Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.

Department of Geography Faculty of Science Masaryk University Kotlářská 2 611 37 Brno Czech Republic

Global Change Research Institute of the Czech Academy of Sciences Bělidla 986 4a 603 00 Brno Czech Republic

Institute of Computer Science Masaryk University Šumavská 416 15 602 00 Brno Czech Republic

Zobrazit více v PubMed

Homolová L., Janoutová R., Malenovský Z. Evaluation of various spectral inputs for estimation of forest biochemical and structural properties from airborne imaging spectroscopy data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016;XLI-B7:961–966. doi: 10.5194/isprs-archives-XLI-B7-961-2016. DOI

Gastellu-Etchegorry J.-P., Lauret N., Yin T., Landier L., Kallel A., Malenovský Z., Bitar A.A., Aval J., Benhmida S., Qi J., Medjdoub G., Guilleux J., Chavanon E., Cook B., Morton D., Chrysoulakis N., Mitraka Z. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017;10:2640–2649. doi: 10.1109/JSTARS.2017.2685528. DOI

Malenovský Z., Regaieg O., Yin T., Lauret N., Guilleux J., Chavanon E., Duran N., Janoutová R., Delavois A., Meynier J., Medjdoub G., Yang P., van der Tol C., Morton D., Cook B.D., Gastellu-Etchegorry J.-P. Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: structural impacts in geometrically explicit vegetation canopies. Remote Sens. Environ. 2021;263 doi: 10.1016/j.rse.2021.112564. DOI

Wang Y., Kallel A., Yang X., Regaieg O., Lauret N., Guilleux J., Chavanon E., Gastellu-Etchegorry J.-P. DART-Lux: an unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images. Remote Sens. Environ. 2022;274 doi: 10.1016/j.rse.2022.112973. DOI

Malenovský Z., Lamsal K., Janoutová R., Devereux T., Woodgate W., Hambrecht L., Cimoli E., Lucieer A., Homolová L., Reagieg O., Wang Y., Gastellu-Etchegorry J.P. 3D radiative transfer modelling of forest canopies reconstructed from terrestrial laser scanning: a case of tall australian eucalypts, in: IGARSS 2023 - 2023. IEEE Int. Geosci. Remote Sens. Symp. 2023:2683–2686. doi: 10.1109/IGARSS52108.2023.10281746. DOI

Kholiavchuk D., Gurgiser W., Mayr S. Carpathian forests: past and recent developments. Forests. 2024;15:65. doi: 10.3390/f15010065. DOI

Švik M., Lukeš P., Lhotakova Z., Neuwirthová E., Albrechtová J., Campbell P., Homolová L. Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods. Int. J. Remote Sens. 2023;44:3083–3105. doi: 10.1080/01431161.2023.2216847. DOI

Janoutová R., Homolová L., Malenovský Z., Hanuš J., Lauret N., Gastellu-Etchegorry J.-P. Influence of 3D spruce tree representation on accuracy of airborne and satellite forest reflectance simulated in DART. Forests. 2019;10:292. doi: 10.3390/f10030292. DOI

Janoutová R., Homolová L., Novotný J., Navrátilová B., Pikl M., Malenovský Z. Detailed reconstruction of trees from terrestrial laser scans for remote sensing and radiative transfer modelling applications. Silico Plants. 2021;3:diab026. doi: 10.1093/insilicoplants/diab026. DOI

Konôpka B., Pajtík J., Marušák R., Bošeľa M., Lukac M. Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Picea abies Karst. For. Ecol. Manag. 2016;364:52–59. doi: 10.1016/j.foreco.2015.12.005. DOI

Chen J.M., Mo G., Pisek J., Liu J., Deng F., Ishizawa M., Chan D. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycles. 2012;26 doi: 10.1029/2010GB003996. DOI

J.-P. Gastellu-Etchegorry, DART user's manual (5.10.0), (2023).

Féret J.-B., Berger K., de Boissieu F., Malenovský Z. PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sens. Environ. 2021;252 doi: 10.1016/j.rse.2020.112173. DOI

The MathWorks Inc., MATLAB, (2020) . https://www.mathworks.com.

Python Software Foundation, Python, Version 3.6, Python Software Foundation, 2016. Available at: https://www.python.org/.