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Terry, Neil; Day‐Lewis, Frederick D.; Lane, John W.; Johnson, Carole D.; Werkema, Dale
Vadose zone journal, March/April 2023, 2023-Mar, 2023-03-00, 20230301, 2023-03-01, Volume: 22, Issue: 2Journal Article
Geophysical methods can provide three‐dimensional (3D), spatially continuous estimates of soil moisture. However, point‐to‐point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one‐thousand 60/40 training/test cross‐validation folds produced root mean squared errors ranging from 0.025–0.046 cm3/cm3). This cross‐validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi‐automated model‐selection process that could be used for other sites and datasets to locally improve accuracy. Core Ideas Various geophysical methods (e.g., frequency domain electromagnetics FDEM, ground penetrating radar, electrical resistivity tomography ERT) are sensitive to soil moisture (volumetric water content VWC). Machine learning provides methods for data fusion and less need for assumptions/advanced data processing. Raw and processed geophysical data were evaluated in traditional and machine learning models to predict VWC. Random regression forest models using FDEM and ERT information gave the highest overall VWC accuracy. Models yielded good results even when trained on only half of a physically separated portion of the dataset.
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