End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission

Abstract

An end-to-end sensor simulation is a proper tool for the prediction of the sensor's performance over a range of conditions which cannot be easily measured. In this study (ESA-Contract: 19962/06/NL/EL) 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 (SNR) 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 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" domain are presented. The recommended optimum spectral design predominantly confirms the sensor parameters given by ESA. The system is capable of retrieving atmospheric and geo-biophysical 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.

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

“…The forward simulation of realistic EnMAP or Sentinel‐2 spectral images, in fact, requires addressing factors affecting all the digital numbers images. These factors include atmospheric parameters (e.g., cloud cover, water vapor, and aerosols), environmental conditions (e.g., vegetation and water bodies), radiometric correction (e.g., SNR), spectral correction (e.g., spectral response function), and spatial resolution (Segl et al, 2009, 2012a, 2012b). Because both EnMAP and Sentinel‐2 sensors are not yet in orbit, we used ideal cases of Scenarios II and III mimicking satellite remote sensors bands that have been produced in the laboratory.…”

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

“…We identified the most useful wavelengths for remote sensing applications mainly based on the studies of Segl et al [24] and Miglani et al [25]. Segl et al [24] discussed the relevance of the multispectral bands of S2, and Miglani et al [25] focused on the agricultural wavelengths.…”

“…Segl et al [24] discussed the relevance of the multispectral bands of S2, and Miglani et al [25] focused on the agricultural wavelengths. They were classified depending on their main application topic (vegetation, agriculture, soil, geology, water resources, disasters or land use).…”

“…The most useful wavelengths for remote sensing applications were mainly identified based on the publications of Thenkabail et al [16] and of Segl et al [24]. Segl et al [24] discussed the relevance of the S2 bands, while many others reviewed the use of hyperspectral data for agricultural and vegetation applications.…”

“…Segl et al [24] discussed the relevance of the S2 bands, while many others reviewed the use of hyperspectral data for agricultural and vegetation applications. Wavelengths were classified depending on their main applications (Figures 4 and 5).…”