Special Issue "GPS/GNSS for Earth Science and Applications"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Dr. Chi O. Ao
E-Mail
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: GPS/GNSS remote sensing; radio occultation techniques and technology; planetary boundary layer; climate observations; extreme weather
Dr. Shu-peng Ho
E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration,NESDIS/STAR/SMCD,College Park, MD 20740-3818, USA
Interests: satellite remote sensing, GNSS applications on meteorology and climate
Dr. Derek Posselt
E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: Active and passive remote sensing; Cloud and precipitation processes; Data assimilation

Special Issue Information

Dear Colleagues,

Microwave signals in L- and S-bands from the ever-growing constellations of global navigation satellite systems (GNSS), which includes the global positioning system (GPS), provide unique opportunities to sense the Earth’s environments from a variety of observing geometries with relatively low-cost sensors. 

GNSS radio occultations (RO) have been used since the 1990s to probe the vertical structure of the atmosphere from the planetary boundary layer to the stratosphere, yielding unique thermodynamics information, even under the most extreme weather conditions. GNSS-RO measurements are fundamentally self-calibrating and do not require any external calibration source.  As a result, they can be assimilated into numerical weather prediction models without any bias correction, and are ideally suited for long-term climate monitoring.  In recent years, the potential values of GNSS reflections in a wide array of Earth science and applications, including coastal altimetry, ocean winds, and soil moisture, have garnered increasing attention.  With high spatial resolutions and rapid revisit times, these measurements can complement traditional sensors at a small fraction of the cost.

Contributions are solicited that include, but are not limited to, the following topics:

  • Assimilation of GNSS measurements in improving weather forecasts, climate monitoring and climate model assessments;
  • Unique process studies using GNSS and other synergistic observations (e.g., clouds and precipitation);
  • New simulation and retrieval methodologies;
  • Novel measurement and mission concepts;
  • Technology developments involving small satellites, such as CubeSats.
Dr. Chi O. Ao
Dr. Shu-peng Ho
Dr. Derek Posselt
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 papers will be 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 1800 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

  • GPS
  • GNSS
  • Radio occultation
  • Reflectometry

Published Papers (4 papers)

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Open AccessArticle
Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements
Remote Sens. 2019, 11(20), 2399; https://doi.org/10.3390/rs11202399 - 16 Oct 2019
Abstract
The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, [...] Read more.
The climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. Currently, a better understanding of the cloud-convective process lacks observational constraints, due to the difficulty in obtaining accurate, vertically resolved pressure, temperature, and water vapor structure inside and near convective clouds. This manuscript describes the potential advantages of collecting sequential radio occultation (RO) observations from a constellation of closely spaced low Earth-orbiting satellites. In this configuration, the RO tangent points tend to cluster together, such that successive RO ray paths are sampling independent air mass quantities as the ray paths lie “parallel” to one another. When the RO train orbits near a region of precipitation, there is a probability that one or more of the RO ray paths will intersect the region of heavy precipitation, and one or more would lie outside. The presence of heavy precipitation can be discerned by the use of the polarimetric RO (PRO) technique recently demonstrated by the Radio Occultations through Heavy Precipitation (ROHP) receiver onboard the Spanish PAZ spacecraft. This sampling strategy provides unique, near-simultaneous observations of the water vapor profile inside and in the environment surrounding heavy precipitation, which are not possible from current RO data. Full article
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
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Open AccessArticle
High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
Remote Sens. 2019, 11(19), 2272; https://doi.org/10.3390/rs11192272 - 28 Sep 2019
Abstract
This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals [...] Read more.
This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 × 0.0833 (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018. Full article
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
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Open AccessArticle
Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model
Remote Sens. 2019, 11(9), 1127; https://doi.org/10.3390/rs11091127 - 10 May 2019
Abstract
With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological [...] Read more.
With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days. Full article
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
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Open AccessTechnical Note
A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
Remote Sens. 2019, 11(16), 1858; https://doi.org/10.3390/rs11161858 - 09 Aug 2019
Abstract
This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time [...] Read more.
This manuscript describes real-time on-orbit instrument compatible open loop signal tracking techniques for Global Navigation Satellite Systems (GNSS) reflection observations. All GNSS-reflection (GNSS-R) satellite instruments require algorithms which run in real-time on-board the satellite, that are capable of predicting the code phase time delay and Doppler frequency of surface reflected signals. The algorithms presented here are for open loop tracking techniques in reflected GNSS signals for the purposed of making surface remote sensing observations. Initially, the algorithms are demonstrated using high resolution sampled data from the NASA Cyclone GNSS (CYGNSS) mission over ocean and land surfaces. Subsequently. the algorithm performance over ocean regions is analyzed in detail using a larger data set. As part of the analysis, the algorithm is assessed for its speed of convergence, to demonstrate general compatibility with spacecraft instrument processing limitations. Results indicate that over ocean regions is it possible to robustly predict in real time the Doppler frequency and code phase time delay of multiple reflected signal to sufficient precision to make science observations of the scattering surface. These algorithms are intended to provide a baseline technique and variations from which the scientific community can design more specialized algorithms for individual applications. Full article
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
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