# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 4 Data Fusion Techniques Applied to Remote Sensing Data  
The heterogeneity of the study cases presented in the previous chapter highlighted the advantage of using remote sensing for investigating various environments and aspects of the Earth surface by means of a number of different sensors, each one gathering different intervals of the electromagnetic solar flux reflected back by the surface, and varying in terms of spatial and spectral resolutions [284; 285]. Optical sensors in the VIS to NIR spectral range may better serve for mapping horizontal patterns, and SAR systems allow to characterize the targets in all weather conditions, whereas LiDAR technology provides for accurate vertical profile measurements (e.g., [284]). At the same time, the revisiting capability of the satellites allowed to include the temporal information to a given 2D scene [286]. This additional information provided by space-born sensors enables to monitor the evolution of a certain process or of a natural disaster, and/or the variation over time of a certain area [287]. One explicative example is given by the case of _P. Oceanica_ (chapter SS3.4), whose mapping at various times allows to appraise the eventual active degradation processes, and to establish mitigation plans. The growth in the number and variety of both space platforms and remote sensing sensors has opened the path to new techniques of data analysis, which can provide more reliable, coherent, and comprehensive information about the observed scene [286; 288]. The data fusion approach thus relies on coupling observations acquired by different sources, whose joint analysis can favor a better interpretation of a certain process or of the observed scene, because the resulting fused data encompass more detailed information than each single acquisition, merging together all the meaningful information acquired by the different sensors (e.g., [284; 289; 290; 291; 292]). Data fusion techniques enable for the monitoring of fast-evolving environmental processes, e.g., highly dynamic natural processes such as polynynas [132], climate changes [293], desertification processes [294], deforestation [295], coastal erosion [296], and the phases of urban development [297], as well as object recognition and tracking or classification, and change detection [288]. Data fusion has been firstly adopted to blend the high spatial resolution of PAN with the high spectral resolution of MS acquisitions [287]. The accessibility to both the panchromatic and usually four multispectral bands will enable the application of pan-sharpening techniques, and the consequent availability of multispectral data at the same geometric resolution as the panchromatic. Indeed, due to their constructional constraints, MS sensors have a very narrow spectral bandwidth, which in turn requires a larger instantaneous field of view (IFOV), and are thus characterized by a lower spatial resolution than PAN sensors [284; 285]. On the contrary, the smaller IFOV of PAN sensors allows to achieve better spatial resolutions, but their broader bandwidths do not allow to reach high spectral resolutions. Such a distinctiveness in the bandwidth properties of PAN and MS images means that a single sensor is unable to simultaneously have both high spatial and spectral resolution [284; 292]. Nevertheless, both PAN and MS sensors simultaneously acquire the same ground scene, with the same acquisition mode too, facilitating the synthesis of the PAN high spatial resolution with the MS high spectral resolution properties [284]. This is the so-called pan-sharpening technique, which stands for its key role for producing high-resolution multispectral images (e.g., [284; 292; 298; 299; 300]), opening to the possibility of mapping the territory at the highest resolution and of having color ortho-images comparable to those obtainable from aircrafts, which becomes particularly useful for land cover classification, visual interpretation, and target recognition [284; 286].