Modeling Transient Root-zone Soil Moisture Dichotomies in Landscapes with Intermixed Land Covers

Although large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, deficiencies of many existing networks include the small spatial support of each station, the low spatial density of stations, and the almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations may not adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. The objectives of this study were i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as a transfer function; and iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 78 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 78 locations using a soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only seven inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand, silt, and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9x9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSE = 24 mm, using observed PAW under grassland as an input. This promising new approach for estimating soil moisture under adjacent, contrasting land covers at a relatively low computational cost may significantly enhance the scientific and societal value of existing large-scale monitoring networks.