Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests

0555551 - ÚVGZ 2023 RIV US eng J - Journal Article
Hovi, A. - Schraik, D. - Hanuš, Jan - Homolová, Lucie - Juola, J. - Lang, M. - Lukeš, Petr - Pisek, J. - Rautiainen, M.
Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests.
Remote Sensing of Environment. Roč. 269, FEB (2022), č. článku 112804. ISSN 0034-4257. E-ISSN 1879-0704
R&D Projects: GA MŠMT(CZ) LM2018123; GA MŠMT(CZ) LTAUSA18154
Institutional support: RVO:86652079
Keywords : leaf-area index * spectral invariants * global products * solar-radiation * canopy * algorithm * fraction * modis * fpar * lai * Spectral invariants * Radiative transfer * Scattering * Hyperspectral * Spectra * Forest * Leaf area index * Coniferous * Broadleaved
OECD category: Meteorology and atmospheric sciences
Impact factor: 13.5, year: 2022
Method of publishing: Open access
https://www.sciencedirect.com/science/article/pii/S0034425721005241

We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48 degrees62 degrees N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450-2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.
Permanent Link: http://hdl.handle.net/11104/0330441