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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: closed (1 August 2023) | Viewed by 32980

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Special Issue Editors

Dr. Hugo Carreno-Luengo

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Guest Editor

NASA CYGNSS Mission, Climate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
Interests: GNSS-reflectometry; microwave radiometry; bistatic scattering; SmallSats; planetary sciences; water cycle; carbon cycle
Special Issues, Collections and Topics in MDPI journals

Dr. Chun-Liang Lin

E-Mail Website
Guest Editor

1. National Space Organization, Taiwan
2. Electrical Engineering Department, National Chung Hsing University, Taiwan
Interests: guidance and control; intelligent control; robust control

Special Issue Information

Dear Colleagues,

Small satellites are changing the paradigm in Earth remote sensing, taking advantage of innovative payloads. As such, the operation of constellations of these instruments has the potential to observe Earth’s dynamic processes with a higher spatio-temporal sampling than traditional techniques. In particular, the so-called Global Navigation Satellite Systems Reflectometry (GNSS-R) is a sort of L-band passive multi-static radar (as many transmitters as navigation satellites are in view) that provides a wide swath up to ~1500 km. GNSS-R spatio-temporal sampling properties could open new process insights on mesoscale studies, wind speed determination, soil moisture content determination, vegetation water content monitoring etc

This Special Issue aims to trigger the development of a potential virtual network of satellites providing inter-comparable data to the scientific community, based on the new GRSS Standard for GNSS-Reflectometry. New and novel GNSS-R scientific applications, methodologies, and retrieval algorithms are the focus of this Special Issue, including contributions from academia, international space agencies, and private industry. Works arising from present and future GNSS-R missions are invited to participle in this scientific forum:

CYGNSS
BuFeng-1
Spire CubeSats series
Fengyun-3 series
FSSCat
PRETTY
Triton
HydroGNSS

Dr. Hugo Carreno-Luengo
Dr. Chun‐Liang Lin
Guest Editors

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Keywords

GNSS-R ocean surface wind speed
GNSS-R ocean altimetry
GNSS-R soil moisture content
GNSS-R biomass
GNSS-R inland water bodies
GNSS-R cryosphere

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Published Papers (11 papers)

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19 pages, 6036 KiB

Open AccessArticle

Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space

by Mario Moreno, Maximilian Semmling, Georges Stienne, Mainul Hoque and Jens Wickert

Remote Sens. 2023, 15(20), 5049; https://doi.org/10.3390/rs15205049 - 20 Oct 2023

Viewed by 1855

Abstract

Coherent observations in GNSS reflectometry are prominent in regions with smooth reflecting surfaces and at grazing elevation angles. However, within these lower elevation ranges, GNSS signals traverse a more extensive atmospheric path, and increased ionospheric effects (e.g., delay biases) are expected. These biases can be mitigated by employing dual-frequency receivers or models tailored for single-frequency receivers. In preparation for the single-frequency GNSS-R ESA “PRETTY” mission, this study aims to characterize ionospheric effects under variable parameter conditions: elevation angles in the grazing range (5° to 30°), latitude-dependent regions (north, tropic, south) and diurnal changes (day and nighttime). The investigation employs simulations using orbit data from Spire Global Inc.’s Lemur-2 CubeSat constellation at the solar minimum (F10.7 index at 75) on March, 2021. Changes towards higher solar activity are accounted for with an additional scenario (F10.7 index at 180) on March, 2023. The electron density associated with each reflection event is determined using the Neustrelitz Electron Density Model (NEDM2020) and the NeQuick 2 model. The results from periods of low solar activity reveal fluctuations of up to approximately 300 TECUs in slant total electron content, 19 m in relative ionospheric delay for the GPS L1 frequency, 2 Hz in Doppler shifts, and variations in the peak electron density height ranging from 215 to 330 km. Sea surface height uncertainty associated with ionospheric model-based corrections in group delay altimetric inversion can reach a standard deviation at the meter level. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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24 pages, 4832 KiB

Open AccessArticle

Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States

by Qidi Luo, Yueji Liang, Yue Guo, Xingyong Liang, Chao Ren, Weiting Yue, Binglin Zhu and Xueyu Jiang

Remote Sens. 2023, 15(18), 4576; https://doi.org/10.3390/rs15184576 - 17 Sep 2023

Cited by 2 | Viewed by 1758

Abstract

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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18 pages, 9808 KiB

Open AccessArticle

The Correction Method of Water and Fresnel Reflection Coefficient for Soil Moisture Retrieved by CYGNSS

by Qi Wang, Jiaojiao Sun, Xin Chang, Taoyong Jin, Jinguang Shang and Zhiyong Liu

Remote Sens. 2023, 15(12), 3000; https://doi.org/10.3390/rs15123000 - 8 Jun 2023

Cited by 1 | Viewed by 1520

Abstract

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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26 pages, 7007 KiB

Open AccessArticle

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

by Jinwei Bu, Kegen Yu, Xiaoqing Zuo, Jun Ni, Yongfa Li and Weimin Huang

Remote Sens. 2023, 15(3), 590; https://doi.org/10.3390/rs15030590 - 18 Jan 2023

Cited by 18 | Viewed by 3561

Abstract

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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20 pages, 6755 KiB

Open AccessArticle

Quality Control of CyGNSS Reflectivity for Robust Spatiotemporal Detection of Tropical Wetlands

by Hironori Arai, Mehrez Zribi, Kei Oyoshi, Karin Dassas, Mireille Huc, Shinichi Sobue and Thuy Le Toan

Remote Sens. 2022, 14(22), 5903; https://doi.org/10.3390/rs14225903 - 21 Nov 2022

Cited by 3 | Viewed by 2247

Abstract

The aim of this study was to develop a robust methodology for evaluating the spatiotemporal dynamics of the inundation status in tropical wetlands with the currently available Global Navigation Satellite System-Reflectometry (GNSS-R) data by proposing a new quality control technique called the “precision index”. The methodology was applied over the Mekong Delta, one of the most important rice-production systems comprising aquaculture areas and natural wetlands (e.g., mangrove forests, peatlands). Cyclone Global Navigation Satellite System (CyGNSS) constellation data (August 2018–December 2021) were used to evaluate the spatiotemporal dynamics of the reflectivity Γ over the delta. First, the reflectivity Γ, shape and size of each specular footprint and the precision index were calibrated at each specular point and reprojected to a 0.0045° resolution (approximately equivalent to 500 m) grid at a daily temporal resolution (Lv. 2 product); then, the results were obtained considering bias-causing factors (e.g., the velocity/effective scattering area/incidence angle). The Lv. 2 product was temporally integrated every 15 days with a Kalman smoother (+/− 14 days temporal localization with Gaussian kernel: 1σ = 5 days). By applying the smoother, the regional-annual dynamics over the delta could be clearly visualized. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 Phased-Array type L-band Synthetic Aperture Radar-2 quadruple polarimetric scatter signals were compared and found to be nonlinearly correlated due to the influence of the incidence angle and the effective scattering area. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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21 pages, 5939 KiB

Open AccessArticle

Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

by Changyang Wang, Kegen Yu, Fangyu Qu, Jinwei Bu, Shuai Han and Kefei Zhang

Remote Sens. 2022, 14(14), 3507; https://doi.org/10.3390/rs14143507 - 21 Jul 2022

Cited by 14 | Viewed by 3080

Abstract

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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11 pages, 32927 KiB

Open AccessCommunication

A Coastal Experiment for GNSS-R Code-Level Altimetry Using BDS-3 New Civil Signals

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

Remote Sens. 2021, 13(7), 1378; https://doi.org/10.3390/rs13071378 - 3 Apr 2021

Cited by 19 | Viewed by 3283

Abstract

High temporal and spatial resolutions are the key advantages of the global navigation satellites system-reflectometry (GNSS-R) technique, while low precision and instabilities constrain its development. Compared with conventional Ku/C band nadir-looking radar altimetry, the precision of GNSS-R code-level altimetry is restricted by the smaller bandwidth and the lower transmitted power of the signals. Fortunately, modernized GNSS broadcast new open-available ranging codes with wider bandwidth. The Chinese BDS-3 system was built on 31 July 2020; its inclined geostationary orbit and medium circular orbit satellites provide B1C and B2a public navigation service signals in the two frequency bands of B1 and B2. In order to investigate their performance on GNSS-R code-level altimetry, a coastal experiment was conducted on 5 November 2020 at a trestle of Weihai in the Shandong province of China. The raw intermediate frequency data with a 62 MHz sampling rate were collected and post-processed to solve the sea surface height every second continuously for over eight hours. The precisions were evaluated using the measurements from a 26 GHz radar altimeter mounted on the same trestle near our GNSS-R setup. The results show that a centimeter-level accuracy of GNSS-R altimetry—based on B1C code after the application of the moving average—can be achieved, while for B2a code, the accuracy is about 10 to 20 cm. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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17 pages, 3680 KiB

Open AccessArticle

Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data

by Zhounan Dong and Shuanggen Jin

Remote Sens. 2021, 13(4), 570; https://doi.org/10.3390/rs13040570 - 5 Feb 2021

Cited by 35 | Viewed by 4357

Abstract

With the development of spaceborne global navigation satellite system-reflectometry (GNSS-R), it can be used for terrestrial applications as a promising remote sensing tool, such as soil moisture (SM) retrieval. The reflected L-band GNSS signal from the land surface can simultaneously generate coherent and incoherent scattering, depending on surface roughness. However, the contribution of the incoherent component was directly ignored in previous GNSS-R land soil moisture content retrieval due to the hypothesis of its relatively small proportion. In this paper, a detection method is proposed to distinguish the coherence of land GNSS-R delay-Doppler map (DDM) from the cyclone global navigation satellite system (CYGNSS) mission in terms of DDM power-spreading features, which are characterized by different classification estimators. The results show that the trailing edge slope of normalized integrated time-delay waveform presents a better performance to recognize coherent and incoherent dominated observations, indicating that 89.6% of CYGNSS land observations are dominated by the coherent component. Furthermore, the impact of the land GNSS-Reflected DDM coherence on soil moisture retrieval is evaluated from 19-month CYGNSS data. The experiment results show that the influence of incoherent component and incoherent observations is marginal for CYGNSS soil moisture retrieval, and the RMSE of GNSS-R derived soil moisture reaches 0.04 cm³/cm³. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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18 pages, 6425 KiB

Open AccessReview

The CYGNSS Mission: On-Going Science Team Investigations

by Hugo Carreno-Luengo, Juan A. Crespo, Ruzbeh Akbar, Alexandra Bringer, April Warnock, Mary Morris and Chris Ruf

Remote Sens. 2021, 13(9), 1814; https://doi.org/10.3390/rs13091814 - 6 May 2021

Cited by 26 | Viewed by 4003

Abstract

In 2012, the National Aeronautics and Space Administration (NASA) selected the CYclone Global Navigation Satellite System (CYGNSS) mission coordinated by the University of Michigan (UM) as a low-cost and high-science Earth Venture Mission. The CYGNSS mission was originally proposed for ocean surface wind speed estimation over Tropical Cyclones (TCs) using Earth-reflected Global Positioning System (GPS) signals, as signals of opportunity. The orbital configuration of each CYGNSS satellite is a circular Low Earth Orbit (LEO) with an altitude ~520 km and an inclination angle of ~35°. Each single Delay Doppler Mapping Instrument (DDMI) aboard the eight CYGNSS microsatellites collects forward scattered signals along four specular directions (incidence angle of the incident wave equals incidence angle of the reflected wave) corresponding to four different transmitting GPS spacecrafts, simultaneously. As such, CYGNSS allows one to sample the Earth’s surface along 32 tracks simultaneously, within a wide range of the satellites’ elevation angles over tropical latitudes. Following the Earth Science Division 2020 Senior Review, NASA announced recently it is extending the CYGNSS mission through 30 September 2023. The extended CYGNSS mission phase is focused on both ocean and land surface scientific investigations. In addition to ocean surface wind speed estimation, CYGNSS has also shown a significant ability to retrieve several geophysical parameters over land surfaces, such as Soil Moisture Content (SMC), Above Ground Biomass (AGB), and surface water extent. The on-going science team investigations are presented in this article. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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15 pages, 19184 KiB

Open AccessTechnical Note

Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation

by Jack Winkelried, Christopher Ruf and Scott Gleason

Remote Sens. 2023, 15(2), 333; https://doi.org/10.3390/rs15020333 - 5 Jan 2023

Cited by 4 | Viewed by 1991

Abstract

Using large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to those metrics. Orbits are defined by a set of Keplerian elements, and the relationship between those elements and the spatial and temporal coverage metrics are examined in order to maximize global and zonal (latitude-dependent) coverage. Additional optimization is performed by dividing a constellation into multiple orbit planes. An iterative method can be applied to this design process to compare the performance of current and previous designs. The main objective of this work is the design of optimized configurations of satellites in low Earth orbiting constellations to maximize the spatial and temporal sampling and coverage provided by its sensors. The key innovations developed are a new cost function which measures the temporal sampling properties of a satellite constellation, and the use of it together with existing cost functions for spatial sampling to design satellite constellations that optimize performance with respect to both performance metrics. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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16 pages, 4625 KiB

Open AccessTechnical Note

A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data

by Dongmei Song, Qiqi Zhang, Bin Wang, Cong Yin and Junming Xia

Remote Sens. 2022, 14(20), 5129; https://doi.org/10.3390/rs14205129 - 14 Oct 2022

Cited by 6 | Viewed by 2393

Abstract

Microwave remote sensing is widely applied in flood monitoring due to its independence from severe weather conditions, which usually restrict the usage of optical sensors. However, it is challenging to track the variation process of flood events in a timely manner by traditional active and passive microwave techniques, since they cannot simultaneously provide measurements with high spatial and temporal resolution. The emerging Global Navigation Satellite System Reflectometry (GNSS-R) technique with high spatio-temporal resolution offers a new solution to the dynamic monitoring of flood inundation. Considering the high sensitivity of GNSS-R signals to flooding, this paper proposes a dual-branch neural network (DBNN) with a convolution neural network (CNN) and a back propagation (BP) neural network for flood monitoring. The CNN module is used to automatically extract the abstract features from delay-Doppler maps (DDMs), while the BP module is fed with GNSS-R typical features, such as surface reflectivity and power ratio, as well as vegetation information from Soil Moisture Active Passive satellite (SMAP) data. In the experiments, the superiority of the DBNN method is firstly demonstrated by comparing it with the surface reflectivity and power ratio methods. Then, the spatio-temporal variation process of the 2020 South Asian flood events is analyzed by the proposed method based on Cyclone Global Navigation Satellite System (CYGNSS) data. The understanding of flood change processes could help enhance the capacity for resisting flood disasters. Full article

(This article belongs to the Special Issue GNSS-R Earth Remote Sensing from SmallSats)

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