{'fraction_non_alphanumeric': 0.02008269344359126, 'fraction_numerical': 0.01004134672179563, 'mean_word_length': 5.093525179856115, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comparison of leaf area index derived by statistical relationships and inverse radiation transport modeling using RapidEye data in the European alpine upland

Leaf Area Index (LAI) is a relevant input parameter for flux modeling of energy and matter in the biosphere. However, in a landscape such as the European alpine upland with small-scale land use patterns and high vegetation heterogeneity, existing global products are less suited and a high spatial resolution is required. Within this study two methods are compared to derive the LAI for grassland in the prealpine River Ammer catchment from high spatial resolution RapidEye data: the empirical approach based on regression functions, and the physical approach of inverted radiation transfer modeling (RTM). Established vegetation indices (VIs) as well as new ones incorporating RapidEye’s red edge band are calculated for four dates of the vegetation period 2011 and correlated with in situ LAI data. The statistical regressions between VIs and LAI of the different time steps show high correlations (R2 of 0.57 up to 0.85). However, the established regressions are scene specific and the method requires excessive field work. In the physical approach the RapidEye reflectances are used as input data to an inverted RTM (PROSAIL), which is parameterized with leaf and canopy properties collected in the field. The LAI derived by the RTM have a RMSE between 2.02 and 2.28 for the different dates. Both methods capture the general LAI pattern. However, due to the broad parameterization of the RTM used to cover the heterogeneous grassland conditions, resulting LAI values are generally higher than the statistically derived LAI values.