Upscaling soil moisture from point scale to field scale: Toward a general model

Abstract

Abstract Field‐scale soil moisture measurements are valuable but rarely available because the resolution of most satellite soil moisture products is too coarse, while most in situ sensors provide only point‐scale data. Previous upscaling approaches for such data are mostly site‐specific, and none are suitable to upscale data from the thousands of stations in existing monitoring networks. To help fill this gap, this research aims to develop a more broadly applicable upscaling approach using data from the Marena, Oklahoma, In Situ Sensor Testbed and a cosmic‐ray neutron rover. Rover survey data were used to measure average soil moisture for the ∼64‐ha field on 12 dates in 2019–2020. Statistical modeling was used to identify the soil, terrain, and vegetation properties influencing the relationships between the field‐scale rover data and point‐scale in situ data from six monitoring sites. Site‐specific linear upscaling models estimated the field average soil moisture with root mean squared error (RMSE) values ranging from 0.007 to 0.017 cm3 cm−3, but such models are not transferrable between sites. To create a more general model, Least Absolute Shrinkage and Selection Operator regression was used with a leave‐one‐out cross‐validation approach to identify the key predictors for upscaling. The resulting parsimonious model required only the point‐scale observations and sand content data and achieved RMSE values ranging from 0.006 to 0.031 cm3 cm−3 for the six monitoring sites. The texture‐based model demonstrated reasonable accuracy and is a promising step toward a general model that could be broadly applied for upscaling point‐scale in situ monitoring stations.