An end-to-end sensor simulation is a proper tool for the prediction of the sensor’s performance over a range of conditions that cannot be easily measured. In this study, such a tool has been developed that enables the assessment of the optimum spectral resolution configuration of a sensor based on key applications. It employs the spectral molecular absorption and scattering properties of materials that are used for the identification and determination of the abundances of surface and atmospheric constituents and their interdependence on spatial resolution and signal-to-noise ratio as a basis for the detailed design and consolidation of spectral bands for the future Sentinel-2 sensor. The developed tools allow the computation of synthetic Sentinel-2 spectra that form the frame for the subsequent twofold analysis of bands in the atmospheric absorption and window regions. One part of the study comprises the assessment of optimal spatial and spectral resolution configurations for those bands used for atmospheric correction, optimized with regard to the retrieval of aerosols, water vapor, and the detection of cirrus clouds. The second part of the study presents the optimization of thematic bands, mainly driven by the spectral characteristics of vegetation constituents and minerals. The investigation is performed for different wavelength ranges because most remote sensing applications require the use of specific band combinations rather than single bands. The results from the important “red-edge” and the “short-wave infrared” domains are presented. The recommended optimum spectral design predominantly confirms the sensor parameters given by the European Space Agency. The system is capable of retrieving atmospheric and geobiophysical parameters with enhanced quality compared to existing multispectral sensors. Minor spectral changes of single bands are discussed in the context of typical remote sensing applications, supplemented by the recommendation of a few new bands for the next generation of optical Sentinel sensors.
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