# Spatiotemporal Image Fusion in Remote Sensing
## 2 Fusion Methods to Increase Spatiotemporal Resolution of Satellite Images
Therefore, to increase the temporal resolution of fine spatial resolution images, many spatiotemporal image fusion methods have been developed during the last years. These methods use spatial information from the fine spatial resolution images and temporal information from coarse resolution satellites images to generate high spatial-temporal images. Spatiotemporal image fusion methods apply several steps to generate high spatiotemporal images: (1) both coarse and fine-resolution satellite images Digital Numbers (DN) have to be atmospherically corrected; (2) the pair-images have to be geometrically corrected and (3), in the end, one of the existing spatiotemporal fusion methods is applied to generate images which are at an increased spatial and temporal resolution (Figure 1). When the application required, calculation of indices before spatiotemporal fusion is performed [40]. Figure 1: Theoretical workflow of spatiotemporal image fusion. DN stands for Digital Number. According to [PERSON] et al. [41], spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based; (2) unmixing based and (3) learning-based methods. The advantages and disadvantages of these methods are presented in [42]. An overview of some of the existing spatiotemporal image fusion methods are presented in Table 1. These methods have been successfully applied in different application domains including forest and crop monitoring, daily-field scale evapotranspiration etc. [10]. We evaluated how many times the above-listed methods were evaluated using the Web of Science database (Figure 2). This evaluation revealed that STARFM method, followed by ESTARFM and STAARCH are among the most popular spatiotemporal image fusion methods. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Spatiotemporal Fusion Model** & **Categories** \\ \hline [PERSON] et al. [32] & Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) & Reconstruction-based \\ \hline [PERSON] et al. [43] & Spatial-Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) & Reconstruction-based \\ \hline [PERSON] et al. [44] & Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) & Reconstruction-based \\ \hline [PERSON] and [PERSON] [45] & Spatiotemporal image-fusion model (STI-FM) & Reconstruction-based \\ \hline [PERSON] et al. [46] & Satellite Data Integration (STAIR) & Reconstruction-based \\ \hline [PERSON] et al. [47] & Robust Adaptive Spatial and Temporal Fusion Model (RASFTM) & Reconstruction-based \\ \hline [PERSON] and [PERSON] [48] & FIT-FC & Reconstruction-based \\ \hline [PERSON] et al. [41] & Hierarchical Spatiotemporal Adaptive Fusion model (HSTAFM) & Learning-based \\ \hline [PERSON] et al. [7] & Sparse representation based Spatio temporal reflectance Fusion Model (SPSTFM) & Learning-based model \\ \hline [PERSON] and [PERSON] [49] & One-pair learning image fusion model & Learning-based model \\ \hline [PERSON] et al. [50] & Spatial and Temporal Data Fusion Approach (STDFA) & Unmixing-based \\ \hline [PERSON] and [PERSON] [51] & Spatio-Temporal Reflectance Fusion Model (U-STFM) & Unmixing-based \\ \hline [PERSON] et al. [36] & Spatial and Temporal Reflectance Unmixing Model (STRUM) & Unmixing-based \\ \hline [PERSON] et al. [52] & Modified Spatial and Temporal Data Fusion Approach (MSTDFA) & Unmixing-based \\ \hline [PERSON] et al. [53] & Constrained unmixing image fusion model & Unmixing-based \\ \hline [PERSON] et al. [54] & Spatial-Temporal Fraction Map Fusion (STFMF) & Unmixing-based \\ \hline [PERSON] et al. [12] & Flexible Spatiotemporal Data Fusion (FSDAF) & Hybrid \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of some of the spatiotemporal image fusion methods developed for blending high spatial resolution images with high temporal resolution images. The dark-grey highlighted methods are those based on feature fusion level, whereas the remaining ones are based on pixel fusion level.