The Terra/Aqua MODerate resolution Imaging Spectroradiometer (MODIS) data have been used widely for global monitoring of the Earth's surface due to their daily fine temporal resolution. The spatial resolution of MODIS time-series (i.e., 500 m), however, is too coarse for local monitoring. A feasible solution to this problem is to downscale the coarse MODIS images, thus creating time-series images with both fine spatial and temporal resolutions. Generally, the downscaling of MODIS images can be achieved by fusing them with fine spatial resolution images (e.g., Landsat images) using spatio-temporal fusion methods. Among the families of spatio-temporal fusion methods, spatial unmixing-based methods have been applied widely owing to their lighter dependence on the available fine spatial resolution images. However, all techniques within this class of method suffer from the same serious problem, that is, the block effect, which reduces the prediction accuracy of spatio-temporal fusion. To our knowledge, almost no solution has been developed to tackle this issue directly. To address this need, this paper proposes a blocks-removed spatial unmixing (SU-BR) method, which removes the blocky artifacts by including a new constraint constructed based on spatial continuity. SU-BR provides a flexible framework suitable for any existing spatial unmixing-based spatio-temporal fusion method. Experimental results on a heterogeneous region, a homogeneous region and a region experiencing land cover changes show that SU-BR removes the blocks effectively and increases the prediction accuracy obviously in all three regions. SU-BR also outperforms two popular spatio-temporal fusion methods. SU-BR, thus, provides a crucial solution to overcome one of the longest standing challenges in spatio-temporal fusion.

Terra / Aqua中等分辨率成像光谱辐射仪（MODIS）数据由于每天的精细时间分辨率而被广泛用于全球监测地球表面。但是，MODIS时间序列（即500 m）的空间分辨率对于本地监视来说太粗糙了。该问题的可行解决方案是缩小粗略的MODIS图像，从而创建具有良好空间和时间分辨率的时间序列图像。通常，可以通过使用时空融合方法将MODIS图像与精细的空间分辨率图像（例如Landsat图像）融合来实现MODIS图像的缩小。在时空融合方法家族中，基于空间分解的方法由于对可用的精细空间分辨率图像的依赖性较小而得到了广泛应用。然而，此类方法中的所有技术都遭受相同的严重问题，即块效应，这降低了时空融合的预测精度。据我们所知，几乎没有解决方案可以直接解决这个问题。为了满足这一需求，本文提出了一种块去除空间分解（SU-BR）方法，该方法通过包含基于空间连续性构造的新约束来去除块状伪影。SU-BR提供了适用于任何现有的基于空间分解的时空融合方法的灵活框架。在异质区域，同质区域和经历土地覆盖变化的区域上进行的实验结果表明，SU-BR在所有三个区域中均有效地去除了块体并明显提高了预测精度。SU-BR还优于两种流行的时空融合方法。因此，SU-BR提供了一种关键的解决方案，可以克服时空融合中最长的挑战之一。