{'fraction_non_alphanumeric': 0.01715137956748695, 'fraction_numerical': 0.006711409395973154, 'mean_word_length': 5.483091787439614, '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 three methods of interpolating soil moisture in Oklahoma

Interpolating soil moisture at the regional scale (104–107 km2) is challenging because in situ observations tend to be sparse, and hydrometeorological conditions and soil characteristics have significant spatial variability. This study is the first to apply the reduced optimal interpolation (ROI) method to soil moisture. The ROI method uses both in situ and variable infiltration capacity (VIC) simulated‐soil moisture to generate interpolated soil moisture surfaces in Oklahoma. The ROI method is based on an empirical orthogonal function (EOF) analysis that identifies the leading spatial modes of soil moisture variation based on the model simulation and then applies these EOFs using the temporal variations in the observed soil moisture. The interpolated soil moisture surfaces generated using ROI are compared with Cokriging, which also uses VIC simulations as a secondary input, and the inverse distance weighting (IDW) approach. The accuracy of all three interpolation methods are evaluated using soil moisture measurements from 65 stations. The ROI method is significantly more accurate than IDW and it also outperforms Cokriging. We demonstrate that ROI can be used to accurately depict the spatial patterns of soil moisture at the local and regional level.