# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Increasing the Resolution of Various Satellite-Derived Data Products  
Important research efforts are dedicated to increasing the resolution of satellite-derived products such as NDVI, land surface temperature, evapotranspiration, and precipitation. In order to obtain a better resolution of these data products, several studies used the remote sensing data whose spatiotemporal resolution has been improved by means of one of the methods presented in this paper.  
A large number of these studies are dedicated to land surface temperature data, mainly because these data are important in a wide range of environmental modeling applications from local to global scales ([PERSON] et al., 2019; [PERSON] et al., 2021). There are basically two main methods used to increase the coarse resolution of land surface temperature, namely the methods which rely on physical models and those using statistical models ([PERSON] et al., 2019) such as the linear regression models ([PERSON] et al., 2019; [PERSON] et al., 2021), co-kriging model ([PERSON] et al., 2021) or random forest (RF) regression ([PERSON] et al., 2019; [PERSON] et al., 2021). [PERSON] et al. (2021) used a random forest model to downscale MODIS based land surface temperature in arid regions from 1 km to 500 m. [PERSON] et al. (2021) proposed a disaggregation method for subpixel temperature using the remote sensing endmember index-based method. ASTER visible and near-infrared bands and shortwave bands at 30 m spatial resolution were used in combination with the 990 m resolution MODIS land surface temperature data. [PERSON] et al. (2019) disaggregated MODIS surface temperature at 100 m by considering the temperature difference between photosynthetically and non-photosynthetically active vegetation. For this study, the authors used Formosat-2 data due to their high spatial resolution, i.e., 8 m, and high temporal resolution, i.e., one image per day. [PERSON] et al. (2021) applied the spatiotemporal data fusion methods developed by ([PERSON] et al., 2021; [PERSON] et al., 2021; [PERSON] et al., 2021) to generate high temporal and high spatial resolution land surface temperature product by combining ASTERand MODIS data products. The authors concluded that the quality of the generated land surface temperature products increased by using high spatiotemporal resolution satellite images.  
A region-based and pixel-based disaggregation method is proposed by [PERSON] et al. [97] to improve the resolution of evapotranspiration data from MODIS images from 1 km to 250 m and further to 30 m resolution. [PERSON] et al. [98] proposed the extension of the traditional cokriging method from the spatial domain to spatiotemporal domain capable to account for spatiotemporal structures of the input images. The method was tested for fusing MODIS NDVI images available at 250 m with ETM+ 30 m NDVI images. [PERSON] et al. [99] fused multi-temporal MODIS and Landsat data together with topographic information for a better estimation of biophysical parameters over complex terrain. Data were validated by making use of a ground-based continuous fraction of absorbed photosynthetically active radiation and leaf area index measurements.  
Several studies were dedicated to increasing the spatial resolution of soil moisture through disaggregation methods [100; 101]. [PERSON] et al. [102] disaggregated tropical Rainfall Measuring Mission dataset using digital elevation model data and SPOT satellite images The results were validated by using in-situ data from different local stations present in the study area, i.e., Qaidam basin. [PERSON] and [PERSON] [103] disaggregated the same data product on the basis on a rather limited number of rain gauge data sets. The authors generated an improved monthly pixel-based precipitation with a special resolution of 1 km.