复杂地形地区月平均气温(混合)地理加权回归克里格插值

以地形地貌特征复杂、观测站点分布稀疏不均匀的四川省为研究区，引入地形因子（坡度和坡向）和植被指数，采用顾及空间关系非平稳性的（混合）地理加权回归克里格模型（（mixed）geographically weighted regression Kriging，（m）GWRK）进行月尺度平均气温插值方法及精度分析研究。针对不同季节和不同地区，将（m）GWRK插值结果与基于全局回归的回归克里格（regression Kriging，RK）插值结果进行对比。结果表明，RK、GWRK、mGWRK回归关系的决定系数R2分别为0.795、0.922、0.911，均方根误差分别为0.83℃、0.64℃、0.55℃，表明GWRK、mGWRK对目标变量的解释能力以及插值精度都优于RK；GWRK、mGWRK相对于RK对月平均气温插值的改进具有季节与地区差异，冬半年的改进大于夏半年，在地形地貌变化大的地区改进大于地形地貌变化小的地区。

Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region

Based on the complex topographical features and sparse uneven observation sites in Sichuan Province, terrain factors (slope and aspect) and vegetation index were introduced in this paper. The method of (mixed) geographically weighted regression kriging ((m)GWRK) which took into account the non-stationary of spatial relationship was adopted to study the interpolation method of monthly mean temperature and the precision analysis of the estimation results. In different seasons and different regions, the estimation results of (m)GWRK and regression Kriging (RK) based on global regression were compared. The results indicate that the coefficient of determination(R2) of regression relationship of RK, GWRK and mGWRK are 0.795, 0.922 and 0.911, respectively, and root meansquare error of these three methods are 0.83℃, 0.64℃, 0.55℃, respectively. This implies (m)GWRK is better than RK in ability to interpret the target variable and estimation accuracy. Compared with RK, the improvements of (m)GWRK on estimating monthly average temperature have the characteristics of seasonal and regional differences. The improvement is more significant in winter half year than in summer half year. And in northwest and southwest Sichuan, where topography changes acutely, the improvement is greater than in basin where topography changes gently.