• 中国科学引文数据库(CSCD)
  • 中文科技期刊数据库
  • 中国核心期刊(遴选)数据库
  • 日本科学技术振兴机构数据库(JST)
  • 中国学术期刊(网络版)(CNKI)
  • 中国学术期刊综合评价数据库(CAJCED)
  • 中国超星期刊域出版平台
GNSS World of China

GNSS World of China

LIU Dexiang, ZHANG Xuanzhen, LIU Chengbo, LI Xin. Regional tropospheric delay modeling considering terrain environment factors[J]. GNSS World of China, 2025, 50(1): 86-92. DOI: 10.12265/j.gnss.2024111
Citation: LIU Dexiang, ZHANG Xuanzhen, LIU Chengbo, LI Xin. Regional tropospheric delay modeling considering terrain environment factors[J]. GNSS World of China, 2025, 50(1): 86-92. DOI: 10.12265/j.gnss.2024111

Regional tropospheric delay modeling considering terrain environment factors

More Information
  • Received Date: June 16, 2024
  • Accepted Date: January 06, 2025
  • Available Online: February 26, 2025
  • Tropospheric delays act as one of the main error sources affecting the precision positioning of the GNSS. In recent years, machine learning has been widely used for modeling tropospheric delays. Based on the GNSS tropospheric delay and normalized difference vegetation index (NDVI) in China, a novel NDVI-constrained regional tropospheric delay model (NZTD) is established by considering the topographic factors represented by NDVI. The results show that the root mean square error (RMSE) and the mean bias (Bias) of the NZTD model in China are 7.91 mm and 1.43 mm, showing 70% and 65% decreases compared with that of the GPT3 model, respectively. Meanwhile, the NZTD model possesses stronger anti-interference to seasonal changes than the GPT3 model. In addition, the NZTD model demonstrates significantly improved accuracy performance compared with the regional tropospheric delay model without NDVI constraint, evidenced by the average enhancement of 8%. It shows that the NZTD model can refine the environmental characteristics of different geographical regions and reflect the fine changes of tropospheric delay, with better adaptability and accuracy.

  • [1]
    姚宜斌, 赵庆志. GNSS对流层水汽监测研究进展与展望[J]. 测绘学报, 2022, 51(6): 935-952.
    [2]
    SAASTAMOINEN J. Atmospheric correction for the troposphere and stratosphere in radio ranging satellites[J]. The use of artificial satellites for geodesy, 2013(15): 247-251. DOI: 10.1029/GM015p0247
    [3]
    FARHAT NH, PSALTIS D, PRATA A, et al. Optical implementation of the hopfield model[J]. Applied optics, 1985, 24(10): 1469. DOI: 10.1364/AO.24.001469
    [4]
    孙鹏. GNSS实时水汽反演关键算法研究[D]. 徐州: 中国矿业大学, 2022.
    [5]
    PENNA N, DODSON A, CHEN W. Assessment of EGNOS tropospheric correction model[J]. Journal of navigation, 2001, 54(1): 37-55. DOI: 10.1017/s0373463300001107
    [6]
    LAGLER K, SCHINDELEGGER M, BOHM J, et al. Gpt2: empirical slant delay model for radio space geodetic techniques[J]. Geophysical research letters, 2013, 40(6): 1069-1073. DOI: 10.1002/grl.50288
    [7]
    BOHM J, MOLLER G, SCHINDELEGGER M, et al. Development of an improved empirical model for slant delays in the troposphere (GPT2w)[J]. GPS solutions, 2015, 19(3): 433-441. DOI: 10.1007/s10291-014-0403-7
    [8]
    杨旭, 何祥祥, 王媛媛, 等. 一种基于机器学习算法的区域/单站ZTD组合预测模型[J]. 全球定位系统, 2022, 47(1): 98-102,126.
    [9]
    王勇, 张立辉, 杨晶. 基于BP神经网络的对流层延迟预测研究[J]. 大地测量与地球动力学, 2011, 31(3): 134-137.
    [10]
    肖恭伟, 欧吉坤, 刘国林, 等. 基于改进的BP神经网络构建区域精密对流层延迟模型[J]. 地球物理学报, 2018, 61(8): 3139-3148.
    [11]
    时瑶佳, 吴飞, 朱海, 等. 基于Keras平台的LSTM模型的对流层延迟预测[J]. 全球定位系统, 2020, 45(6): 115-122.
    [12]
    张洛恺. 地基GNSS反演大气水汽含量方法研究[D]. 郑州: 解放军信息工程大学, 2014.
    [13]
    PETTORELLI N. Using the satellite-derived NDVI to assess ecological responses to environmental change[J]. Trends in ecology & evolution, 2005, 20(9): 503-510. DOI: 10.1016/j.tree.2005.11.006
    [14]
    HE T, LIU F Y, WANG A, et al. Estimating monthly surface air temperature using MODIS LST data and an artificial neural network in the loess plateau, china[J]. Chinese geographical science, 2023, 33(4): 751-763. DOI: 10.1007/s11769-023-1370-0
    [15]
    SAVICH AW, MOUSSA M, AREIBI S. The impact of arithmetic representation on implementing MLP-BP on FPGAs: a study[J]. IEEE transactions on neural networks, 2007, 18(1): 240-252. DOI: 10.1109/TNN.2006.883002
    [16]
    谢劭峰, 潘清莹, 黄良珂, 等. 中国区域ZTD、ZWD高程缩放因子的时空特性分析[J]. 大地测量与地球动力学, 2021, 41(12): 1211-1215,1240.
    [17]
    CHAI T, DRAXLER R R. Root mean square error (RMSE) or mean absolute error (MAE)? -arguments against avoiding RMSE in the literature[J]. Geoscientific model development discussions, 2014, 7(3): 1247-1250. DOI: 10.5194/gmd-7-1247-2014
    [18]
    SHINICHI N, HOLGER S. A general and simple method for obtaining R2 from generalized linear mixed-effects models[J]. Methods in ecology and evolution, 2013, 4(2): 133-142. DOI: 10.1111/j.2041-210x.2012.00261.x
    [19]
    文仙姣. GPT3模型的综合性能评价与优化方法[J]. 科技通报, 2023, 39(11): 9-14,27.
    [20]
    罗亦泳, 张静影, 陈郡怡, 等. 基于相空间重构和高斯过程回归的对流层延迟预测[J]. 武汉大学学报(信息科学版), 2021, 46(1): 103-110.
    [21]
    綦子民, 屈小川, 赖山东, 等. GPT3模型在安徽地区的性能[J]. 大地测量与地球动力学, 2023, 43(5): 481-486.

Catalog

    Article Metrics

    Article views (38) PDF downloads (47) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return