# Spatiotemporal Image Fusion in Remote Sensing
## 1 Introduction  
Image fusion is a well-established research field [1; 2; 3; 4; 5] with important developments during the last years. The reason for these developments is the increasing demand for satellite images with higher spatial, temporal and/or spectral resolution. In the past, image fusion methods were dedicated to enhancing spatial resolution [6] and to combining multimodal input images. Recently, the focus of these methods has changed to fusing fine spatial resolution images with high temporal frequency images [7]. The last decades have witnessed the emergence of various satellite-borne sensors. The NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra (operating since 1999) and Aqua satellites (operating since 2002), for example, collects data covering an area of 2330 km at different spatial (250 m, 500 m, 1 km) and temporal resolution. The Enhanced Thematic Mapper (ETM+) (launched on 5 October 1993) or Operational Land Imager (OLI) (launched in 2013) sensors onboard Landsat satellites collect 15 m panchromatic and 30 m multi-spectral bands in a 185 km wide swath, with a revisit time of 16 days [8]. MultiSpectral Instrument (MSI) onboard Sentinel-2 measures the Earth's reflected radiance with a high revisit time, i.e., 5 days since the launch of Sentinel-2B on 7 March 2017, and a high spatial resolution (four bands at 10 m, six bands at 20 m and three bands at 60 m spatial resolution). The Sentinel-3 Ocean and Land Color Imager (OLCI) sensor (launched in 2015) delivers currently images at a spatial resolution of 300 m and a temporal resolution of 2.8 days, which will be increased once Sentinel-3B is launched. The micro-satellites launched by Planet acquire images daily and at a spatial resolution of 3.125 m. Due to these technical advances, the remote sensing community now has access to both dense time-series data and high spatial, spectral resolution images. Yet, there are studies which require access to fine temporal and spatial resolution images collected in the past. These data are vital for a wide range of applications, including urban expansion and deforestation monitoring, crop mapping and yield estimation, inundation and wetland mapping [9] or quantifying the magnitude and climate change type [10]. Therefore, advanced methods to fuse high-temporal frequency and fine spatial-resolution sensors are required [11; 12]. Several papers have reviewed available methods developed to increase the spatial and temporal resolution of remote sensing [13; 14]. [PERSON] et al. [1] summarized remote sensing image fusion solutions with a focus on pansharpening used to enhance spatial resolution. [PERSON] et al. [15] focused in their review paper on the methods dedicated to disaggregating land surface temperature, while [PERSON] et al. [16] discussed in details the taxonomy, concepts, and principles of spatiotemporal reflectance fusion methods. The overall goal of our paper is to review existing methods dedicated to enhancing the spatiotemporal resolution of satellite images with a focus on the ability of the described methods to account for gradual changes, such as changes in vegetation phenology occurring during the period of fused images. We consider this review timely, given the importance of high temporal and spatial resolution images for different applications dedicated to surface dynamics mapping at seasonal and annual temporal scales, and the diversity of sensors which acquire images at different spatiotemporal resolutions. Note that we did not review the applications where the reviewed spatiotemporal image fusion methods were applied. Interested readers can find more information on this topic in the recent review paper by [PERSON] et al. [16]. Does the remote sensing community still need spatiotemporal data fusion methods nowadays, when the industry is constantly developing sensors capable of delivering sub-meters images on a daily basis?