Abstract
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°–62°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.
Highlights
Multi- and hyperspectral remote sensing provide a means of spatially and temporally continuous monitoring of forest extent, change, health, phenology, energy exchange, photosynthesis, and biodiversity (Ryu et al, 2019; Schneider et al, 2017; Wulder et al, 2019)
We evaluated the new parameterization based on inversion of the model, which resulted in average root mean square error (RMSE) of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests
We present the results separately for coniferous, broadleaved, and mixed forest plots (n = 6), because coniferous and broadleaved forests showed distinct spectral characteristics
Summary
Multi- and hyperspectral remote sensing provide a means of spatially and temporally continuous monitoring of forest extent, change, health, phenology, energy exchange, photosynthesis, and biodiversity (Ryu et al, 2019; Schneider et al, 2017; Wulder et al, 2019). A persisting problem, is the lack of empirical data for constructing models which are needed to interpret the remote sensing signals. Physically-based forest reflectance models allow to estimate forest variables from spectral observations while relying on a limited amount of empirical data (e.g., Rautiainen, 2005; Schraik et al, 2019). The models should be simple, yet realistic enough to be practically useful in interpreting remote sensing signals and extracting the forest parameters of interest (Woodcock et al, 1994, 1997). The spectral invariant and photon recollision proba bility theories offer a useful framework for designing simple and realistic forest reflectance models which account for multiple scattering
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