Spatial interpolation of meteorological parameters, closely related to the earth surface, plays important roles in climatological study. However, most of traditional spatial interpolation methods ignore the geographic semantics of interpolation sample points in practical application. This paper attempts to propose an improved inverse-distance weighting interpolation algorithm considering geographic semantics (S-IDW), which adds geographic semantic similarity to the traditional IDW formula and adjusts weight coefficient. In the interpolation process, the geographic semantic differences between sample points and estimation points are considered comprehensively. In this study, 3 groups of land surface temperature data from 2 different areas were selected for experiments, and several commonly used spatial interpolation methods were compared. Experimental results indicated that S-IDW outperformed IDW and several existing spatial interpolation methods, but there were also some abnormal value and interpolation outliers. This method provides a new insight toward the estimation accuracy, data missing, and error correction of spatial attributes related to meteorological parameters.

中文翻译：

---

考虑地理语义的气象参数空间插值

与地球表面密切相关的气象参数的空间插值在气候研究中起着重要作用。然而，在实际应用中，大多数传统的空间插值方法都忽略了插值采样点的地理语义。本文尝试提出一种考虑地理语义（S-IDW）的改进的反距离加权插值算法，该算法将地理语义相似度添加到传统的IDW公式中并调整权重系数。在插值过程中，综合考虑了采样点和估计点之间的地理语义差异。在这项研究中，选择了来自2个不同区域的3组陆地表面温度数据进行实验，并比较了几种常用的空间插值方法。实验结果表明，S-IDW优于IDW和几种现有的空间插值方法，但是也存在一些异常值和插值离群值。该方法为有关气象参数的空间属性的估计准确性，数据丢失和错误校正提供了新的见解。