Reanalysis products have been applied to calculate the tropospheric delay for Global Navigation Satellite System (GNSS) positioning purposes widely. It is necessary to obtain high-precision ...tropospheric delay information from GNSS users with a high-precision tropospheric vertical stratification model because the height of the grids of the atmospheric reanalysis data is inconsistent with that of GNSS users, especially in regions with high terrains. In addition, the variation of the tropospheric delay in the vertical direction is much higher than that in the horizontal direction. The zenith total delay (ZTD) vertical stratification model is also key to the development of real-time and high-precision ZTD models. A new approach, the sliding window algorithm, is proposed to develop a ZTD vertical stratification model. In this work, a ZTD vertical stratification model considering spatiotemporal factors is developed based on the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data, which is named the GZTD-H model. Radiosonde and International GNSS Service (IGS) data are treated as reference values to evaluate the performance of the GZTD-H model, which is compared to the model GPT2w. The results show that the GZTD-H model realizes the highest performance in ZTD layered vertical interpolation against ZTD layered profiles obtained at radiosonde sites, which achieves an improvement of 10% over the model GPT2w. Compared to model GPT2w, the GZTD-H model attains a spatial interpolation improvement of 8% for the Global Geodetic Observing System (GGOS) Atmosphere gridded ZTD over the surface ZTD calculated from radiosonde profiles. Furthermore, compared to model GPT2w, the model GZTD-H also attains improvements of 11% over the precise ZTD products acquired at IGS sites. In terms of model parameters, the GZTD-H model is greatly reduced and optimized over model GPT2w. Hence, the applicability of this model is enhanced in terms of GNSS atmospheric sounding and precise GNSS positioning.
In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global ...Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.
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.
Abstract
Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in ...mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.
•Huixian Karst National Wetland is currently the most representative karst wetland in China.•Optimized RF outperforms fused SegNet classifier in coarse classification of vegetation.•RF-based ...classifications have significant differences from SegNet classifier using UAV images.•RF-DT algorithm achieved over 0.878 overall accuracy of karst vegetation communities.•Statistical analysis of variables’ importance for classifying karst vegetation communities.
Karst wetlands have the characteristics of small scale and poor stability. At present, the wetland is being severely damaged and its area is seriously degraded, and the accurate identification of vegetation communities is very important for the rapid assessment and management of karst wetland. In this paper, Huixian Karst National Wetland Park, located in Guilin city, China, was taken as the study area, the digital orthophoto map (DOM) and digital surface model (DSM) of UAV images were selected as the data sources, and the vegetation communities of karst wetland were classified by using the object-based Random Forest (RF)-Decision Tree (DT) algorithm and SegNet algorithm. When the object-based RF algorithm and SegNet algorithm were used for coarse classification of karst vegetation, the parameters (mtry, ntree) of the object-based RF algorithm were optimized, and the data dimensionality reduction and RFE variable selection algorithm were used for selecting feature, and the single-class SegNet model was integrated based on the soft voting method to improve the applicability of vegetation classification in karst wetlands. In the classification of vegetation communities in karst wetlands, the optimized object-based RF-DT algorithm were used to extract the vegetation communities in the Areas A, B, and C. The statistical analysis of the importance of the feature variables (spectral features, texture features, geometric features, and position features) of various types of land cover in the three areas was carried out to explore the optimal classification variables of various types of vegetation. The results showed that: (1) the optimized object-based RF algorithm performed better than the SegNet algorithm in classifying karst vegetation at 95% confidence level during the coarse classification. The average accuracy of wetland vegetation was improved by 1.06–13.58%; (2) the object-based RF-DT algorithm had high classification ability for the karst wetland vegetation community, with overall accuracy and kappa coefficient above 0.85; and that (3) although geometric features accounted for the largest proportion (52.2%) in the classification of bermudagrass, water hyacinth, lotus, linden and other vegetation, texture features accounted for the highest proportion of 56.3% in the classification of vegetation whose importance was more than 90.
•Polarimetric SAR images improve marsh vegetation classification accuracy.•Multi-temporal and multi-source classifications outperform single-temporal classification.•The RF model synergistically ...using optical and SAR data has the strongest applicability.•Data source and imaging time affect herbaceous vegetation discrimination.•Cross-polarization SAR data provided important contributions for wetland mapping.
The accurate classification of marsh vegetation is an important prerequisite for wetland management and protection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination variable selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Additionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantitatively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important variables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetric decomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenvalues of the matrix (Mean_lambda) are the backscattering coefficient features and polarimetric decomposition parameters with the highest contributions. (4) The modified normalized difference water index in June (MNDWI_ Jun), blue band in September (Mean_B_Sep), location feature pixel coordinates (Y_Max_Pxl), and ratio vegetation index in September (RVI_Sep) have the highest contribution to the identification and classification of deep-water marsh vegetation, shallow-water marsh vegetation, forest, and shrubs, respectively. (5) The identification of forest is the strongest, and the classification accuracy for shrubs and deep-water marsh vegetation is greatly affected by the combination of time phase and data sources.
In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully ...considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south.
Land subsidence is a common geological hazard. Rapid urban expansion has led to different degrees of ground subsidence within Wuhan in the past few years. The novel coronavirus outbreak in 2020 has ...seriously impacted urban construction and people’s lives in Wuhan. Land subsidence in Wuhan has changed greatly with the resumption of work and production. We used 80 Sentinel-1A Synthetic Aperture Radar (SAR) images covering Wuhan to obtain the land subsidence change information of Wuhan from July 2017 to September 2021 by using the small baseline subset interferometric SAR technique. Results show that the subsidence in Wuhan is uneven and concentrated in a few areas, and the maximum subsidence rate reached 57 mm/yr during the study period. Compared with land deformation before 2017, the land subsidence in Wuhan is more obvious after 2020. The most severe area of subsidence is located near Qingling in Hongshan District, with a maximum accumulated subsidence of 90 mm, and obvious subsidence funnels are observed in Qiaokou, Jiangan, Wuchang and Qingshan Districts. The location of subsidence centers in Wuhan is associated with building intensity, and most of the subsidence funnels are formed in connection with urban subway construction and building construction. Carbonate belt and soft ground cover areas are more likely to lead to karst collapse and land subsidence phenomena. Seasonal changes are observed in the land subsidence in Wuhan. A large amount of rainfall can replenish groundwater resources and reduce the rate of land subsidence. The change in water level in the Yangtze River has a certain impact on the land subsidence along the rivers in Wuhan, but the overall impact is small. An obvious uplift is observed in Caidian District in the south of Wuhan, and the reason may be related to the physical and chemical expansion effects of the expansive clay.
Tropospheric delay is one of the main errors affecting high-precision positioning and navigation and is a key parameter of water vapor detection in the Global Navigation Satellite System (GNSS). The ...second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is the latest generation of reanalysis data collected by the National Aeronautics and Space Administration (NASA), which can be used to calculate tropospheric delay products with high spatial and temporal resolution. However, there is no report analyzing the accuracy of the zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) calculated from MERRA-2 data. This paper evaluates the performance of the ZTD and ZWD values derived from global MERRA-2 data using global radiosonde data and International GNSS Service (IGS) precise ZTD products. The results are as follows: (1) Taking the precision ZTD products of 316 IGS stations from around the world from 2015 to 2017 as the reference, the average root mean square (RMS) of the ZTD values calculated from the MERRA-2 data is better than 1.35 cm, and the accuracy difference between different years is small. The bias and RMS of the ZTD values show certain seasonal variations, with a higher accuracy in winter and a lower accuracy in summer, and the RMS decreases from the equator to the poles. However, those of the ZTD values do not show obvious variations according to elevation. (2) Relative to the radiosonde data, the RMS of the ZWD and ZTD values calculated from the MERRA-2 data are better than 1.37 cm and 1.45 cm, respectively. Furthermore, the bias and RMS of the ZWD and ZTD values also show some temporal and spatial characteristics, which are similar to the test results of the IGS stations. It is suggested that MERRA-2 data can be used for global tropospheric vertical profile model construction because of their high accuracy and good stability in the global calculation of the ZWD and ZTD.
Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision ...and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (
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) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new
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m
model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.
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