# Spatiotemporal Image Fusion in Remote Sensing
## 2 Fusion Methods to Increase Spatiotemporal Resolution of Satellite Images  
Data fusion is a well-established research field [1; 2; 3; 4]. Image fusion methods are primarily used for improving the level of interpretability of the input data [17]. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series [18; 19; 20]. According to [PERSON] and [PERSON] [21] \"the main objective of data fusion is either to estimate the state of a target or object from multiple sensors, if it is not possible to carry out the estimate from one sensor or data type alone, or to improve the estimate of this target state by the exploitation of redundant and complementary information\". The authors included in their study 12 definitions of data fusion from different application domains including computer science, information theory, tracking or surveillance [21]. Image fusion can be performed at pixel-level, feature-level (e.g., land-cover classes of interest), and decision-level (e.g., purpose driven) [5; 11] by considering the following image blending scenarios: (1) combining high and low-spatial resolution images from the same satellite system, e.g., 15 m panchromatic images with 30 multispectral images from Landsat satellite, or from different satellite systems, e.g., SPOT 10 m panchromatic with Landsat multispectral images at 30 m spatial resolution [22]; (2) combining optical and microwave remote sensing images [23; 24; 25; 26; 27; 28; 29]; (3) combining multispectral satellite imagery and Light Detection and Ranging(LiDAR) data [30]; (4) combining multispectral satellite imagery and hyperspectral data [31], (5) combining high-resolution, low-frequency images with low resolution, high-frequency images [32] and (6) fusing microwave (passive) and microwave (active) sensors [33]. Long revisit time is not suitable for seasonal vegetation phenology monitoring or rapid surface changes. Therefore, we need high-resolution images in both time and space. Commercial satellites offer images at fine spatial scale and high temporal resolution. Among these sensors, we can mention the micro-satellites launched by Planet Labs [34] which acquire daily images of the Earth with a spatial resolution of about 3 m, or RapidEye sensors which acquire images with 5 m spatial resolution every day. Yet, these images are too costly for many applications in areas as diverse as agriculture mapping, timely monitoring of natural hazards or mining and illegal deforestation activities monitoring [34; 35]. During last years, there have been different free sensors acquiring images at an increased spatial (e.g., Sentinel-2) and temporal resolution (Sentinel-3). These images could serve as a solution to the above-mentioned challenge. In addition, spatiotemporal image fusion methods, called also spatiotemporal downscaling methods [36], represent an efficient solution to generate images at a high temporal resolution [37] for more detailed land cover mapping and monitoring applications [38] and to improve the resolution of historical satellite images. Since its launch in the early 1970s, and especially after allowing the public access to the enormous data archives [39], Landsat data products have been used in different climate, biodiversity, water or agriculture mapping studies. These data products have a great potential to accurately map land cover classes and to monitor land surface parameters. However, these data have a revisit time of only 16 days and therefore, their potential use for monitoring gradual changes such as changes in vegetation phenology or soils moisture, just to give a few examples, is rather reduced, especially in cloudy areas (e.g., tropical areas), where only a few cloud-free images per year are available. NASA's MODIS sensor, on the other hand, acquires data twice a day which makes them more suitable for various surface dynamics mapping.