Testing of observation operators designed to estimate profile soil moisture from surface measurements

The accurate estimation of profile soil moisture is usually difficult due to the associated costs, strong spatiotemporal variability, and nonlinear relationship between surface and profile moisture. Here, we used data sets from the Soil and Climate Analysis Network to test how reliably observation operators developed based on the cumulative distribution function matching method can predict profile soil moisture from surface measurements. We first analysed how temporal resolution (hourly, daily, and weekly) and data length (half year, 1 year, 2 years, and 4 years) affected the performance of observation operators. The results showed that temporal resolution had a negligible influence on the performance of observation operators. However, a leave-one-year-out cross-validation showed that data length affected the performance of observation operators; a 2-year interval was identified as the most suitable duration due to its low uncertainty in prediction accuracy. The robustness of the observation operators was then tested in three primary climates (humid continental, humid subtropical, and semiarid) of the continental United States, with the exponential filter employed as an independent method. The results indicated that observation operators reliably predicted profile soil moisture for most of the tested stations and performed almost equally well as the exponential filter method. The presented results verified the feasibility of using the cumulative distribution function matching method to predict profile soil moisture from surface measurements.