This paper focuses on estimating a new high-resolution Earth's gravity field model named SGG-UGM-2 from satellite gravimetry, satellite altimetry, and Earth Gravitational Model 2008 (EGM2008)-derived gravity data based on the theory of the ellipsoidal harmonic analysis and coefficient transformation (EHA-CT). We first derive the related formulas of the EHA-CT method, which is used for computing the spherical harmonic coefficients from grid area-mean and point gravity anomalies on the ellipsoid. The derived formulas are successfully evaluated based on numerical experiments. Then, based on the derived least-squares formulas of the EHA-CT method, we develop the new model SGG-UGM-2 up to degree 2190 and order 2159 by combining the observations of the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE), the normal equation of the Gravity Recovery and Climate Experiment (GRACE), marine gravity data derived from satellite altimetry data, and EGM2008-derived continental gravity data. The coefficients of degrees 251–2159 are estimated by solving the block-diagonal form normal equations of surface gravity anomalies (including the marine gravity data). The coefficients of degrees 2–250 are determined by combining the normal equations of satellite observations and surface gravity anomalies. The variance component estimation technique is used to estimate the relative weights of different observations. Finally, global positioning system (GPS)/leveling data in the mainland of China and the United States are used to validate SGG-UGM-2 together with other models, such as European improved gravity model of the earth by new techniques (EIGEN)-6C4, GECO, EGM2008, and SGG-UGM-1 (the predecessor of SGG-UGM-2). Compared to other models, the model SGG-UGM-2 shows a promising performance in the GPS/leveling validation. All GOCE-related models have similar performances both in the mainland of China and the United States, and better performances than that of EGM2008 in the mainland of China. Due to the contribution of GRACE data and the new marine gravity anomalies, SGG-UGM-2 is slightly better than SGG-UGM-1 both in the mainland of China and the United States.
The China Geodetic Coordinate System 2000 (CGCS2000) has been used for several years as a formal published reference frame. The coordinates of all global navigation satellite system (GNSS) stations in China need to be corrected to align with the CGCS2000 frame. Different strategies can be adopted for the realization of an optimal reference frame. However, different strategies lead to different results, with differences as great as several decimeters when GNSS station coordinates are transformed into CGCS2000-defined coordinates. The two common methods for the coordinate correction of a GNSS station are quasi-stable adjustment under CGCS2000 and plate movement correction, and the differences between their results can be greater than 10 cm. In this study, a statistic method called “supervised clustering”is applied to the selection of GNSS reference stations; a new scheme named “artition spacing” for the grouping of all processed GNSS stations is proposed; and the plate movement correction method is used to correct the coordinates of all GNSS stations from the GNSS epoch to the CGCS2000 epoch. The results from the new partitioning method were found to be significantly better than those from the conventional station-blocking approach. When coordinates from the stations without grouping were used as the standard, the accuracy of all the three-dimensional coordinate components from the new partitioning method was better than 2 mm. The root mean squares (RMSs) of the velocities in the x, y, and z directions resulting from the supervised clustering method were 0.19, 0.45, and 0.32 mm∙a−1, respectively, which were much smaller than the values of 0.92, 0.72, and 0.97 mm∙a−1 that resulted from the conventional approach. In addition, singular spectrum analysis (SSA) was used to model and predict the position nonlinear movements. The modeling accuracies of SSA were better than 3, 2, and 5 mm in the east (E), north (N), and up (U) directions, respectively; and its prediction accuracies were better than 5 mm and 1 cm for the horizontal and vertical domains, respectively.
The analysis centers of the Multi-GNSS Pilot Project of the International GNSS Service provide orbit and clock products for the global navigation satellite systems (GNSSs) Global Positioning System (GPS), GLONASS, Galileo, and BeiDou, as well as for the Japanese regional Quasi-Zenith Satellite System (QZSS). Due to improved solar radiation pressure modeling and other more sophisticated models, the consistency of these products has improved in recent years. The current orbit consistency between different analysis centers is on the level of a few centimeters for GPS, around one decimeter for GLONASS and Galileo, a few decimeters for BeiDou-2, and several decimeters for QZSS. The clock consistency is about 2 cm for GPS, 5 cm for GLONASS and Galileo, and 10 cm for BeiDou-2. In terms of carrier phase modeling error for precise point positioning, the various products exhibit consistencies of 2–3 cm for GPS, 6–14 cm for GLONASS, 3–10 cm for Galileo, and 10–17 cm for BeiDou-2.
Using the FengYun-3C (FY-3C) onboard BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS) data from 2013 to 2017, this study investigates the performance and contribution of BDS to precise orbit determination (POD) for a low-Earth orbit (LEO). The overlap comparison result indicates that code bias correction of BDS can improve the POD accuracy by 12.4%. The multi-year averaged one-dimensional (1D) root mean square (RMS) of the overlapping orbit differences (OODs) for the GPS-only solution is 2.0, 1.7, and 1.5 cm, respectively, during the 2013, 2015, and 2017 periods. The 1D RMS for the BDS-only solution is 150.9, 115.0, and 47.4 cm, respectively, during the 2013, 2015, and 2017 periods, which is much worse than the GPS-only solution due to the regional system of BDS and the few BDS channels of the FY-3C receiver. For the BDS and GPS combined solution (also known as the GC combined solution), the averaged 1D RMS is 2.5, 2.3, and 1.6 cm, respectively, in 2013, 2015, and 2017, while the GC combined POD presents a significant accuracy improvement after the exclusion of geostationary Earth orbit (GEO) satellites. The main reason for the improvement seen after this exclusion is the unfavorable satellite tracking geometry and poor orbit accuracy of GEO satellites. The accuracy of BDS-only and GC combined solutions have gradually improved from 2013 to 2017, thanks to improvements in the accuracy of International GNSS Service (IGS) orbit and clock products in recent years, especially the availability of a high-frequency satellite clock product (30 s sampling interval) since 2015. Moreover, the GC POD (without GEO) was able to achieve slightly better accuracy than the GPS-only POD in 2017, indicating that the fusion of BDS and GPS observations can improve the accuracy of LEO POD. GC combined POD can significantly improve the reliability of LEO POD, simply due to system redundancy. An increased contribution of BDS to LEO POD can be expected with the launch of more BDS satellites and with further improvements in the accuracy of BDS satellite products in the near future.
Up-to-date digital elevation model (DEM) products are essential in many fields such as hazards mitigation and urban management. Airborne and low-earth-orbit (LEO) space-borne interferometric synthetic aperture radar (InSAR) has been proven to be a valuable tool for DEM generation. However, given the limitations of cost and satellite repeat cycles, it is difficult to generate or update DEMs very frequently (e.g., on a daily basis) for a very large area (e.g., continental scale or greater). Geosynchronous synthetic aperture radar (GEOSAR) satellites fly in geostationary earth orbits, allowing them to observe the same ground area with a very short revisit time (daily or shorter). This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors. In this work, we systematically analyze the quality of daily GEOSAR DEM. The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar (SAR) sensors; therefore, it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.
Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.
Conventional synthetic aperture radar (SAR) interferometry (InSAR) has been successfully used to precisely measure surface deformation in the line-of-sight (LOS) direction, while multiple-aperture SAR interferometry (MAI) has provided precise surface deformation in the along-track (AT) direction. Integration of the InSAR and MAI methods enables precise measurement of the two-dimensional (2D) deformation from an interferometric pair; recently, the integration of ascending and descending pairs has allowed the observation of precise three-dimensional (3D) deformation. Precise 3D deformation measurement has been applied to better understand geological events such as earthquakes and volcanic eruptions. The surface deformation related to the 2016 Kumamoto earthquake was large and complex near the fault line; hence, precise 3D deformation retrieval had not yet been attempted. The objectives of this study were to ① perform a feasibility test of precise 3D deformation retrieval in large and complex deformation areas through the integration of offset-based unwrapped and improved multiple-aperture SAR interferograms and ② observe the 3D deformation field related to the 2016 Kumamoto earthquake, even near the fault lines. Two ascending pairs and one descending the Advanced Land Observing Satellite-2 (ALOS-2) Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2) pair were used for the 3D deformation retrieval. Eleven in situ Global Positioning System (GPS) measurements were used to validate the 3D deformation measurement accuracy. The achieved accuracy was approximately 2.96, 3.75, and 2.86 cm in the east, north, and up directions, respectively. The results show the feasibility of precise 3D deformation measured through the integration of the improved methods, even in a case of large and complex deformation.
It is well-known that optimizing the wheel system of lunar rovers is essential. However, this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers. In this study, an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance. First, a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension, considering the complex terrain of the moon. Next, the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption. This was achieved via analysis of the wheel force during movement. Finally, the effectiveness of the proposed method was demonstrated by several simulation experiments. The newly designed wheel can protrude on demand and reduce energy consumption; it can be used as a reference for lunar rover development engineering in China.
Particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5) concentration forecasting is desirable for air pollution early warning. This study proposes an improved hybrid model, named multi-feature clustering decomposition (MCD)–echo state network (ESN)–particle swarm optimization (PSO), for multi-step PM2.5 concentration forecasting. The proposed model includes decomposition and optimized forecasting components. In the decomposition component, an MCD method consisting of rough sets attribute reduction (RSAR), k-means clustering (KC), and the empirical wavelet transform (EWT) is proposed for feature selection and data classification. Within the MCD, the RSAR algorithm is adopted to select significant air pollutant variables, which are then clustered by the KC algorithm. The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm. In the optimized forecasting component, an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation. The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor. Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model. The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.