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Applications of GNSS Reflectometry for Earth Observation III

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 14770

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Special Issue Editors

Dr. Nereida Rodriguez-Alvarez

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Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: bistatic radar; GNSS-R; freeze/thaw; vegetation characterization; sea ice; wetlands; urban/agricultural flooding
Special Issues, Collections and Topics in MDPI journals

Dr. Mary Morris

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: atmospheric & hydrologic science; geophysical remote sensing; passive microwave radiometry; GNSS-Reflectometry; inversion techniques; multi-sensor data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Joan Francesc Munoz-Martin

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: GNSS-R; microwave radiometry; nano-satellites; CubeSats; soil moisture; sea-ice; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The availability of data from missions such as the Cyclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1) has had a significant impact on the scientific return of the Global Navigation Satellite System Reflectometry (GNSS-R) measurements. Data from these missions demonstrate the capabilities of GNSS-R and build on many applications that relate the properties of scattered GNSS signals to geophysical parameters. TDS-1 provides global data coverage, while the constellation of CYGNSS spacecraft, although limited to the tropics (±37° latitude), provides observations on rapid timescales with high spatial resolution. Equally important are measurements from airborne and ground-based instruments; these data enable investigations of the sensitivity of GNSS-R measurements to different phenomena and their use in new applications at a local/regional scale.

We invite authors to submit their work on applications that use GNSS-R data for Earth science to this third edition following our first and second Special Issue of “Applications of GNSS Reflectometry for Earth Observation”. As in the previous editions, we encourage the submission of works related to the synergistic use of GNSS-R data with data from other sensors at the same or different frequency of operations, enhancing spatial resolution and/or temporal sampling to improve estimates of geophysical parameters. Topics considered for this Special Issue should emphasize practical applications and reach beyond theoretical and model-based studies. Topics suggested include, but are not limited to:

Ocean, land or cryosphere applications using GNSS-R;
Applications using GNSS-R ground-based or airborne measurements;
Applications using GNSS-R satellite measurements;
GNSS-R-based neural networks for specific applications;
GNSS-R-based classification algorithms for targeted applications;
GNSS-R and SAR/Radiometer/Optical combined products;
Downscaling or enhancement methods employing GNSS-R.

Dr. Nereida Rodriguez-Alvarez
Dr. Mary Morris
Dr. Joan Francesc Munoz-Martin
Guest Editors

Manuscript Submission Information

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Keywords

GNSS-R
cryosphere
near-surface ocean wind vector
soil moisture
terrestrial hydrology
biomass
ship detection
oil slick detection
neural networks
classification
downscaling

Published Papers (9 papers)

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Research

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19 pages, 6154 KiB

Open AccessArticle

Enhancing Soil Moisture Active–Passive Estimates with Soil Moisture Active–Passive Reflectometer Data Using Graph Signal Processing

by Johanna Garcia-Cardona, Nereida Rodriguez-Alvarez, Joan Francesc Munoz-Martin, Xavier Bosch-Lluis and Kamal Oudrhiri

Remote Sens. 2024, 16(8), 1397; https://doi.org/10.3390/rs16081397 - 15 Apr 2024

Viewed by 333

Abstract

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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18 pages, 6313 KiB

Open AccessArticle

Hybrid CNN-LSTM Deep Learning for Track-Wise GNSS-R Ocean Wind Speed Retrieval

by Sima Arabi, Milad Asgarimehr, Martin Kada and Jens Wickert

Remote Sens. 2023, 15(17), 4169; https://doi.org/10.3390/rs15174169 - 24 Aug 2023

Cited by 1 | Viewed by 1287

Abstract

The NASA Cyclone GNSS (CYGNSS) mission provides one Delay Doppler Map (DDM) per second along observational tracks. To account for spatiotemporal correlations within adjacent DDMs in a track, a deep hybrid CNN-LSTM model is proposed for wind speed prediction. The model combines convolutional and pooling layers to extract features from DDMs in one track, which are then processed by LSTM as a sequence of data. This method leads to a test RMSE of 1.84 m/s. The track-wise processing approach outperforms the architectures that process the DMMs individually, namely based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and a network based solely on fully connected (FC) layers, as well as the official retrieval algorithm of the CYGNSS mission with RMSEs of 1.92 m/s, 1.92 m/s, 1.93 m/s, and 1.90 m/s respectively. Expanding on the CNN-LSTM model, the CNN-LSTM+ model is proposed with additional FC layers parallel with convolutional and pooling layers to process ancillary data. It achieves a notable reduction in test RMSE to 1.49 m/s, demonstrating successful implementation. This highlights the significant potential of track-wise processing of GNSS-R data, capturing spatiotemporal correlations between DDMs along a track. The hybrid deep learning model processing the data sequentially in one track learns these dependencies effectively, improving the accuracy of wind speed predictions. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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20 pages, 7456 KiB

Open AccessArticle

Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection

by Stylianos Kossieris, Milad Asgarimehr and Jens Wickert

Remote Sens. 2023, 15(12), 3206; https://doi.org/10.3390/rs15123206 - 20 Jun 2023

Cited by 1 | Viewed by 1488

Abstract

Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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17 pages, 6300 KiB

Open AccessCommunication

Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement

by Pengjie Zhang, Zhiguo Pang, Jingxuan Lu, Wei Jiang and Minghan Sun

Remote Sens. 2023, 15(12), 3119; https://doi.org/10.3390/rs15123119 - 14 Jun 2023

Viewed by 1555

Abstract

Real-time and high-precision water level monitoring is crucial for the fields of hydrology, hydraulic engineering, and disaster prevention and control. The most prevalent method for measuring water level is through the use of water level gauges, which can be costly and have limited coverage. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has emerged as a promising approach for water level monitoring due to its low cost and high coverage. However, a limitation of current GNSS-R technology is the extended time required to record signals, which hinders its potential for real-time application. This paper introduces a novel real-time water level monitoring method based on GNSS dual-antenna attitude measurement and develops a model to invert water level based on baseline vector. This method uses double-difference observations to eliminate errors caused by various factors, such as satellite and receiver clock, and ionospheric and tropospheric delay. To avoid the impact of detecting and correcting cycle slips during real-time operations, a single-epoch calculation method is introduced. In order to verify the stability and reliability of our method, field tests were carried out at Dongshahe Station in Beijing. We obtained water level data with a time resolution of 1 Hz through field experiments. Experimental data collected from 12 May to 8 June 2022 and from 4 July to 8 August 2022 showed good agreement with on-site water gauge measurements, with root mean square errors of 2.77 cm and 2.54 cm, respectively. Experimental results demonstrate that this method can achieve high-precision, high-temporal-resolution water level monitoring. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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20 pages, 17633 KiB

Open AccessArticle

Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals

by Joan Francesc Munoz-Martin, Nereida Rodriguez-Alvarez, Xavier Bosch-Lluis and Kamal Oudrhiri

Remote Sens. 2023, 15(8), 2013; https://doi.org/10.3390/rs15082013 - 11 Apr 2023

Viewed by 1574

Abstract

Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm³/cm³ for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm³/cm³ when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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20 pages, 8444 KiB

Open AccessArticle

GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study

by Amadeu Gonga, Adrián Pérez-Portero, Adriano Camps, Daniel Pascual, Anton de Fockert and Peter de Maagt

Remote Sens. 2023, 15(3), 637; https://doi.org/10.3390/rs15030637 - 21 Jan 2023

Cited by 5 | Viewed by 1576

Abstract

Currently, eight million metric tons of plastic end up in the oceans every year, and microplastics in different forms are present in almost all water systems in the world: streams, rivers, lakes, or oceans, and even in our blood. Detection of marine litter is an urgent task. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the “Atlantic Basin” at the Deltares research institute (Delft, The Netherlands). Several types of wave conditions were created: sinusoidal and with JONSWAP spectrum, with different significant wave heights, and with different types of plastics and marine litter collected from the Dutch coast. Experimental results show the difficulty in detecting marine plastic litter based on a change of the reflected power. However, a statistical analysis of the GNSS-R estimated reflectivities (amplitude and phase) computed with very short integration times (coherent integration time T_coh = 1 ms, and no incoherent averaging: N_incoh = 1) show that it may be possible to detect large accumulations of some types of marine litter that dampen the water waves, such as nets, bottles in a net, food wraps, and bags. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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Review

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26 pages, 37961 KiB

Open AccessReview

GNOS-II on Fengyun-3 Satellite Series: Exploration of Multi-GNSS Reflection Signals for Operational Applications

by Yueqiang Sun, Feixiong Huang, Junming Xia, Cong Yin, Weihua Bai, Qifei Du, Xianyi Wang, Yuerong Cai, Wei Li, Guanglin Yang, Xiaochun Zhai, Na Xu, Xiuqing Hu, Yan Liu, Cheng Liu, Dongwei Wang, Tongsheng Qiu, Yusen Tian, Lichang Duan, Fu Li, Xiangguang Meng, Congliang Liu, Guangyuan Tan, Peng Hu, Ruhan Wu and Dongmei Song Show full author list Hide full author list

Remote Sens. 2023, 15(24), 5756; https://doi.org/10.3390/rs15245756 - 16 Dec 2023

Viewed by 909

Abstract

The Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the Chinese Fengyun-3E (FY-3E) satellite is the world’s first operational spaceborne mission that can utilize reflected signals from multiple navigation systems for Earth remote sensing. The satellite was launched into an 836-km early-morning polar orbit on 5 July 2021. Different GNSS signals show different characteristics in the observations and thus require different calibration methods. With an average data latency of less than 3 h, many near real-time applications are possible. This article first introduces the FY-3E/GNOS-II mission and instrument design, then describes the extensive calibration methods for the multi-GNSS measurements, and finally presents application results in the remote sensing of ocean surface winds, land soil moisture and sea ice extent. Especially, the ocean surface wind product has been used in operational applications such as assimilation in the numerical weather prediction model and monitoring of tropical cyclones. Currently, GNOS-II has been carried by FY-3E, FY-3F (launched in August 2023) and FY-3G (launched in April 2023). It will be also carried by future follow-on FY series and a more complete multi-GNSS reflectometry constellation will be established. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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29 pages, 2321 KiB

Open AccessReview

Latest Advances in the Global Navigation Satellite System—Reflectometry (GNSS-R) Field

by Nereida Rodriguez-Alvarez, Joan Francesc Munoz-Martin and Mary Morris

Remote Sens. 2023, 15(8), 2157; https://doi.org/10.3390/rs15082157 - 19 Apr 2023

Cited by 5 | Viewed by 3652

Abstract

The global navigation satellite system-reflectometry (GNSS-R) field has experienced an exponential growth as it is becoming relevant to many applications and has captivated the attention of an elevated number of research scholars, research centers and companies around the world. Primarily based on the contents of two Special Issues dedicated to the applications of GNSS-R to Earth observation, this review article provides an overview of the latest advances in the GNSS-R field. Studies are reviewed from four perspectives: (1) technology advancements, (2) ocean applications, (3) the emergent land applications, and (4) new science investigations. The technology involved in the GNSS-R design has evolved from its initial GPS L1 LHCP topology to include the use of other GNSS bands (L2, L5, Galileo, etc.), as well as consider RHCP/LHCP-receiving polarizations in order to perform polarimetric studies. Ocean applications have included developments towards ocean wind speed retrievals, swell and altimetry. Land applications have evolved considerably in the past few years; studies have used GNSS-R for soil moisture, vegetation opacity, and wetland detection and monitoring. They have also determined flood inundation, snow height, and sea ice concentration and extent. Additionally, other applications have emerged in recent years as we have gained more understanding of the capabilities of GNSS-R. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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Other

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14 pages, 3323 KiB

Open AccessTechnical Note

Improving Consistency of GNSS-IR Reflector Height Estimates between Different Frequencies Using Multichannel Singular Spectrum Analysis

by Jintao Lei, Wenhao Li and Shengkai Zhang

Remote Sens. 2023, 15(7), 1779; https://doi.org/10.3390/rs15071779 - 27 Mar 2023

Cited by 1 | Viewed by 1314

Abstract

Previous studies of GNSS-IR mainly focused on the legacy L1C signal; the potential of modernized signals (L2C and L5Q) has not yet been fully exploited. In this paper, we applied the Multichannel Singular Spectrum Analysis (M-SSA) method to extract common interference patterns from different frequencies simultaneously. The three-frequency (L1C, L2C, and L5Q) signal-to-noise ratio (SNR) measurements from a total of 840 satellite rising and setting arcs, occurring between day of year 250 to 279 in year 2020 and 2021, were used. By comparing GNSS-IR reflector heights obtained from the original and M-SSA-reconstructed SNR time series, we found that M-SSA significantly improves the between-frequency consistency, as shown by an increase in the values of R-squared of linear regression from (0.69, 0.67, 0.89) to (0.95, 0.96, 0.98), and a decrease in RMSE from (0.10 m, 0.10 m, 0.06 m) to (0.04 m, 0.04 m, 0.02 m) for S1C-S2C, S1C-S5Q, and S2C-S5Q pair, respectively. Our results validate (1) the effectiveness of the M-SSA method in extracting common interference patterns from multi-frequency SNR time series, and (2) the superiority of modernized civil signals L2C and L5Q over the legacy L1C signal in GNSS-IR studies. We also emphasize the important role that the L5 signal will play in future GNSS-IR research because of its compatibility and interoperability among different satellite navigation systems. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation III)

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