Fine spatial resolution remote sensing images are crucial sources of data for monitoring the Earth's surface. Due to defects in sensors and the complicated imaging environment, however, fine spatial resolution images always suffer from various degrees of information loss. According to the basic attributes of remote sensing images, the information loss generally falls into three dimensions, that is, the spatial, temporal and spectral dimensions. In recent decades, many methods have been developed to cope with this information loss problem in the three dimensions, which are termed spatial reconstruction, temporal reconstruction and spectral reconstruction in this paper. This paper presents a comprehensive review of all three types of reconstruction. First, a systematic introduction and review of the achievements is provided, including the refined general mathematical framework and diagram for each of the three parts. Second, the applications in various areas (e.g., meteorology, ecology and environmental science) are introduced. Third, the challenges and recent advances of spatial-temporal-spectral information reconstruction are summarized, such as the efforts for dealing with abrupt land cover changes in spatial reconstruction, inconsistency in multi-scale data acquired by different sensors in temporal reconstruction, and point spread function (PSF) effect in spectral reconstruction. Finally, several thoughts are given for future prospects.