Tropospheric delay is an important factor affecting high precision Global Navigation Satellite System (GNSS) positioning and also the basic data for GNSS atmospheric research. However, the existing tropospheric delay models have some problems, such as only a single function used for the entire atmosphere. In this paper, an ERA5-based (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) global model for vertical adjustment of Zenith Tropospheric Delay (ZTD) using a piecewise function is developed. The ZTD data at 611 radiosonde stations and the MERRA-2 (second Modern-Era Retrospective analysis for Research and Applications) atmospheric reanalysis data were used to validate the model reliability. The Global Zenith Tropospheric Delay Piecewise (GZTD-P) model has excellent performance compared with the Global Pressure and Temperature (GPT3) model. Validated at radiosonde stations, the performance of the GZTD-P model was improved by 0.96 cm (23%) relative to the GPT3 model. Validated with MERRA-2 data, the quality of the GZTD-P model is improved by 1.8 cm (50%) compared to the GPT3 model, showing better accuracy and stability. The ZTD vertical adjustment model with different resolutions was established to enrich the model's applicability and speed up the process of tropospheric delay calculation. By providing model parameters with different resolutions, users can choose the appropriate model according to their applications.

Snow is not only a critical storage component in the hydrologic cycle but also an important data for climate research; however, snowfall observations are only sparsely available. Signal-to-noise ratio (SNR) has recently been applied for sensing snow depths. Most studies only consider either global positioning system (GPS) L1 or L2 SNR data. In the current study, a new snow depth estimation approach is proposed using multipath reflectometry and SNR combination of GPS triple frequency (i.e. L1, L2 and L5) signals. The SNR combination method describes the relationship between antenna height variation and spectral peak frequency. Snow depths are retrieved from the SNR combination data at YEL2 and KIRU sites and validated by comparing it with in situ observations. The elevation angle ranges from 5° to 25°. The correlations for the two sites are 0.99 and 0.97. The performance of the new approach is assessed by comparing it with existing models. The proposed approach presents a high correlation of 0.95 and an accuracy (in terms of Root Mean Square Error) improvement of over 30%. Findings indicate that the new approach could potentially be applied to monitor snow depths and may serve as a reference for building multi-system and multi-frequency global navigation satellite system reflectometry models.

Although the Klobuchar model is widely used in single-frequency GPS receivers, it cannot effectively correct the ionospheric delay. The Klobuchar model sets the night ionospheric delay as a constant, i.e., it cannot reflect temporal changes at night. The observation data of seventeen International Global Navigation Satellite System Service (IGS) stations within and around China from 2011 provided by the IGS center are used in this study to calculate the Total Electron Content (TEC) values using the Klobuchar model and the dual-frequency model. The Holt–Winters exponential smoothing model is used to forecast the error of the 7th day between the Klobuchar model and the dual-frequency model by using the error of the former six days. The forecast results are used to develop the sophisticated Klobuchar model when no epochs are missing, considering that certain reasons may result in some of the observation data being missing and weaken the relationship between each epoch in practical applications. We study the applicability of the sophisticated Klobuchar model when observation data are missing. This study deletes observation data of some epochs randomly and then calculates TEC values using the Klobuchar model. A cubic spline curve is used to restore the missing TEC values calculated in the Klobuchar mode. Finally, we develop the sophisticated Klobuchar model when N epochs are missing in China. The sophisticated Klobuchar model is compared with the dual-frequency model. The experimental results reveal the following: (1) the sophisticated Klobuchar model can correct the ionospheric delay more significantly than the Klobuchar model; (2) the sophisticated Klobuchar model can reflect the ionosphere temporal evolution, particularly at night, with the correct results increasing with increasing latitude; and (3) the sophisticated Klobuchar model can achieve remarkable correction results when N epochs are missing, with the correct results being nearly as good as that of the dual-frequency model when no epochs are missing.

With the development of remote sensing technology and the increasing accuracy of remote sensing images, research on the accuracy of remote sensing classification is becoming more and more important. However, the classification accuracy obtained by different classification algorithms is also different. To this end, this paper selects the maximum likelihood method and the minimum distance method in the traditional supervised classification, the ISODATA method and the k-means algorithm in the unsupervised classification, and uses these four algorithms to classify the Landsat images in the research area of Heze City. The classification results are obtained and the results are evaluated. Then the four algorithms are compared separately, and the advantages and disadvantages of each algorithm are analyzed. The results show that the classification accuracy of the maximum likelihood method in the supervised classification is relatively high, and the classification accuracy is 82.3281%. The ISODATA algorithm in the supervised classification is superior to the K-means algorithm in clustering effect.

The weighted mean temperature (Tm) is a crucial variable in mapping zenith wet delay (ZWD) from the global navigation satellite system (GNSS) to precipitable water vapor (PWV). Existing empirical models for Tm estimation mainly utilize a single data source, either radiosonde (RS) data or reanalysis data. And these models assume that the Tm follows a predefined linear periodic function, failing to capture the detailed nonlinear Tm variations, and yielding low accuracy. This study developed a Tm forecast model (GRNN-F) using the generalized regression neural network (GRNN) and fused Tm data. The performance of GRNN-F was evaluated using Tm from the RS sites not involved in modeling. The results demonstrate strong agreement between GRNN-F and RS, with a bias of 0 K, a standard deviation (STD) of 3.26 K, and a root mean square (RMS) error of 3.26 K. Compared with the traditional predefined function-based model (GPT3) and the linear model (GTm), GRNN-F exhibits a 28.35% and 34.67% reduction in STD and a 30.93% and 36.58% reduction in RMS. Compared to the single data source-based models, GRNN-F demonstrates a significant advantage in Tm forecast, especially at moments with more and larger sudden Tm variations. Moreover, GRNN-F outperforms two comparative models based on random forest (RF) and backpropagation neural network (BPNN) on the same fused data across different cases. The theoretical mean PWV relative error derived from GRNN-F is only 1.15%, with less than 1% of the sites accounting for 19%, whereas other models fail to achieve this level of accuracy at any site.