Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance

Aarne Hovi*, Daniel Schraik, Nea Kuusinen, Tomáš Fabiánek, Jan Hanuš, Lucie Homolová, Jussi Juola, Petr Lukeš, Miina Rautiainen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Forest floor vegetation can account for a notable fraction of forest productivity and species diversity, and the composition of forest floor vegetation is an important indicator of site type. The signal from the forest floor influences the interpretation of optical remote sensing (RS) data. Retrieval of forest floor reflectance properties has commonly been investigated with multiangular RS data, which often have a coarse spatial resolution. We developed a method that utilizes a forest reflectance model based on photon recollision probability to retrieve forest floor reflectance from near-nadir data. The method was tested in boreal, hemiboreal, and temperate forests in Europe, with hemispherical photos and airborne LiDAR as alternative data sources to provide forest canopy structural information. These two data sources showed comparable performance, thus demonstrating the value of using airborne LiDAR as the structural reflectance model input to derive wall-to-wall maps of forest floor reflectance. We derived such maps from multispectral Sentinel-2 MSI and hyperspectral PRISMA satellite images for a boreal forest site. The validation against in situ measurements showed fairly good performance of the retrievals in sparse forests (that had effective plant area index less than 2). In dense forests, the retrievals were less accurate, due to the small contribution of forest floor to the RS signal. We also demonstrated the use of the method in monitoring the recovery of forest floor vegetation after a thinning disturbance. The reflectance model that we used is computationally efficient, making it well applicable also to data from new and forthcoming hyperspectral satellite missions.

Original languageEnglish
Article number113610
Number of pages15
JournalRemote Sensing of Environment
Publication statusPublished - 1 Aug 2023
MoE publication typeA1 Journal article-refereed


  • Airborne laser scanning
  • Hyperspectral
  • Radiative transfer
  • Reflectance modeling
  • Sentinel-2
  • Spectroscopy
  • Understory


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