scholarly journals Space-Borne GNSS-R Ionospheric Delay Error Elimination by Optimal Spatial Filtering

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5535
Author(s):  
Qiuyang Zhang ◽  
Yang Liu ◽  
Junming Xia
Keyword(s):  
Remote Sensing ◽  
Spatial Filtering ◽  
Satellite System ◽  
Sea Surface ◽  
Statistical Probability ◽  
Sensing Technology ◽  
Error Elimination ◽  
Absolute Bias ◽  
Global Navigation Satellite ◽  
Orbital Data

Global Navigation Satellite System Reflectometry (GNSS-R) technology is a new and promising remote sensing technology, especially satellite-based GNSS-R remote sensing, which has broad application prospects. In this work, the ionospheric impacts on space-borne GNSS-R sea surface altimetry were investigated. An analysis of optimal values for spatial filtering to remove ionospheric delays in space-borne GNSS-R altimetry was conducted. Considering that there are few satellite-borne GNSS-R orbit observations to date, simulated high-resolution space-borne GNSS-R orbital data were used for a comprehensive global and applicable study. The curves of absolute bias in relation to the bilateral filtering points were verified to achieve the minimum absolute bias. The optimal filtering points were evaluated in both statistical probability density and quantile analysis to show the reliability of the selected values. The proposed studies are helpful and valuable for the future implementation of high-accuracy space-borne GNSS-R sea surface altimetry.

scholarly journals Measurement of the Sea Surface Height with Airborne GNSS Reflectometry and Delay Bias Calibration

2021 ◽  
Vol 13 (15) ◽  
pp. 3014
Author(s):  
Feng Wang ◽  
Dongkai Yang ◽  
Guodong Zhang ◽  
Jin Xing ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Arrival Time ◽  
Elevation Angle ◽  
Satellite System ◽  
Sea Surface Height ◽  
Antenna Pattern ◽  
Sea Surface ◽  
Observation Method ◽  
Global Navigation Satellite ◽  
The Impact

Sea surface height can be measured with the delay between reflected and direct global navigation satellite system (GNSS) signals. The arrival time of a feature point, such as the waveform peak, the peak of the derivative waveform, and the fraction of the peak waveform is not the true arrival time of the specular signal; there is a bias between them. This paper aims to analyze and calibrate the bias to improve the accuracy of sea surface height measured by using the reflected signals of GPS CA, Galileo E1b and BeiDou B1I. First, the influencing factors of the delay bias, including the elevation angle, receiver height, wind speed, pseudorandom noise (PRN) code of GPS CA, Galileo E1b and BeiDou B1I, and the down-looking antenna pattern are explored based on the Z-V model. The results show that (1) with increasing elevation angle, receiver height, and wind speed, the delay bias tends to decrease; (2) the impact of the PRN code is uncoupled from the elevation angle, receiver height, and wind speed, so the delay biases of Galileo E1b and BeiDou B1I can be derived from that of GPS CA by multiplication by the constants 0.32 and 0.54, respectively; and (3) the influence of the down-looking antenna pattern on the delay bias is lower than 1 m, which is less than that of other factors; hence, the effect of the down-looking antenna pattern is ignored in this paper. Second, an analytical model and a neural network are proposed based on the assumption that the influence of all factors on the delay bias are uncoupled and coupled, respectively, to calibrate the delay bias. The results of the simulation and experiment show that compared to the meter-level bias before the calibration, the calibrated bias decreases the decimeter level. Based on the fact that the specular points of several satellites are visible to the down-looking antenna, the multi-observation method is proposed to calibrate the bias for the case of unknown wind speed, and the same calibration results can be obtained when the proper combination of satellites is selected.

Performance Assessment of GNSS-R Polarimetric Observations for Sea Level Monitoring

2021 ◽  
Author(s):  
Mahmoud Rajabi ◽  
Mstafa Hoseini ◽  
Hossein Nahavandchi ◽  
Maximilian Semmling ◽  
Markus Ramatschi ◽  
...  
Keyword(s):  
Time Series ◽  
Circular Polarization ◽  
Sea Level ◽  
Tide Gauge ◽  
Satellite System ◽  
Sea Surface ◽  
Analysis Tool ◽  
Lower Accuracy ◽  
Coastal Sea ◽  
Global Navigation Satellite

<p>Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge.  </p><p>We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.</p><p>Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.</p>

An Assessment of CYGNSS Ocean Wind Speed Products

2021 ◽  
Author(s):  
Matthew Hammond ◽  
Giuseppe Foti ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Nicolas Floury
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Satellite System ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
The Earth ◽  
L1 Data ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Ocean Wind

<p>Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth’s surface. CYGNSS is a constellation of 8 satellites launched in 2016 which use GNSS-R technology for the remote sensing of ocean wind speed. The ESA ECOLOGY project aims to evaluate CYGNSS data which has recently undergone a series of improvements in the calibration approach. Using CYGNSS collections above the ocean surface, an assessment of Level-1 calibration is presented, alongside a performance evaluation of Level-2 wind speed products. L1 data collected by the individual satellites are shown to be generally well inter-calibrated and remarkably stable over time, a significant improvement over previous versions. However, some geographical biases are found, which appear to be linked to a number of factors including the transmitter-receiver pair considered, viewing geometry, and surface elevation. These findings provide a basis for further improvement of CYGNSS products and have wider applicability to improving calibration of GNSS-R sensors for remote sensing of the Earth.</p>

scholarly journals Determination of sea surface height from moving ships with dynamic corrections

2012 ◽  
Vol 2 (3) ◽  
pp. 172-187 ◽  
Author(s):  
J. Reinking ◽  
A. Härting ◽  
L. Bastos
Keyword(s):  
Satellite System ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Sea Surface Heights ◽  
Positioning Analysis ◽  
Remote Sensing Techniques ◽  
Global Navigation Satellite

AbstractWith the growing global efforts to estimate the influence of civilization on the climate change it would be desirable to survey sea surface heights (SSH) not only by remote sensing techniques like satellite altimetry or (GNSS) Global Navigation Satellite System reflectometry but also by direct and in-situ measurements in the open ocean. In recent years different groups attempted to determine SSH by ship-based GNSS observations. Due to recent advances in kinematic GNSS (PPP) Precise Point Positioning analysis it is already possible to derive GNSS antenna heights with a quality of a few centimeters. Therefore it is foreseeable that this technique will be used more intensively in the future, with obvious advantages in sea positioning. For the determination of actual SSH from GNSS-derived antenna heights aboard seagoing vessels some essential hydrostatic and hydrodynamic corrections must be considered in addition to ocean dynamics and related corrections. Systematic influences of ship dynamics were intensively analyzed and sophisticated techniques were developed at the Jade University during the last decades to precisely estimate mandatory corrections. In this paper we will describe the required analyses and demonstrate their application by presenting a case study from an experiment on a cruise vessel carried out in March 2011 in the Atlantic Ocean.

scholarly journals IWV retrieval from ship-borne GNSS receiver in the framework of the MAP-IO project

2021 ◽  
Author(s):  
Pierre Bosser ◽  
Joël Van Ballen ◽  
Olivier Bousquet
Keyword(s):  
Marine Biology ◽  
Weather Prediction ◽  
Vertical Component ◽  
Satellite System ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Gnss Receiver ◽  
Height Determination ◽  
Global Navigation Satellite ◽  
Height Model

<p>In the framework of the research project “Marion Dufresne Atmospheric Program – Indian Ocean” (MAP-IO), which is aiming at collecting long-term atmospheric and marine biology observations in the under-instrumented Indian and Austral Oceans, a Global Navigation Satellite System (GNSS) receiver was installed on the research vessel (RV) Marion Dufresne in October 2020 to describe, and monitor, global moisture changes in these areas. GNSS raw data are recorded continuously and used to retrieve integrated water vapor contents (IWV) along the RV route.</p><p>After a data quality check that confirmed that a wise choice of location of the GNSS antenna on the RV is crucial to avoid mask, signal reflection and interference from other instruments that may degrade IWV retrieval, a first assessment of the GNSS analysis performances was carried out by comparing the vertical component of the estimated positions to sea surface height model. The differences are on the order of 20 to 30 cm; they are consistent with both the error budget for sea surface height determination using GNSS and the sea surface height model formal errors.</p><p>An evaluation of GNSS-derived IWV was conducted using IWV estimates from the ECMWF fifth ReAnalysis (ERA5) and ground-based GNSS reference stations located nearby the tracks of RV Marion Dufresne. Preliminary analyses show encouraging results with a mean root mean square error of ~2-3 kg m<sup>-</sup><sup>2</sup> between ERA5 and GNSS-derived IWV. The use of ultra-rapid GNSS orbit and clock product was also investigated to assess the performance of near real-time GNSS-derived IWV estimation for numerical weather prediction purposes.</p>

scholarly journals Calibration of an Airborne Interferometric Radar Altimeter over the Qingdao Coast Sea, China

2020 ◽  
Vol 12 (10) ◽  
pp. 1651
Author(s):  
Lei Yang ◽  
Yongsheng Xu ◽  
Xinghua Zhou ◽  
Lin Zhu ◽  
Qiufu Jiang ◽  
...  
Keyword(s):  
Simulated Data ◽  
Satellite System ◽  
Sea Surface ◽  
Two Dimensional ◽  
Radar Altimeter ◽  
New Strategy ◽  
Time Domains ◽  
Dimensional Measurements ◽  
Global Navigation Satellite ◽  
Wave Induced

Calibration/Validation (Cal/Val) of satellite altimeters is fundamental for monitoring onboard sensor performance and ensuring long-term data quality. As altimeter technology has been evolving rapidly from profile to wide swath and interferometric altimetry, different requirements regarding Cal/Val have emerged. Most current Cal/Val technology has been developed for conventional profile altimeters, whereby satellite observations are compared against measurements at one point along orbit lines. However, the application of this type of Cal/Val technique to swath interferometric altimeters with two-dimensional measurements is difficult. Here, we propose a new strategy for the evaluation of interferometric altimeters based on comparison of wave-induced sea surface elevation (WSSE) spectra from one- and two-dimensional measurements. This method assumes that the WSSE variance of an equilibrium wave field is uniform and can be measured equivalently in the space or time domains. The method was first tested with simulated data and then used to evaluate the performance of an airborne interferometric radar altimeter system (AIRAS) using Global Navigation Satellite System (GNSS) buoy measurements. The differences between the WSSE variances from the AIRAS and two GNSS buoys were below 8 cm2, corresponding to a standard deviation of 2.8 cm, which could serve as a reference for the WSSE error over the scale range of waves. The correlation coefficient between the AIRAS and GNSS buoys was approximately 0.90, indicating that the error was small relative to the WSSE signals. In addition, the sea surface height (SSH) difference measured by the AIRAS was compared with that derived from the GNSS buoys at two sites. The results indicated that the error of the SSH difference was 3 cm. This approach represents a possible technique for the Cal/Val of future spaceborne/airborne interferometric altimeters; however, additional experiments and applications are needed to verify the feasibility of this method.

Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area

2017 ◽  
Vol 862 ◽  
pp. 90-95 ◽  
Author(s):  
Agung Budi Cahyono ◽  
Dian Saptarini ◽  
Cherie Bhekti Pribadi ◽  
Haryo Dwito Armono
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Coastal Development ◽  
Sea Surface ◽  
Distribution Area ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology

The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.

scholarly journals GNSS-R Delay-Doppler Map Simulation Based on the 2004 Sumatra-Andaman Tsunami Event

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Qingyun Yan ◽  
Weimin Huang
Keyword(s):  
Region Of Interest ◽  
Final Analysis ◽  
Satellite System ◽  
Sea Surface ◽  
Scattering Coefficient ◽  
Scattering Coefficients ◽  
Coefficient Distribution ◽  
Simulation Based ◽  
Global Navigation Satellite ◽  
Fixed Region

A new method for simulating Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs) of a tsunami-dominant sea surface is presented. In this method, the bistatic scattering Z-V model, the sea surface mean square slope model of Cox and Munk, and the tsunami-induced wind perturbation model are employed. The feasibility of the Cox and Munk model under a tsunami scenario is examined by comparing the Cox and Munk model based scattering coefficient with the Jason-1 measurement. A good consistency between these two results is obtained with a correlation coefficient of 0.93. After confirming the applicability of the Cox and Munk model for a tsunami-dominated sea, this study provides the simulations of the scattering coefficient distribution and the corresponding DDMs of a fixed region of interest before and during the tsunami. In the final analysis, by subtracting the simulation results that are free of tsunami from those with presence of tsunami, the tsunami-induced variations in scattering coefficients and DDMs can be clearly observed. As a result, the tsunami passage can be readily interpreted.

scholarly journals Prakiraan Daerah Penangkapan Ikan Di Selat Bali Berdasarkan Data Citra Satelit [Fishly Ground Area Estimation In The Bali Strait Satellite Image Database Based]

2019 ◽  
Vol 4 (1) ◽  
pp. 87
Author(s):  
Iqbal Ghazali, Abdul Manan
Keyword(s):  
Remote Sensing ◽  
Marine Ecosystem ◽  
Satellite Image ◽  
Sea Surface ◽  
Fishing Ground ◽  
Fishing Activity ◽  
Area Estimation ◽  
Remote Sensing Technology ◽  
Sensing Technology

Abstract Indonesia has a lot of potential marine ecosystem and fisheries, this condition make some many Indonesian get a occupation to be fisherman. However, that is make a problem during his fishing activity, so we have to make some sophisticated technology to support that is activity. At the time, remote sensing technology is the answer for they problem, it is because of that is technology fisherman can be improvement they catcher with more efficient. Determination of fishly ground area by remote sensing technology has some stage before arranging layout Fishly Ground Area Estimation (FGAE) map. Procedure to arranging are respectively data searching, data processing, and data analysis, and the last stage is composing of layout of FGAE map. The main purpose of this study is to know about catching area at Bali straits based on image satellite with creating layout of FGAE map. This study doing descriptive method. At the PDPI making process, the chlorophyl-a data and sea surface temperature getting important parameter, which temperature is the main to parameter to understand of front area and upwelling which is have a lot of nutrient composition. In the other hand, chlorophyl-a is also parameter to know of prosperity area. The data of current, wind speed, wave, and sea surface level are important component as supporting data that will be help to fisherman on determination of fishing ground area at helping fisherman in catching activity.

Sign in / Sign up

Export Citation Format

Share Document