{'fraction_non_alphanumeric': 0.017148014440433214, 'fraction_numerical': 0.0036101083032490976, 'mean_word_length': 5.721212121212122, '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}}
Assessment of the MODIS LAI and FPAR algorithm: Retrieval quality, theoretical basis and validation

Green leaf area index (LAI) measures the amount of foliage in a vegetation canopy and strongly influences many ecological processes, including the fraction of photosynthetically active radiation (FPAR) absorbed by the canopy. Since these two variables are required in most modeling studies of vegetation and climate, they are operationally derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA TERRA spacecraft. The objectives of this research are to evaluate the MODIS LAI and FPAR algorithm in terms of retrieval quality, theoretical basis and validation. Investigation of the performance of the algorithm as a function of uncertainties in input spectral reflectances indicates that knowledge of these uncertainties is critical for the retrieval of biophysical parameters of highest possible quality. Neglecting this information can cause destabilization of the retrieval process, while its use can increase the number of high quality retrievals by 10-20%.