No single satellite remote sensing system is able to provide the observations on the Earth's surface at both high spatial and high temporal resolution due to the general trade-off between orbit revisit frequency and satellite sensor's spatial resolution. This paper presents a spatio-temporal Cokriging (ST-Cokriging) method for assimilating remote sensing data sets acquired by multiple remote sensing systems with different temporal sampling frequencies and different spatial resolutions. By extending the traditional Cokriging technique from a sole spatial domain to a spatio-temporal domain, we derived and implemented ST-Cokriging algorithm that explicitly takes the spatial covariance, temporal covariance and spatio-temporal covariance structures within and between different data sets into account. Compared with previous downscaling methods, such as, STARFM and FSDAF, our ST-Cokriging method produces more accurate and reliable assimilation results for the heterogeneous region, with associated uncertainty estimates. This method has been implemented into a software package using Python language within ArcGIS environment. The advantages and effectiveness of our ST-Cokriging method have been demonstrated through an application example, in which MODIS images (daily, 250 m and 500 m spatial resolution) and Landsat TM/ETM+ images (16 days revisit cycle, 30 m) are assimilated to generate daily spectral bands and NDVI images at 30 m spatial resolution. Our validation and accuracy assessments indicate that our ST-Cokriging method can effectively fill in data gaps due to clouds and generate reliable assimilation results and uncertainty estimates at both high spatial resolution and high temporal frequency

由于轨道重访频率与卫星传感器的空间分辨率之间存在一般的权衡关系，因此没有一个单一的卫星遥感系统能够在高空间分辨率和高时间分辨率下提供对地球表面的观测。本文提出了一种时空协同克里格法（ST-Cokriging）方法，用于同化具有不同时间采样频率和不同空间分辨率的多个遥感系统获取的遥感数据集。通过将传统的Cokriging技术从唯一的空间域扩展到时空域，我们推导并实现了ST-Cokriging算法，该算法明确考虑了不同数据集内和之间的空间协方差，时间协方差和时空协方差结构。与以前的缩减方法相比，例如STARFM和FSDAF，我们的ST-Cokriging方法可为异质区域产生更准确和可靠的同化结果，并具有相关的不确定性估计。该方法已在ArcGIS环境中使用Python语言实现为软件包。通过一个应用实例证明了我们的ST-Cokriging方法的优势和有效性，其中MODIS图像（每天250 m和500 m空间分辨率）和Landsat TM / ETM +图像（16天重访周期30 m）被同化了生成每日光谱带和30 m空间分辨率的NDVI图像。