-based and airborne experiments

Fusion with other remote sensing technologies

Current and potential products

New and future satellite missions

Prof. Kegen Yu
Dr. Joon Wayn Cheong
Dr. Sajad Tabibi
Guest Editors

Manuscript Submission Information

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Keywords

GNSS reflectometry
Receiver design
Land, cryosphere and ocean applications
Dada processing, calibration and fusion
Products
Experimental campaigns
Satellite missions

Published Papers (9 papers)

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Research

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15 pages, 6909 KiB

Open AccessCommunication

Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression

by Sizhe Chen, Qingyun Yan, Shuanggen Jin, Weimin Huang, Tiexi Chen, Yan Jia, Shuci Liu and Qing Cao

Remote Sens. 2022, 14(9), 1961; https://doi.org/10.3390/rs14091961 - 19 Apr 2022

Cited by 5 | Viewed by 2029

Abstract

Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (

Γ

) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth [...] Read more.

Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (

Γ

) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD/

τ

) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived

Δ Γ

is modeled as a function of both variations in SM and VOD (

Δ S M

and

Δ τ

). For retrieving SM, ancillary

τ

data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating

Δ τ

is suggested as an alternative. Experimental evaluation is performed for the time span from August 2019 to July 2021. Excellent agreements between the final retrievals and referenced SMAP SM products are achieved for both training (1-year period) and test (1-year duration) sets. On the whole, overall correlation coefficients (r) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm

^{3}

/cm

^{3}

are obtained based on models using the SMAP and simulated

Δ τ

, respectively. The model without

τ

generates an r of 0.95 and an RMSE of 0.031 cm

^{3}

/cm

^{3}

. The efficiency and necessity of considering

τ

are thus confirmed by its enhancement based on correlation and RMSE against the one without

τ

, and the usefulness of approximating

Δ τ

by sinusoidal functions is also validated. Influences of SM statistics in terms of mean and variance on the retrieval accuracy are evaluated. This work unveils the interaction between CyGNSS data, SM, and

τ

and demonstrates the feasibility of integrating the

Δ τ

approximation function into a bilinear regression model to obtain SM results. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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15 pages, 2654 KiB

Open AccessArticle

Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

by Shuangcheng Zhang, Zhongmin Ma, Zhenhong Li, Pengfei Zhang, Qi Liu, Yang Nan, Jingjiang Zhang, Shengwei Hu, Yuxuan Feng and Hebin Zhao

Remote Sens. 2021, 13(24), 5181; https://doi.org/10.3390/rs13245181 - 20 Dec 2021

Cited by 34 | Viewed by 3899

Abstract

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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27 pages, 59215 KiB

Open AccessArticle

Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

by Yongchao Zhu, Tingye Tao, Jiangyang Li, Kegen Yu, Lei Wang, Xiaochuan Qu, Shuiping Li, Maximilian Semmling and Jens Wickert

Remote Sens. 2021, 13(22), 4577; https://doi.org/10.3390/rs13224577 - 14 Nov 2021

Cited by 5 | Viewed by 2444

Abstract

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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15 pages, 9920 KiB

Open AccessCommunication

Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China

by Wentao Yang, Fan Gao, Tianhe Xu, Nazi Wang, Jinsheng Tu, Lili Jing and Yahui Kong

Remote Sens. 2021, 13(22), 4561; https://doi.org/10.3390/rs13224561 - 13 Nov 2021

Cited by 13 | Viewed by 3001

Abstract

Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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15 pages, 15286 KiB

Open AccessArticle

Analysis and Mitigation of Crosstalk Effect on Coastal GNSS-R Code-Level Altimetry Using L5 Signals from QZSS GEO

by Yunqiao He, Tianhe Xu, Fan Gao, Nazi Wang, Xinyue Meng and Baojiao Ning

Remote Sens. 2021, 13(22), 4553; https://doi.org/10.3390/rs13224553 - 12 Nov 2021

Cited by 3 | Viewed by 1633

Abstract

Coastal Global Navigation Satellite System Reflectometry (GNSS-R) can be used as a valuable supplement for conventional tide gauges, which can be applied for marine environment monitoring and disaster warning. Incidentally, an important problem in dual-antenna GNSS-R altimetry is the crosstalk effect, which means that the direct signal leaks into the down-looking antenna dedicated to the reflected signals. When the path delay between the direct and reflected signals is less than one chip length, the delay waveform of the reflected signal is distorted, and the code-level altimetry precision decreases consequently. To solve this problem, the author deduced the influence of signal crosstalk on the reflected signal structure as the same as the multipath effect. Then, a simulation and a coastal experiment are performed to analyze the crosstalk effect on code delay measurements. The L5 signal transmitted by the Quasi-Zenith Satellite System (QZSS) from a geosynchronous equatorial orbit (GEO) satellite is used to avoid the signal power variations with the elevation, so that high-precision GNSS-R code altimetry measurements are achieved in the experiment. Theoretically and experimentally, we found there exists a bias in proportion to the power of the crosstalk signals and a high-frequency term related to the phase delay between the direct and reflected signals. After weakening the crosstalk by correcting the delay waveform, the results show that the RMSE between 23-h sea level height (SSH) measurements and the in-situ observations is about 9.5 cm. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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20 pages, 3450 KiB

Open AccessArticle

Robust Kalman Filter Soil Moisture Inversion Model Using GPS SNR Data—A Dual-Band Data Fusion Approach

by Lili Jing, Lei Yang, Wentao Yang, Tianhe Xu, Fan Gao, Yilin Lu, Bo Sun, Dongkai Yang, Xuebao Hong, Nazi Wang, Hongliang Ruan and José Darrozes

Remote Sens. 2021, 13(19), 4013; https://doi.org/10.3390/rs13194013 - 07 Oct 2021

Cited by 4 | Viewed by 2170

Abstract

This article aims to attempt to increase the number of satellites that can be used for monitoring soil moisture to obtain more precise results using GNSS-IR (Global Navigation Satellite System-Interferometric Reflectometry) technology to estimate soil moisture. We introduce a soil moisture inversion model by using GPS SNR (Signal-to-Noise Ratio) data and propose a novel Robust Kalman Filter soil moisture inversion model based on that. We validate our models on a data set collected at Lamasquère, France. This paper also compares the precision of the Robust Kalman Filter model with the conventional linear regression method and robust regression model in three different scenarios: (1) single-band univariate regression, by using only one observable feature such as frequency, amplitude, or phase; (2) dual-band data fusion univariate regression; and (3) dual-band data fusion multivariate regression. First, the proposed models achieve higher accuracy than the conventional method for single-band univariate regression, especially by using the phase as the input feature. Second, dual-band univariate data fusion achieves higher accuracy than single-band and the result of the Robust Kalman Filter model correlates better to the in situ measurement. Third, multivariate variable fusion improves the accuracy for both models, but the Robust Kalman Filter model achieves better improvement. Overall, the Robust Kalman Filter model shows better results in all the scenarios. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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17 pages, 5580 KiB

Open AccessArticle

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

by Kun Chen, Xinyun Cao, Fei Shen and Yulong Ge

Remote Sens. 2021, 13(18), 3725; https://doi.org/10.3390/rs13183725 - 17 Sep 2021

Cited by 11 | Viewed by 1644

Abstract

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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Review

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33 pages, 2964 KiB

Open AccessReview

Spaceborne GNSS Reflectometry

by Kegen Yu, Shuai Han, Jinwei Bu, Yuhang An, Zhewen Zhou, Changyang Wang, Sajad Tabibi and Joon Wayn Cheong

Remote Sens. 2022, 14(7), 1605; https://doi.org/10.3390/rs14071605 - 27 Mar 2022

Cited by 11 | Viewed by 4477

Abstract

This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium- and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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17 pages, 14917 KiB

Open AccessTechnical Note

Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

by Xiaohui Li, Dongkai Yang, Jingsong Yang, Guoqi Han, Gang Zheng and Weiqiang Li

Remote Sens. 2021, 13(9), 1832; https://doi.org/10.3390/rs13091832 - 07 May 2021

Cited by 6 | Viewed by 2492

Abstract

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds. Full article

(This article belongs to the Special Issue Recent Advances in GNSS Reflectometry)

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