  Remote sensing images and techniques are powerful tools to investigate earth
surface. Data quality is the key to enhance remote sensing applications and
obtaining a clear and noise-free set of data is very difficult in most
situations due to the varying acquisition (e.g., atmosphere and season),
sensor, and platform (e.g., satellite angles and sensor characteristics)
conditions. With the increasing development of satellites, nowadays Terabytes
of remote sensing images can be acquired every day. Therefore, information and
data fusion can be particularly important in the remote sensing community. The
fusion integrates data from various sources acquired asynchronously for
information extraction, analysis, and quality improvement. In this chapter, we
aim to discuss the theory of spatiotemporal fusion by investigating previous
works, in addition to describing the basic concepts and some of its
applications by summarizing our prior and ongoing works.
