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

Authors: BROWN, W. - Oklahoma State University; OCHSNER, T. - Oklahoma State University; Cosh, Michael; DONG, G. - South Dakota School Of Mines And Technology

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/9/2023
Publication Date: 2/9/2023
Citation: Brown, W.G., Ochsner, T., Cosh, M.H., Dong, G. 2023. Upscaling soil moisture from point scale to field scale: Toward a general model. Vadose Zone Journal. 22(2). Article e20244. https://doi.org/10.1002/vzj2.20244.
DOI: https://doi.org/10.1002/vzj2.20244

Interpretive Summary: Soil moisture monitoring is accomplished by a variety of technologies. The majority of in situ sensors are valid for a small scale, but cost effective. Cosmic ray probes have a wide footprint and are valuable for management scales, but expensive. One solution to make common in situ sensor installations more representative of the large scales, like the cosmic ray technologies, is to develop scaling equations using a mobile form of the cosmic ray sensor. An experiment was conducted near Stillwater Oklahoma to begin the development of a scaling methodology using a mobile sensor. Estimation errors were very small and proved the concept for such a scaling equation. This research will be of value to natural resource managers, modelers, and remote sensors, who are hoping to calibrate and validate products.

Technical Abstract: Soil moisture measurements at the field-scale are valuable but rarely available because the resolution of most satellite-based soil moisture products is too coarse, while most in situ sensors provide only point-scale data. To help fill this gap, this research attempts to develop a broadly applicable upscaling approach for observations from in situ soil moisture sensors using data from the Marena, Oklahoma, In Situ Sensor Testbed (MOISST) and a cosmic-ray neutron rover. Cosmic-ray neutron rover survey data were used to measure average soil moisture for the ~64 ha site on 12 dates in 2019-2020. The relationships between the point-scale in situ data and the field-scale rover data were examined using data from six in situ stations. Statistical modeling was used to identify the soil, terrain, and vegetation properties that influence these relationships. Site-specific linear upscaling models estimated the field average soil moisture with root mean squared error (RMSE) values ranging from 0.014 – 0.022 cm3 cm-3, though these particular models are not transferable to other sites. A general upscaling model using soil texture data was developed and achieved RMSE values ranging from 0.017 – 0.038 cm3 cm-3 for four calibration sites and values ranging from 0.015 – 0.021 cm3 cm-3 for two validation sites. The general upscaling model demonstrated accuracy better than the commonly used threshold for satellite and model validation of 0.04 cm3 cm-3 and should be further tested across diverse landscapes to evaluate its suitability as a broadly applicable upscaling approach for point-scale in situ monitoring stations