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For multitemporal synthetic aperture radar (SAR) images, the change detection methods based on nonlocal theory can well suppress the adverse effects of coherent speckle noise over the change detection results. However, effectively retaining the edge information of the changed area is still a challenging task. To overcome this problem, this study proposes a change detection method based on progressive nonlocal theory. First, the progressive nonlocal theory is used to extract the spatial–temporal nonlocal information from multitemporal SAR images. Compared with the traditional nonlocal theory, the progressive nonlocal theory proposed in this study has three distinctive characteristics: 1) the progressive nonlocal neighborhood from the matching window to the search window; 2) the progressive optimization of matching window weight from the isotropic Gaussian distribution to the irregular distribution; and 3) the progressive increase of noise level from the 2 sigma principle to the 4/3 sigma principle (the noise level corresponding to the 4/3 sigma principle is 1.5 times the noise level corresponding to the 2 sigma principle). The difference image is then obtained by using the spatial–temporal nonlocal information and the ratio operator. Finally, the change map is obtained by applying a threshold segmentation method to the difference image. Two data sets were used for the testing, and it was shown that compared with other advanced methods, the method proposed in this study can better retain the edge information of the changed area and improve the Kappa coefficient and F1 score of the change map. [ABSTRACT FROM AUTHOR]

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment. [ABSTRACT FROM AUTHOR]

In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events. [ABSTRACT FROM AUTHOR]

Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial–temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results. [ABSTRACT FROM AUTHOR]

Non-line-of-sight (NLOS) propagation of radio signals can significantly degrade the performance of ultra-wideband localization systems indoors, it is hence crucial to mitigate the NLOS effect to enhance the accuracy of positioning. The existing NLOS mitigation algorithms to improve localization accuracy are either by compensating range errors through NLOS identification and mitigation methods for ranging or by using dedicated localization techniques. However, they are only applicable to some specific scenarios due to some special assumptions or the need of a priori knowledge, such as thresholds and distribution functions. Another disadvantage is that they neither have the capability to evaluate the magnitude of NLOS effect nor take account of the residual NLOS range errors during location estimation. To remedy these problems, this paper proposes a less environment-dependent and a priori knowledge-independent NLOS identification and mitigation method for ranging which is able to determine the specific NLOS channel. Based on the identified channel information, a rule is developed to select appropriate NLOS ranges for location estimation. Meanwhile, an equality constrained Taylor series robust least squares (ECTSRLS) technique is proposed to suppress residual NLOS range errors by introducing robustness to Taylor series least squares method. All these constitute our FCE-ECTSRLS NLOS mitigation algorithm. The performance of the proposed algorithm is compared with four existing NLOS mitigation algorithms by both static and mobile localization experiments in a harsh indoor environment. Experimental results have demonstrated that the proposed FCE-ECTSRLS algorithm outperforms the other four algorithms significantly. [ABSTRACT FROM AUTHOR]

Rapid developments in satellite positioning, navigation, and timing have revolutionized surveying and mapping practice and significantly influenced the way people live and society operates. The advent of new generation global navigation satellite systems (GNSS) has heralded an exciting future for not only the GNSS community, but also many other areas that are critical to our society at large. With the rapid advances in space-based technologies and new dedicated space missions, the availability of large scale and dense contemporary GNSS networks such as regional continuously operating reference station (CORS) networks and the developments of new algorithms and methodologies, the ability of using space geodetic techniques to remotely sense the atmosphere (i.e., the troposphere and ionosphere) has dramatically improved. Real time GNSS-derived atmospheric variables with a high spatio-temporal resolution have become an important new source of measurements for meteorology, particularly for extreme weather events since water vapour (WV), as the most abundant element of greenhouse gas and accounting for $\sim $70% of global warming, is under-sampled in current meteorological and climate observing systems. This study investigates the emerging area of GNSS technology for near real-time monitoring and forecasting of severe weather and climate change research. This includes both ground-based global positioning system (GPS)-derived precipitable water vapour (PWV) estimation and four-dimensional (4-D) tomographic modeling for wet refractivity fields. Two severe weather case studies were used to investigate the signature of GPS-derived PWV and wet refractivity derived from the 4-D GPS tomographic model under the influence of severe mesoscale convective systems (MCSs). GPS observations from the Victorian state-wide CORS network, i.e., GPSnet, in Australia were used. Results showed strong spatial and temporal correlations between the variations in the ground-based GPS-derived PWV and the passage of the severe MCS. This indicates that the GPS method can complement conventional meteorological observations for the studying, monitoring, and potentially predicting of severe weather events. The advantage of using the ground-based GPS technique is that it can provide continuous observations for the storm passage with high temporal and spatial resolution. Results from these two case studies also suggest that GPS-derived PWV can resolve the synoptic signature of the dynamics and offer precursors to severe weather, and the tomographic technique has the potential to depict the three-dimensional (3-D) signature of wet refractivity for the convective and stratiform processes evident in MCS events. This research reveals the potential of using GNSS-derived PWV to strengthen numerical weather prediction (NWP) models and forecasts, and the potential for GNSS-derived PWV and wet refractivity fields to enhance early detection and sensing of severe weather. [ABSTRACT FROM PUBLISHER]

A fundamental principle of local interaction of radio waves with a refractive spherical medium is formulated and illustrated using the radio occultation (RO) method of remote sensing of the atmosphere and the ionosphere of the Earth and the planets. In accordance with this principle, the main contribution to variations of the amplitude and the phase of radio waves propagating through a medium makes a neighborhood of a tangential point, where the gradient of the refractive index is perpendicular to the radio wave trajectory. A necessary and sufficient condition (a criterion) is established to detect the displacement of the tangential point from the radio ray perigee using analysis of the RO experimental data. This criterion is applied to the identification and the location of layers in the atmosphere and the ionosphere by the use of Global Positioning System RO data. RO data from the CHAllenge Minisatellite Payload (CHAMP) are used to validate the criterion introduced when significant variations of the amplitude and the phase of the RO signals are observed at the RO ray perigee altitudes below 80 km. The detected criterion opens a new avenue in terms of measuring the altitude and the slope of the atmospheric and ionospheric layers. This is important for the location determination of the wind shear and the direction of internal wave propagation in the lower ionosphere and possibly in the atmosphere. The new criterion provides an improved estimation of the altitude and the location of the ionospheric plasma layers compared with the backpropagation radio-holographic method previously used. [ABSTRACT FROM AUTHOR]

The new pseudo three-dimensional (3-D) segment method analytical ray tracing technique (3-D SMART) is similar to the 2-D SMART technique however with the added ability to determine the effects of transverse refractive gradients on a ray path. In the past numerical ray tracing techniques using a form of Haselgrove's equations were required for realistic 3-D ray tracing. This new 3-D SMART technique is considerably less computer resource intensive than numerical ray tracing techniques and thus is of particular importance for near real-time applications and the general case where it is necessary to trace a vast number of ray paths. [ABSTRACT FROM PUBLISHER]

The ionosphere and atmosphere may have significant impacts on the high-stable navigational signals of the Global Positioning System (GPS) in the communication link satellite to satellite. The classification of the different types of the ionospheric impact on the phase and amplitude of the GPS signals at altitudes of 40–90 km is introduced using the CHAllenging Minisatellite Payload (CHAMP) radio occultation (RO) data. An analytical model is elaborated for the description of the radio wave propagation in the stratified ionosphere and atmosphere. The propagation medium consists of sectors having the spherically symmetric distributions of refractivity. The newly developed model presents analytical expressions for the phase path and refractive attenuation of radio waves. The model explains significant amplitude and phase variations at altitudes of 40–90 km of the RO ray perigee associated with the influence of the inclined ionospheric layers. An innovative eikonal acceleration technique is described and applied to the identification and location of the inclined ionospheric layers using the comparative analysis of the amplitude and phase variations of the RO signals. [ABSTRACT FROM PUBLISHER]

A method, which can invert the ionospheric electron content above the CHAMP satellite, is presented. The daytime behavior of the equatorial ionospheric anomaly (EIA) is monitored using the CHAMP global positioning system data and the ionosphere map exchange format data supplied by the Center for Orbit Determination in Europe during the period at quiet geomagnetic conditions between November 7 and December 12, 2003. Results show that the plasma congregates and rises above the geomagnetic equator due to the daytime upward vertical E \times B drift during the early variations of EIA. After being lifted to higher altitudes, the topside ionospheric equatorial plasma above the CHAMP orbit (over 400 km above the ground) controlled mainly by the electromagnetic field starts to diffuse downward to higher latitudes along magnetic field lines, and the double-humped structure begins to appear in the topside ionosphere above the CHAMP orbit. However, a single crest is still observed for the entire ionosphere above the ground, and this single peak moves slightly to the winter hemisphere as a result of the transequatorial neutral wind. With the continuous diffusion of the plasma, the two crests also occur for the entire ionosphere at both sides of the geomagnetic equator. [ABSTRACT FROM AUTHOR]