Comparison of simulated hyperspectral HyspIRI and multispectral Landsat 8 and Sentinel-2 imagery for multi-seasonal, regional land-cover mapping

Abstract This study used simulated hyperspectral (HyspIRI) and multispectral (Landsat 8 OLI, Sentinel-2 MSI) satellite imagery to compare regional land-cover mapping capabilities (San Francisco Bay Area, California) within an analytical framework that included consistent reference data and classification rules. Imagery had the pixel resolution (30 m) and extent (30,000 km 2 ) of a Landsat scene, with multi-seasonal (spring, summer, fall) acquisitions from year 2013. Primary study objectives were to assess differences in map accuracy related to Multiple Endmember Spectral Mixture Analysis (MESMA) and Random Forests (RF) classifiers, spectral resolution (hyperspectral vs. multispectral), and temporal resolution (multi-seasonal vs. summer). The RF classifier generally outperformed MESMA by 1.1 to 9.0% overall accuracy, with the exception of summer HyspIRI reflectance data. There were no clear patterns in accuracy when comparing HyspIRI and simulated multispectral reflectance bands with RF and MESMA classifiers. With summer data, HyspIRI had significantly higher accuracy for MESMA (+ 5.5 to + 8.7%) and significantly lower accuracy for RF (− 9.7 to − 16.4%). There were no significant differences in accuracy when using multi-seasonal HyspIRI and multispectral data with RF or MESMA (

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