High spatio‐temporal variability of atmospheric water vapor affects microwave signals of Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). A better knowledge of the distribution of water vapor improves both GNSS‐ and InSAR‐derived data products. In this work, we present a collocation framework to combine and retrieve zenith and (relative) slant tropospheric delays. GNSS and InSAR meteorological products are combined aiming at a better retrieval of the atmospheric water vapor. We investigate the combination approach with synthetic and real data acquired in the Alpine region of Switzerland. Based on a closed‐loop validation with simulated delays, a few mm accuracy is achieved for the GNSS‐InSAR combination in terms of retrieved ZTDs. Furthermore, when real delays are collocated, the combination results are more congruent with InSAR computed products. This research is a contribution to improve the spatio‐temporal mapping of tropospheric delays by combining GNSS‐derived and InSAR‐derived delays.

中文翻译：

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一种组合框架，用于从GNSS和InSAR的组合中检索对流层延迟

大气水蒸气的高时空变化会影响全球导航卫星系统（GNSS）和干涉式合成孔径雷达（InSAR）的微波信号。更好地了解水蒸气的分布情况会改善GNSS和InSAR衍生的数据产品。在这项工作中，我们提出了一个搭配框架，以组合和检索天顶和（相对）倾斜对流层延迟。GNSS和InSAR气象产品相结合，旨在更好地回收大气中的水蒸气。我们使用瑞士阿尔卑斯地区获得的综合和真实数据调查组合方法。基于模拟延迟的闭环验证，就检索到的ZTD而言，GNSS-InSAR组合的精度达到了几毫米。此外，当实际延迟并置时，组合结果与InSAR计算产品更为一致。这项研究通过结合GNSS衍生和InSAR衍生的延迟，改善了对流层延迟的时空映射。