Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data

Spaceborne imaging spectrometers are expected to facilitate regional-scale vegetation analyses with multi-season hyperspectral imagery. However, we still lack a better understanding on both whether approaches favorable over single-season approaches, as well the benefits of compared multispectral data. Our study investigates potential unmixing simulated Environmental Mapping and Analysis Program (EnMAP) data for class fraction mapping across diverse natural semi-natural ecoregions in California, USA. We utilized spring, summer fall 2013 EnMAP imagery derived from Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) covering sites San Francisco Bay Area, Lake Tahoe Santa Barbara. Regression-based synthetic training datasets spectral libraries was implemented needleleaf tree, broadleaf shrub, herbaceous non-vegetation fractions, independent reference used validation. Multi-season had average Mean Absolute Errors (MAE) all classes 8.7% 8.5% 9.6% larger errors low high end range remained, particularly open-canopy woodlands xeric shrub-dominated regions. Single-season revealed large seasonal regional variations within individual classes. In most cases, best performing similar unmixing, i.e., ?MAEs ±1.0%. This points advantage integration strategy more robust generalized season site. Relative analyses, Landsat composites same seasons yielded increases MAEs +1.7%, +2.3% +1.4% three sites. indicates that higher resolution provides relevant discriminative information when comparing contemporary image pairs. Unmixing spectral-temporal metrics (STMs) available images an entire year took full temporal detail provided by these ongoing missions. found STMs effectively map 9.9%, 10.0% 9.7% Still, improvements fractions woody through point benefit data, assume comparable satellites will further positively influence results. Overall, conclude spaceborne spectroscopy holds great advancing ecosystems.