ALBERT

Description: Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.

Description: The effect of tropical precipitation on the inverse change in the entrance and exit of the tropical easterly jet (TEJ) is studied. The TEJ's entrance and exit are over the northwest Pacific Ocean and equatorial Africa, inversely varying from July to September. When the entrance of the TEJ is weaker-than-normal, the exit is stronger-than-normal, and vice versa. Observed data show that a west-east dipole precipitation pattern located in the Maritime Continent (MC) and tropical Pacific (TP) has a distinct influence on such inverse variation of the TEJ. A series of numerical experiments are carried out based on the linear baroclinic model, qualitatively exploring the independent effects of MC and TP precipitation anomalies. The results show that the heat source over the MC can induce a Kelvin wave propagating eastward in the upper troposphere, resulting in the weakening of the TEJ entrance. The heat sink over the TP generates a Rossby wave response, forming a pair of cyclones in the troposphere over the equatorial western Pacific, and the westerly anomaly in the middle weakens the TEJ's entrance; meanwhile, a Kelvin wave propagating eastward in the upper troposphere between 120°W and 70°E, which is caused by the heat sink over the TP strengthens the exit of the TEJ. There is a reverse phase change in the inlet and exit regions of the jet stream as a result. It’s shows that the west-east dipole precipitation pattern in the MC and TP may be related to La Niña-like SST anomalies in synchronous summer.

Description: Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries.

Description: Connecting the plateau with cratons in east China, the eastern margin of the Tibetan Plateau is an area with the youngest uplift, strongest deformation, and frequent occurrence of large earthquakes. This region where the topographic characteristics change significantly from south to north becomes an ideal experimental site for investigating the continental geodynamics and seismogenic environment. Most seismological researchers use seismic velocity to constrain the rheological strength and crustal flow distribution in the lithosphere, upon which the deformation can be explored for the crust and upper mantle. However, seismic velocity is related to both rock strength and composition. Therefore, the multiplicity of geodynamic interpretation is unavoidable. However, seismic attenuation is a direct observation of deep temperature and rheological strength. Therefore, we construct a broadband high-resolution layered attenuation model for the lithosphere in the eastern margin of the Tibetan Plateau by using the Pg, Lg, Pn, and Sn waves propagating in the crust and uppermost mantle. The attenuation model provides a fine structure with 3D rheological strength and thermal distribution in the lithosphere. We investigate the dynamic origins of the distribution of soft ductile materials and upon which we exploit the tectonic evolution and seismogenic environment under the lithospheric compression and collision in the eastern margin of the Tibetan Plateau. This research was supported by the National Natural Science Foundation of China (U2139206, 41974061, and 41974054).

Description: The wet tropospheric correction (WTC) retrieved from the onboard calibration microwave radiometer (CMR) of Haiyang-2A (HY-2A) is critical in monitoring the global sea level. However, the CMR WTC became significantly biased from June 2017 due to the failure of the 18.7-GHz band, which caused massive errors in the sea surface height (SSH) measurements. We investigate the accuracy of the CMR WTC derived from the two remaining bands to address this problem. A comprehensive evaluation using multisource data demonstrates that the dual-band + backscattering coefficient (BC) algorithm achieves comparable accuracy to the three-band algorithm, and it does not suffer from any large errors when the equipment works well. Hence, we calibrated the HY-2A CMR data with the dual-band + BC algorithm when the 18.7-GHz band failed, and the accuracy of the CMR WTC is improved from 2.34 to 1.39 cm compared with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 data. In addition, the SSH measurements are improved significantly by a maximum of 2 cm in mean value using the dual-band + BC WTC during the failure period of HY-2A CMR. Compared with Jason-3 SSH measurements, the HY-2A with dual-band + BC shows a slightly larger difference than HY-2A with three-band by 0.1 cm in rms. This method prolongs the operational lifetime of the HY-2A CMR and could be used in the reprocessing of HY-2A observations.

Description: The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method.

Description: As a consumed and influential natural plant beverage, tea is widely planted in subtropical and tropical areas all over the world. Affected by (sub) tropical climate characteristics, the underlying surface of the tea distribution area is extremely complex, with a variety of vegetation types. In addition, tea distribution is scattered and fragmentized in most of China. Therefore, it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods. This study proposed a boundary-enhanced, object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data. This method uses multispectral indexes, textures, vegetable indices, and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations. To reduce feature redundancy and computation time, the feature elimination algorithm based on Mean Decrease Accuracy (MDA) was used to generate the optimal feature set. Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types, high resolution GF-2 image was segmented based on the MultiResolution Segmentation (MRS) algorithm to assist the segmentation of Sentinel-2, which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations. Finally, the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain, Yunnan Province. The resulting tea plantation map had high accuracy, with a 95.38% overall accuracy and 0.91 kappa coefficient. We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.

Description: This article analyzes the interferometric measurements of ground-based global navigation satellite systems (GNSSs) stations and proposes a novel method for sea surface states detection. The novel technique benefits from a cost-effective data collection from a large number of global GNSS stations. In this study, we extend a traditional GNSS interferometry reflectometry (GNSS-IR) model so that it can be applied to a multilayer surface by considering the surface roughness, total reflectivity, and penetration loss in multilayer situations. Based on this model, the wavelet analysis is used to perform parameterization on the interferometric observations represented by the signal to noise ratio (SNR). An integration factor and power curve are also proposed to characterize the surface state transition. One-year data from an Arctic geodetic GNSS station in the north of Canada are collected for analysis to validate the proposed approach in comparison with the existing methods based on the amplitude and damping factors. The results show that the new method demonstrates good usability and sensitivity to detect surface state transitions, e.g., icing, snowfall, and snow melting. However, the amplitude and damping factor-based methods derived from the single-layer model are only able to detect the pure ice surface and cannot respond to thick snow conditions. Finally, the high-resolution spaceborne images confirm the reliability of this method, exhibiting a great potential for long-term coastal sea surface detection based on the global geodetic GNSS stations and later being expected to be applied to sense cryosphere surface states.