Hyperspectral Remote Sensing from Spaceborne and Low Altitude Aerial/Drone-Based Platforms — Differences in Approaches, Data Processing Methods, and Applications

Dear Colleagues,

In the last two decades, several important space-borne hyperspectral sensors have been launched by different space agencies. However, since the time of Hyperion (in 1999) to the latest launch of the Hyperspectral Imager Suite (HISUI) (in December 2019), no hyperspectral sensors have had global coverage. Despite this, these sensors have made significant use of hyperspectral data and also led to innovative approaches to data processing (from noise removal to spectral mapping). Previous studies have highlighted the limitations of these space-borne sensors in identifying a pure target and also in identifying spectral targets with subdued spectral signatures as these hyperspectral sensors had coarse spatial resolution (in general 20 meters to 30 meters) and poor signal to noise ratio (e.g., Hyperion has poor SNR in the shortwave electromagnetic domain). However, these spaceborne sensors have had encouraging results in environmental monitoring, for example, in improved forest cover classification, detection of phonological changes in forest, land use/land cover mapping, agriculture land cover characterization, crop stress estimation, mapping of rock types, minerals, etc. Due to the lack of global coverage of space-borne hyperspectral sensors; routine aircraft-based and drone-based hyperspectral surveys are carried out in different countries using different advanced hyperspectral sensors like advanced visible infrared spectrometer (AVIRIS) and its latest version AVIRIS-next generation (AVIRIS-NG); HyMap, DAIS, etc. These sensors, capable of collecting high spatial and spectral resolution data with optimum spectral fidelity, have led to new applications, such as soil geochemistry, water quality, forest species mapping, agricultural stress, and exploration scale mineral alteration mapping, etc. These applications have not been explored using hyperspectral data from spaceborne platforms. Machine or artificial intelligence can be used to understand and utilize the higher-order variation of field grade spectral data collected using these low-altitude airborne sensors to automate spectral feature-based target detection. It is now important to capitalize on the comparative the potential of spaceborne and airborne hyperspectral remote sensing datasets based on analyzing different applications that have been addressed by hyperspectral data from different platforms to identify the specificity of each of these two platforms.

Dr. Amin Beiranvand Pour
Dr. Arindam Guha
Prof. Dr. Laura Crispini
Dr. Snehamoy Chatterjee
Guest Editors

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