{'fraction_non_alphanumeric': 0.024350649350649352, 'fraction_numerical': 0.003246753246753247, 'mean_word_length': 5.603571428571429, '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}}
Estimation of Radiative Transfer Parameters from L‐Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been designed to extend our knowledge of the Earth's water cycle. Soil Moisture and Ocean Salinity records brightness temperatures at the L‐band, which over land are sensitive to soil and vegetation parameters. On the basis of these measurements, soil moisture and vegetation opacity data sets have been derived operationally since 2009 for applications comprising hydrology, numerical weather prediction (NWP), and drought monitoring. We present a method to enhance the knowledge about the temporal evolution of radiative transfer parameters. The radiative transfer model L‐Band Microwave Emission of the Biosphere (L‐MEB) is used within a data assimilation framework to retrieve vegetation opacity and soil surface roughness. To analyze the ability of the data assimilation approach to track the temporal evolution of these parameters, scenario analyses were performed with increasing complexity. First, the HYDRUS‐1D code was used to generate soil moisture and soil temperature time series. On the basis of these data, the L‐MEB forward model was run to simulate brightness temperature observations. Finally, the coupled model system HYDRUS‐1D and L‐MEB were integrated into a data assimilation framework using a particle filter, which is able to update L‐MEB states as well as L‐MEB parameters. Time invariant and time variable radiative transfer parameters were estimated. Moreover, it was possible to estimate a “bias” term when model simulations show a systematic difference as compared to observations. An application to a USDA‐NRCS Soil Climate Analysis Network (SCAN) site showed the good performance of the proposed approach under real conditions.