Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, China.
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel). 2019 Feb 8;19(3):698. doi: 10.3390/s19030698.
A The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature () model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.
研究目的在于提出一种插补方法（LZ 方法）来改进 GNSS 反演的水汽总含量（PWV）图。我们为此实验建立了一个区域加权平均温度（）模型，与之前的模型相比，该模型对 GNSS 反演的 PWV 结果仅引入了微小的差异。LZ 方法的核心在于通过虚拟样本点增加样本密度。这些虚拟样本点源于数字高程模型（DEM），是基于 PWV 与地理位置（即地理坐标和海拔）之间的显著相关性构建的。我们对 LZ 方法进行了验证，并与仅考虑原始样本点的传统插值方法进行了比较。结果表明，LZ 方法生成的 PWV 图比传统方法显示出更多的细节。此外，LZ 方法的预测性能也优于传统方法，这是通过交叉验证得到的。
