  Remotely sensed, spatially continuous and high spatiotemporal resolution
(hereafter referred to as high resolution) land surface temperature (LST) is a
key parameter for studying the thermal environment and has important
applications in many fields. However, difficult atmospheric conditions, sensor
malfunctioning and scanning gaps between orbits frequently introduce spatial
discontinuities into satellite-retri1eved LST products. For a single sensor,
there is also a trade-off between temporal and spatial resolution and,
therefore, it is impossible to obtain high temporal and spatial resolution
simultaneously. In recent years the reconstruction and spatiotemporal fusion of
LST products have become active research topics that aim at overcoming this
limitation. They are two of most investigated approaches in thermal remote
sensing and attract increasing attention, which has resulted in a number of
different algorithms. However, to the best of our knowledge, currently no
review exists that expatiates and summarizes the available LST reconstruction
and spatiotemporal fusion methods and algorithms. This paper introduces the
principles and theories behind LST reconstruction and spatiotemporal fusion and
provides an overview of the published research and algorithms. We summarized
three kinds of reconstruction methods for missing pixels (spatial, temporal and
spatiotemporal methods), two kinds of reconstruction methods for cloudy pixels
(Satellite Passive Microwave (PMW)-based and Surface Energy Balance (SEB)-based
methods) and three kinds of spatiotemporal fusion methods (weighted
function-based, unmixing-based and hybrid methods). The review concludes by
summarizing validation methods and by identifying some promising future
research directions for generating spatially continuous and high resolution LST
products.
