Towards robust forest leaf area index assessment using an imaging spectroscopy simulation approach

Few studies have evaluated how per-pixel structural configurations could impact spectral response. This has an impact on how we assess especially large area/global ecosystems. In an effort to understand this impact of sub-pixel structural variation on large-footprint imaging spectroscopy, a simulation approach was used, which provides precise knowledge of target geometry and radiometry. We demonstrated the validity of the proposed simulation in terms of one such structural metric of interest, namely leaf area index (LAI). LAI is a key vegetation structural parameter, which has implications for predicting ecosystems' foliar spatial distribution, health, photosynthesis, transpiration, and energy transfer. Simulated LAI measurements were validated with field data obtained from AccuPAR measurements (R2 = 0.76) and by comparison to NDVI data obtained from simulated AVIRIS imagery (R2 = 0.92−0.65, depending on sampling interval). These data were used to propose an appropriate sampling protocol for LAI data collection, thus providing for efficient data collection, while minimizing variability of individual measurements. These efforts will support preparatory science experiments towards understanding the phenomenology of NASA's next-generation imaging spectrometer, HyspIRI.