顾及地形因素的区域对流层延迟建模  

Regional tropospheric delay modeling considering terrain environment factors

作　　者：刘德湘 张轩祯 刘城伯 李昕[1] LIU Dexiang;ZHANG Xuanzhen;LIU Chengbo;LI Xin(School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China)

机构地区：[1]武汉大学测绘学院,武汉430079

出　　处：《全球定位系统》2025年第1期86-92,共7页Gnss World of China

基　　金：国家重点研发计划项目(2021YFC3000504)。

摘　　要：对流层延迟是影响GNSS精密定位的主要误差源之一.近年来,机器学习被广泛应用于对流层延迟建模领域,本文基于中国区域的GNSS对流层数据和归一化植被指数(normalized difference vegetation index,NDVI),建立了一种顾及以NDVI为代表的地形因素的区域对流层延迟(NDVI constrained regional zenith tropospheric delay,NZTD)模型.结果表明,NZTD模型在中国区域内的均方根误差(root mean square error,RMSE)为7.91 mm,平均偏差(Bias)为1.43 mm,较GPT3模型分别减少了70%和65%,且对季节性变化具有更强的抗干扰性.此外,NZTD模型的预测性能较未约束NDVI的区域对流层延迟模型具有显著提升,平均精度提升为8%.这表明,NZTD模型能够细化不同地理区域的环境特征、反映对流层延迟的精细变化,具有更好的适应性和准确性.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.

关 键 词：GNSS 对流层延迟 归一化植被指数(NDVI) 机器学习 

分 类 号：P228[天文地球—大地测量学与测量工程]