对流层延迟一直被认为是影响重轨合成孔径雷达干涉测量（Interferometric Synthetic Aperture Radar, InSAR）对地观测精度的重要因素之一。由于对流层的水汽含量丰富且变化复杂，因此很难准确地表示雷达信号在穿过对流层时所受到的延迟。目前学者们已经提出了多种基于不同角度的对流层延迟校正方法，但是这些方法仍然存在着一定的局限性。基于自身相位的校正方法没有考虑大气的实际运动特征，可能会使得校正后的干涉图变得更差。基于外部辅助数据的校正方法会受其时空分辨率的影响，以及因辅助数据自身估算大气延迟存在偏差而受限。现有的基于机器学习的延迟校正方法受地形和位置的影响较大，因此区域普适性不高。针对现存的这些问题，本文以全球导航卫星系统（Global Navigation Satellite System, GNSS）地面站点的对流层延迟数据为基础，分别探讨了两种InSAR对流层延迟校正方法。此外，还对比了全球尺度下几种常见的对流层延迟数据的时空变化特征，并分析了它们在InSAR对流层延迟校正中的可用性。本文的主要研究工作如下：

（1）针对GNSS只能提供站点位置的高质量对流层延迟数据，与干涉图像素在空间上不匹配的问题，提出一种基于GNSS和反向传播神经网络（Back Propagation Neural Network, BPNN）的对流层延迟数据空间预测方法，并将该方法应用于美国洛杉矶地区进行校正效果验证。该方法以GNSS站点天顶总延迟（Zenith Tropospheric Delay，ZTD）数据为基础，以区域地形和位置信息为空间预测因子，预测了2021年42张干涉图对应的对流层延迟图。实验表明：GNSS+BPNN方法较常规空间插值方法的均方根误差（Root Mean Square Error, RMSE）下降幅度超过90%，92.86%的干涉图校正后标准差出现了下降，平均下降幅度为52.03%。与GACOS方法相比，季节校正效果更加明显，校正性能更加稳定。尤其是在夏季和冬季，性能明显优于GACOS方法。

（2）为了确定全球尺度下几种常见对流层延迟数据在InSAR延迟校正中的可用性，本文分析了2015-2022年全球GNSS、ERA5、MERRA2估算的对流层延迟变化特征，并估算了2022年夏季和冬季（相对于北半球）的MODIS对流层湿延迟（Zenith Wet Delay, ZWD）。然后，以美国西部为例对比了各数据之间的差异，同时探讨了美国西海岸2022年12月下旬的极端天气对各数据估算InSAR对流层延迟的影响。结果表明：GNSS ZTD与地形高度呈现显著的负指数函数关系，而ZWD则并不明显。ERA5和MERRA2的ZWD在空间上基本与GNSS ZWD保持一致。此外，经过对比发现ERA5在出现一次ZWD波峰之后数据会出现异常。而MODIS数据往往高估了ZWD，并且在极端水汽期间数据容易被污染，导致其可用性不高。

（3）针对局部地区GNSS站点稀疏且分布不均，直接使用GNSS ZTD预测InSAR对流层延迟时误差较大的问题，提出基于GNSS融合多源数据的InSAR对流层延迟校正方法。利用GNSS ZTD与地形之间的函数关系分离垂直分层延迟和湍流混合延迟，并分别对两部分进行建模，在此基础之上进一步通过BPNN融合ERA5和MERRA2数据，以获取最终的InSAR对流层延迟产品。选取2022年夏威夷岛的干涉图对该方法的延迟校正性能进行对比验证。结果表明：使用融合后的延迟数据的校正效果较其他几种方法效果更加显著，标准差平均下降2.85 rad，平均标准差下降为42.47%。此外，融合后的数据在湍流混合延迟占据主导地位或延迟信号和形变信号混合时都能有较好的校正表现。

Tropospheric delay has long been recognized as one of the key factors affecting the accuracy of ground observation using interferometric synthetic aperture radar (InSAR). Due to the abundant and complex variations in water vapor content within the atmosphere, accurately representing the delay experienced by radar signals as they pass through the troposphere is challenging. Various methods for tropospheric delay correction based on different perspectives have been proposed by scholars, but these methods still have certain limitations. The correction method based on the phase of the interferogram itself does not consider the actual motion characteristics of the troposphere, which may result in a worse interferogram after correction. Correction methods based on external auxiliary data are affected by their spatiotemporal resolution and are limited by biases in the estimation of tropospheric delay from the auxiliary data. The existing machine learning-based delay correction methods are significantly influenced by terrain and location, thus resulting in limited regional applicability. To address these existing issues, this paper explores two InSAR tropospheric delay correction methods based on the data from global navigation satellite system (GNSS) ground stations. Additionally, the spatiotemporal variation characteristics of several common atmospheric delay data on a global scale are compared, and their availability in InSAR tropospheric delay correction is analyzed. The primary research content of this paper are as follows:

(1) Aiming at the problem that GNSS can only provide high-quality tropospheric delay data at site locations, which does not match the pixels of the interferogram in space, a spatial prediction method for tropospheric delay data based on GNSS and back propagation neural network (BPNN) is proposed. This method is applied to validate the correction effect in the Los Angeles area of the United States. Utilizing the zenith tropospheric delay (ZTD) data from GNSS stations as the basis and regional terrain and location information as spatial prediction factors, the method predicts tropospheric delay maps corresponding to 42 interferograms in 2021. The experiment demonstrates that the GNSS+BPNN method reduces the root mean square error (RMSE) by over 90% compared to conventional spatial interpolation methods. Furthermore, 92.86% of interferograms exhibit a decrease in standard deviation after correction, with an average reduction of 52.03%. Compared to the GACOS method, the seasonal correction effect of GNSS+BPNN method is more pronounced, and the correction performance is notably more stable. Particularly during summer and winter, its performance exhibits a significant improvement over that of the GACOS method.

(2) To determine the availability of several common tropospheric delay data at global scales for InSAR delay corrections, this paper analyzes the tropospheric delay change characteristics estimated by global GNSS, ERA5, and MERRA2 from 2015 to 2022, and estimates the MODIS tropospheric wet delay (ZWD) in the summer and winter of 2022 (relative to the Northern Hemisphere). Subsequently, differences among these datasets are compared using the example of the western United States, while investigating the impact of extreme weather events in late December 2022 on the estimation of InSAR tropospheric delay. The findings reveal a substantial inverse exponential correlation between GNSS ZTD and terrain altitude, whereas the correlation between ZWD and terrain is less pronounced. The spatial distribution of ZWD from ERA5 and MERRA2 generally corresponds well with that from GNSS. Moreover, ERA5 exhibits anomalous changes following the occurrence of a ZWD peak.  In contrast, MODIS data often overestimate the ZWD, with data severely contaminated during periods of extremely high water vapor content, leading to low usability.

(3) In response to the problem posed by the sparse and uneven distribution of GNSS stations in local regions, coupled with the significant errors incurred when directly utilizing GNSS ZTD for InSAR tropospheric delay prediction, we propose an InSAR tropospheric delay correction method based on GNSS fusion of multi-source data. We separates the vertical stratified delay (VSD) and turbulent mixing delay (TMD) by leveraging the functional relationship between GNSS ZTD and terrain, and models them separately. Additionally, we employ BPNN to fuse ERA5 and MERRA2 data to further enhance the accuracy of InSAR tropospheric delay estimation. We validate the correction performance of our method using interferograms from the Hawaiian Islands in the summer of 2022. The results indicate that the correction effect using fused delay data is more significant compared to other methods, with an average decrease in standard deviation of 2.85 rad and an average percentage decrease in standard deviation of 42.47%. Furthermore, our fused data exhibits better correction performance in scenarios dominated by turbulent mixed delays or when delay signals are mixed with deformation signals.

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