Development of a hybrid geoid model using a global gravity field model over Sri Lanka

IF 1.2 Q4 REMOTE SENSING
Dinithi Udarika Edirisinghe, Duminda Ranganath Welikanna, Thunendran Periyandy, Ranmalee Bandara
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Abstract

Sri Lanka is still in need of a well-defined local geoid model. This geoidal void has made present-day Global Navigation Satellite System (GNSS) surveys heavily dependent on Global Geopotential Models (GGMs) for height determination. Further, in many instances, the accuracy of GGMs have shown drawbacks in elevation determination over Sri Lanka. Therefore, the study focused on developing a hybrid geoid model (HGM) for Sri Lanka by integrating the available GGMs. Five high-resolution (2190°) GGMs; EGM2008, EIGEN-6C4, GECO, XGM2019e-2159, and SGG-UGM2 were employed to extract GGM-derived geoid undulation for 21 Fundamental Benchmarks (FBMs). The residuals (geoid height deviation) were calculated relative to the observed geoid undulation using GNSS/leveling on the FBMs. The data set was clustered based on topography, and residuals were adjusted using weighted least squares adjustment (LSA). The uneven distribution of the FBMs promotes topography-based clustering. EIGEN-6C4 is found to be the robust GGM for Sri Lanka to develop a hybrid approach, with a 0.001 m RMS value of estimated residuals in LSA. The resulting HGM was interpolated at 1 arc-second grid resolution (30 m × 30 m) using the Inverse Distance Weighted Interpolation. Regression lines were generated for the interpolated HGM with respect to the interpolated observed geoid undulation for 9 transects along the parallel passing through Mount Pedro and for the 16 transects along the meridian. The coefficient of determination on both lines is 0.999. HGM generated by EIGEN-6C4 has shown reliable RMS gradient and intercept values of 8.860078 × 10−9 and 0.0039239, respectively, in first-order polynomial fitting.
利用斯里兰卡全球重力场模型开发混合大地水准面模型
斯里兰卡仍然需要一个定义明确的当地大地水准面模型。这种大地水准面的空白使得目前的全球导航卫星系统(GNSS)测量严重依赖全球地势模型(GGMs)来确定高度。此外,在许多情况下,全球位势模型在确定斯里兰卡海拔高度方面的准确性存在缺陷。因此,研究重点是通过整合现有的大地水准面模型,为斯里兰卡开发一个混合大地水准面模型(HGM)。研究采用了五个高分辨率(2190°)GGM:EGM2008、EIGEN-6C4、GECO、XGM2019e-2159 和 SGG-UGM2,以提取 21 个基本基准(FBM)的 GGM 衍生大地水准面起伏。利用 FBM 上的全球导航卫星系统/水准测量,计算出与观测到的大地水准面起伏相对应的残差(大地水准面高度偏差)。根据地形对数据集进行分组,并使用加权最小二乘法调整(LSA)对残差进行调整。FBM 的不均匀分布促进了基于地形的聚类。EIGEN-6C4 被认为是斯里兰卡的稳健 GGM,可用于开发混合方法,在 LSA 中估计的残差均方根值为 0.001 m。使用反距离加权插值法,以 1 弧秒网格分辨率(30 米 × 30 米)对生成的 HGM 进行插值。为沿通过佩德罗山的平行线的 9 个横断面和沿子午线的 16 个横断面的内插 HGM 生成了与内插观测大地水准面起伏相关的回归线。两条线的判定系数均为 0.999。EIGEN-6C4 生成的 HGM 在一阶多项式拟合中显示出可靠的均方根梯度值和截距值,分别为 8.860078 × 10-9 和 0.0039239。
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来源期刊
Journal of Applied Geodesy
Journal of Applied Geodesy REMOTE SENSING-
CiteScore
2.30
自引率
7.10%
发文量
30
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