Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms
Cite this dataset
Bean, Eban Z.; Huffaker, Ray G.; Migliaccio, Kati W. (2018). Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms [Dataset]. Dryad. https://doi.org/10.5061/dryad.f5220r3
Vadose zone measurements of volumetric soil water content (θ) using soil moisture sensors (SMSs) have become more common due to advances in technology and reduction of costs. Soil moisture sensor data exhibit a characteristic cyclical pattern reflecting water flux dynamics into and out of the observed soil volume. Expert review of SMS datasets to distinguish valid from corrupt or incomplete soil water cycles is arguably the most precise method for determining field capacity (θFC) but is impractically cumbersome and time consuming for increasingly large SMS datasets. We evaluated competing approaches for automated soil water cycles analysis that use widely available R packages based on pattern recognition and machine learning (findpeaks [R-FP], symbolic aggregate approximation [R-SAX], and density histogram [R-DH]), and a MATLAB code based on soil water dynamic principles (SWDP). These approaches were applied to three SMS datasets. Our empirical results showed superiority of R-SAX for identifying valid soil water cycles, probably due to benefiting from training sets to calibrate to correct cycles. Two other approaches (SWDP and R-FP) provided similar results without need of training sets or preprocessing data. Three approaches for estimating field capacity were applied to valid cycles, R-FP, regression of exponential decay (SWDP-R), and estimated “knee” of curve (SWDP-K). Each performed similarly to the expert defined values, with R-FP and SWDP-R generally performing best across analyses. Results of this study also show temporal dynamics of θFC within datasets used here. There is potential for optimizing θFC and a need for automated, objective analysis to leverage dynamics in irrigation management and modeling.