Remote sensing images can provide important and abundant information about the Earth at a global or local scale. Thus

many applications often require remote sensing data with high acquisition frequency and high spatial resolution. However

meeting this requirement is a considerable challenge given satellite limitations. The spatiotemporal fusion method provides a feasible way to solve these "spatialtemporal" contradictions.In the last 10 years

spatiotemporal fusion has elicited wide interest in various applications because it integrates the superiority of multisource satellite data in fine spatial resolution or frequent temporal coverage and it can generate fused images with high spatial and temporal resolution. In this study

we reviewed the advantages and limitations of three types of method for spatiotemporal fusion

namely

transformation-based

reconstruction-based

and learning-based methods. First

the transformation-based method consistently filters and processes transformed data and then accesses high-spatiotemporal resolution data via inverse transform. It mainly focuses on the spatial and spectral information of multi-source satellite image enhancement or fusion. The spatial resolution of the results obtained with this method remains low

and the accuracy is relatively poor because the temporal change information is not used in this method. Second

the reconstruction-based method has elicited much attention since the proposal of a semi-physical fusion model and STARFM. This method integrates the information of temporal change

spatial change

and spectral change among multi-source satellite images acquired in different times and generates high-spatiotemporal resolution data by calculating the weight of different changes. This method provides an excellent fusion approach for spatiotemporal fusion because the results show high accuracy.However

the results would be poor when the type of land cover changes or the cover area is heterogeneous. Third

the learning-based method is based on the development of compressed sensing and sparse representation technology. This method represents a recent developmentthat relies on learning the relationship and difference of multi-source satellite images by training samples and constructing an image dictionary. Although the learning-based method could obtain good results

the processing efficiency is lower than that of other methods

and it requires the training of sample selection. Recently

the result of spatiotemporal fusion has been used in various applications

especially in the reconstruction-based method. This method is mainly used in time series data analysis as well as in retrieval and regional data set generation. For time series data analysis and retrieval

many researchers have used the results in developing the missing images of time series

detecting phenology

inversing urban environment parameters

estimating gross primary production

evaluating biomass

calculating land surface temperature

and so on. Given that the covered area of a low spatial resolution is large and the spectrum continuity of spatiotemporal fusion results is high

these results could be applied to the generation of regional data sets. Although the spatiotemporal fusion method has seen considerable development

certain problems remain. The uncertainties are attributed to the complexity of land cover change

the errors of sensor calibration

and the data pretreatment process. The five potential aspects of the spatiotemporal fusion method that require further study are the consistency of data from different sensors

introduction of nonlinear mixed models

addition of prior knowledge

introduction of deep learning theory

and expansion into other satellites.