HyperBlend leaf simulator : improvements on simulation speed, generalizability, and parameterization

Abstract

In recent decades, remote sensing of vegetation by hyperspectral imaging has been of great interest. An important part in interpreting the remotely sensed spectral data is played by simulators, which approximate the connection between plants’ biophysical and biochemical properties and detected spectral response. We introduce improvements and new features to recently published hyperspectral leaf model HyperBlend. We present two methods for increasing simulation speed of the model up to 200 times faster with slight decrease in simulation accuracy. We integrate the well-known PROSPECT leaf model into HyperBlend allowing us to use the PROSPECT parametrization for leaf simulation. For the first time, we show that HyperBlend generalizes well and can be used to accurately simulate a wide variety of plant leaf spectra. HyperBlend is available as an open-source Python project under MIT license in a GitHub repository available at: https://github.com/silmae/hyperblend.