The combination of satellite laser ranging (SLR) observations to various low earth orbit (LEO) satellites can enhance the accuracy and robustness of SLR-derived geodetic parameters, benefiting the realization of the International terrestrial reference frames. Observation stochastic models play a critical role in the integrated processing of SLR observations to multiple LEO satellites. The consideration of precision in heterogeneous SLR observations from various satellites is essential. In this study, we aim to improve the combination of multi-LEO SLR observations for geodetic parameters determination by optimizing the stochastic model using variance component estimation (VCE). We perform weekly estimates of the geodetic parameters, including station coordinates, Earth rotation parameters, and geocenter coordinates (GCC), using three years of SLR observations to seven LEO satellites at different orbits. The satellite-dependent, station-dependent, and satellite–station-dependent variance components are separately estimated through VCE processing to refine the stochastic models. Given the fact that the precision of SLR observations significantly differs in satellites and stations, the multiple LEO combination can be significantly improved with the implementation of VCE. Satellite–station-pair-dependent variance components are more suitable to the SLR VCE and the accuracy of station coordinates, pole coordinates, and length of day can be averagely improved by 8.4, 22.6, and 21.9%, respectively, compared to the equal-weight solution. Our result also indicates that the observation insufficiency for some stations may result in an unreliable VCE estimation, and eventually leads to an accuracy degradation for station coordinates. To overcome this deficiency, we adopt the variance components derived from the monthly solutions to build the stochastic model in the weekly solutions. The application of monthly weights can effectively mitigate the accuracy deterioration of station coordinates, improving the repeatability of the station coordinates by 15.9, 14.6, and 9.2% with respect to the equal-weight solution in E, N, and U components. The global geodetic parameters also benefit from this processing. The import of monthly weight decreases the outliers in the GCC series, especially in the X and Y components.

将卫星激光测距（SLR）观测与各种低地球轨道（LEO）卫星相结合，可以提高SLR导出的大地测量参数的准确性和鲁棒性，有利于国际地球参考系的实现。观测随机模型在多颗LEO卫星SLR观测的综合处理中发挥着关键作用。考虑不同卫星的异构 SLR 观测的精度至关重要。在本研究中，我们的目标是通过使用方差分量估计（VCE）优化随机模型，改进多 LEO SLR 观测组合用于大地测量参数的确定。我们使用三年来对不同轨道上的七颗 LEO 卫星进行的 SLR 观测，每周对大地测量参数进行估计，包括站点坐标、地球自转参数和地心坐标 (GCC)。通过 VCE 处理分别估计卫星相关、站点相关和卫星站相关方差分量，以细化随机模型。鉴于SLR观测精度在卫星和站点上存在显着差异，通过VCE的实施可以显着提高多LEO组合。星站对相关方差分量更适合SLR VCE，站坐标、极坐标和日长精度比同等条件平均分别提高8.4%、22.6%和21.9%。重量解决方案。我们的结果还表明，某些站点的观测不足可能会导致 VCE 估计不可靠，并最终导致站点坐标的精度下降。 为了克服这一缺陷，我们采用从月解中导出的方差分量来构建周解中的随机模型。月权重的应用可以有效缓解站坐标精度恶化的情况，相对于E、N、U分量等权解，站坐标重复性分别提高了15.9%、14.6%和9.2%。全球大地测量参数也受益于该处理。每月权重的导入减少了 GCC 系列中的异常值，尤其是 X 和 Y 分量中的异常值。