The focus of the study is optimizing the technique for estimating geocenter motion and variations in J₂ by combining data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with output from an Ocean Bottom Pressure model and a Glacial Isostatic Adjustment (GIA) model. First, we conduct an end‐to‐end numerical simulation study. We generate input time‐variable gravity field observations by perturbing a synthetic Earth model with realistically simulated errors. We show that it is important to avoid large errors at short wavelengths and signal leakage from land to ocean, as well as to account for self‐attraction and loading effects. Second, the optimal implementation strategy is applied to real GRACE data. We show that the estimates of annual amplitude in geocenter motion are in line with estimates from other techniques, such as satellite laser ranging (SLR) and global GPS inversion. At the same time, annual amplitudes of C₁₀ and C₁₁ are increased by about 50% and 20%, respectively, compared to estimates based on Swenson et al. (2008). Estimates of J₂ variations are by about 15% larger than SLR results in terms of annual amplitude. Linear trend estimates are dependent on the adopted GIA model but still comparable to some SLR results.

Accurate traffic forecasting is one of the key applications within Internet of Things (IoT)-based Intelligent Transportation Systems (ITS), playing a vital role in enhancing traffic quality, optimizing public transportation, and planning infrastructure. However, existing spatial-temporal methods encounter two primary limitations: (1) they have difficulty differentiating samples over time and often ignore dependencies among road network nodes at different time scales; (2) they are limited in capturing dynamic spatial correlations with predefined and adaptive graphs. To overcome these limitations, we introduce a novel Temporal Identity Interaction Dynamic Graph Convolutional Network (TIIDGCN) for traffic forecasting. The central concept involves assigning temporal identity features to raw data and extracting distinctive, representative spatial-temporal features through multiscale interactive learning. Specifically, we design a multiscale interactive model incorporating both spatial and temporal components. This network aims to explore spatial-temporal patterns at various scales from macro to micro, facilitating their mutual enhancement through positive feedback mechanisms. For the spatial component, we design a new dynamic graph learning method to depict the changing dependencies among nodes. We conduct comprehensive experiments using four real-world traffic datasets (PeMS04/07/08 and NYCTaxi Drop-off/Pick-up). Specifically, TIIDGCN achieves a 16.46% reduction in Mean Absolute Error compared to the Spatial-Temporal Graph Attention Gated Recurrent Transformer Network model on the PeMS08 dataset.

Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R² of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research.

Uncertainty in the geocenter position and its subsequent motion affects positioning estimates on the surface of the Earth and downstream products such as site velocities, particularly the vertical component. The current version of the International Terrestrial Reference Frame, ITRF2014, derives its origin as the long‐term averaged center of mass as sensed by satellite laser ranging (SLR), and by definition, it adopts only linear motion of the origin with uncertainty determined using a white noise process. We compare weekly SLR translations relative to the ITRF2014 origin, with network translations estimated from station displacements from surface mass transport models. We find that the proportion of variance explained in SLR translations by the model‐derived translations is on average less than 10%. Time‐correlated noise and nonlinear rates, particularly evident in the Y and Z components of the SLR translations with respect to the ITRF2014 origin, are not fully replicated by the model‐derived translations. This suggests that translation‐related uncertainties are underestimated when a white noise model is adopted and that substantial systematic errors remain in the data defining the ITRF origin. When using a white noise model, we find uncertainties in the rate of SLR X, Y, and Z translations of ±0.03, ±0.03, and ±0.06, respectively, increasing to ±0.13, ±0.17, and ±0.33 (mm/yr, 1 sigma) when a power law and white noise model is adopted.

In this study, we develop a methodology to estimate monthly variations in degree-1 and C₂₀ coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data with oceanic mass anomalies (combination approach). With respect to the method by Swenson et al., the proposed approach exploits noise covariance information of both input data sets and thus produces stochastically optimal solutions supplied with realistic error information. Numerical simulations show that the quality of degree-1 and -2 coefficients may be increased in this way by about 30 per cent in terms of RMS error. We also proved that the proposed approach can be reduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise in oceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C₂₀ coefficients by estimating mass anomaly time-series within carefully selected validation areas, where mass transport is small. Our validation shows that, compared to selected Satellite Laser Ranging (SLR) and joint inversion degree-1 solutions, the proposed combination approach better complements GRACE solutions. The annual amplitude of the SLR-based C₁₀ is probably overestimated by about 1 mm. The performance of the C₂₀ coefficients, on the other hand, is similar to that of traditionally used solution from the SLR technique.

Based on the nonlinear relationship between multi-source marine geodetic data and seafloor topography, the multilayer perceptron (MLP) neural network is introduced into bathymetry prediction to improve the accuracy of bathymetry model. This method not only integrates multi-source marine geodetic data, but also takes into consideration the nonlinear relationships between these data and seafloor topography. Firstly, we utilize terrain information and the multi-source marine geodetic data (vertical deflection, gravity anomaly, vertical gravity gradient, mean dynamic topography) around the shipborne sounding control points within a 6’×6’ grid as input data, while using the actual bathymetry at control points as output data to train the MLP neural network model. Subsequently, inputting the input data from the central point of a 1’×1’ grid within the study area into the MLP model to predict the bathymetry at the grid’s center. Then, based on the predicted bathymetry, a bathymetry model is established of this research area. Utilizing this methodology, this paper establishes the Gulf of Mexico Bathymetric Chart of the Oceans (MBCO1) model. Due to the influence of complex seafloor topography and the distribution of shipborne bathymetry points, there are differences in training and prediction among different regions. To address this, this study divides the research area into five sub-regions (A, B, C, D, and E) and establishes bathymetry model (MBCO2 models) through each sub-region. Finally, we evaluated the accuracy and effectiveness of this method by comparing it with existing bathymetry models, as well as shipboard depths.