# _remote sensing_
## 1 Introduction  
Passive methods for spaceborne Earth Observation (EO) used for the analysis of the landscape and environment towards the understanding of natural phenomena rely mostly on the usage of optical-based panchromatic, hyperspectral and multispectral sensors [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Panchromatic imaging systems use a single channel detector with a broad sensitivity window, encompassing the entire visible-light spectrum. Panchromatic images offer high-spatial resolution information suitable mainly for mapping purposes, without specific spectral information, with an achievable spatial resolution typically equal to four times the one of Visible and NIR (VNIR) bands [11, 12, 13, 14]. Hyperspectral imaging systems can acquire images in as many as 200 (or more) contiguous spectral bands across the VNIR and Short-Wave InfraRed (SWIR) parts of the spectrum [15], offering a higher level of spectral details and a consequent higher sensibility for detecting variations in the reflected radiant energy. The increasing number of narrower recording spectral bands represents also an improvement in spectral resolution [16], yet introducing a greater amount of generated data. The higher amount of acquired data can lead to on-board memory saturation, especially for multiangle acquisitions over the same area of interest (AOI) [2, 17] that can be only partially mitigated by on-board pre-processing techniques [18]. Furthermore, hyperspectral sensors have some issues in terms of qualityof gathered data. The narrow spectral band required for a higher spectral resolution introduces a decrease in the signal-to-noise ratio (SNR) [19], due to the lower amount of energy available in each spectral channel. Multispectral imaging systems are based on a multichannel detector with less than 10 spectral bands. These sensors usually perform between 3 and 10 different band measurements in each pixel of the multilayer images they produce [20]. This typology of systems was introduced with the NASA's Landsat program, started in 1972 [21; 22]. Landsat 1 acquired until 1978 EO data in four spectral bands (green, red, and two infrared) in medium spatial resolution, implemented to study Earth's landmasses [22]. Multispectral satellite data with medium spatial resolution represent a valuable source of recent and historical land cover, ocean and atmosphere information [23], whose implementation has been carried out by different governmental programmes, such as the NASA's Earth Observing System (EOS) [24] or the European Union's Copernicus [25]. Depending on the required application and aim, Earth Observation missions can be characterized by different spatial resolutions of the spaceborne sensors. While high-resolution data (pixels of less than 5 m) are ideally suited for addressing small holder agriculture problems, accurate surface mapping, 3D city models and other applications where a finer detail shall be detected within the AOI and among groups of adjacent pixels [14], medium and low resolution sensors are applied to monitor the environment and global changes in natural resources and agriculture. These sensors typically have large swath widths and provide global coverage on a daily basis. For example, this is the case of Moderate Resolution Imaging Spectroradiometer (MODIS, [26; 27]) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, [28; 29]), instruments on-board the AQUA and TERRA NASA's missions, of Landsat 1, 2, 5 and IRS-1/LISS. Moreover, medium and low resolution sensors find a favorable application also on small satellite platforms and nano-satellite missions. The miniaturization of the required technology achieved recently allows the integration of a multiplicity of sensors, with the possibility to combine different view angles, to be installed on the same satellite platform for multiangle observations.