利用MODIS温度产品进行秩修正滤波FRF时空插值

普通克里金方法具有空间结构探索及插值分析（给出插值结果及其精度）的功能。但是，绝大部分的克里金方法主要用于空间插值，在时空插值方面的研究还较少。综合考虑具体的实验区域和观测数据构建基函数，使用秩修正滤波（fixed rank filtering，FRF）方法对MODIS平流层温度数据进行时空插值预测并将其结果与秩修正克里金（Fixed rank Kriging，FRK）方法的插值结果进行对比分析。实验结果表明，在空间数据（空间点）整体分布均匀且有已知点的情况下，FRK方法预测的数据精度更高，略优于FRF；而对于较大空间范围内缺失数据的情况，考虑温度在时间维上具有一定的相关性，FRF方法在缺失空间信息时能够引入更多时空信息从而获得较其他方法更高质量的插值结果。

Appendix A: FUNFITS,Data Analysis and Statistical Tools for Estimating Functions

Kriging is widely-used for spatial structure exploration and spatial data interpolation. However, most kriging methods are designed for spatial interpolation and not for spatio-temporal data interpolation. After constructing basic functions for a specific experimental area, we made spatio-temporal predictions for MODIS temperature data with Fixed Rank Filtering (FRF). We compared these prediction results with the interpolation results of Fixed Rank Kriging (FRK) and discuss the differences. Experimental results show that when points are evenly distributed in space, the FRK method obtains higher prediction accuracy with results slightly better than FRF. However considering the temporal relevancy of temperature, when data is missing in a larger area, the FRF method shows a capability to comprehensively exploit spatio-temporal information better than other methods for achieving higher quality interpolation results in cases that lack spatial information.