2023

Ihalainen, Olli; Juola, Jussi; Mõttus, Matti

<p>Vegetation biophysical- and chemical traits, defined on the basis of leaf area, can be retrieved from their spectral reflectance. Ultra high resolution hyperspectral images, such as ones collected from drones, allows measuring the spectra of individual leaves. The reflectance signal of such data is calibrated with respect to the top-of-canopy (TOC) irradiance, as the local illumination conditions on leaf surfaces are largely unknown and can vary significantly from the TOC conditions. We developed an inversion algorithm that uses the PROSPECT leaf radiative transfer model and the theory of spectral invariants to retrieve the actual leaf reflectance from TOC-calibrated hyperspectral images. Compared with more traditional canopy reflectance models, this approach accounts for the spatial variation in leaf-level irradiance visible in sub-centimeter-resolution images and is computationally more efficient. We used simulated and measured leaf and canopy reflectance data to validate the approach and found the retrieved leaf reflectances to match closely the actual reflectances (relative RMSD was 12% for simulated data on the average and below 10% for measured data). The proposed method provides an efficient approach for illumination correction, enabling reliable, physically based applications for monitoring vegetation biochemical and biophysical properties from ultra-high-resolution spectral imagery.</p>