Proximal soil sensing for estimation of site-specific soil hydraulic characteristics

Matteo Poggio

Heterogeneous agricultural fields must be partitioned into zones with similar soil properties for effective site-specific management. However, it is expensive and time consuming to generate high-resolution soil maps using standard soil sampling and analysis procedures. Using proximal soil sensing techniques to costly and time effectively collect spatially dense soil data in situ would enhance the application of site-specific management. The goal of this study was to design and test a visible near infrared (VisNIR) penetrometer capable of simultaneously collecting in situ soil spectra and insertion force. Our design allows the use of field-deployable spectrometers that employ signal delivery via fiber optics in combination with a hydraulic push-type soil coring rig. We first compared the quality of VisNIR spectra collected using the penetrometer foreoptic with the Analytical Spectral Device (ASD Inc., Boulder, CO) contact probe foreoptic in a laboratory setting. The reflectance spectra obtained from these two foreoptics were correlated (r > 0.95 for most wavelengths) and our VisNIR penetrometer predicting model for soil organic carbon yielded similar results to the ASD contact probe (prediction error = 0.18 and 0.19 log10[g kg-1], respectively). The next step was to evaluate the in situ VisNIR penetrometer performances to estimate clay content to a depth of 80 cm at ten sites throughout the US Pacific Northwest and Palouse region. We collected intact cores at each location for spectral model development and laboratory clay percent determination. Field VisNIR spectra yielded results (prediction error=5.7%) comparable to previous field studies but less accurate compared to laboratory ASD contact probe estimations (prediction error=4.7%). For the Palouse sites, field-based calibrations improved estimations accuracy compared to Pacific NW or Palouse-based calibrations. Finally, insertion force and spectral data were integrated to capture bulk density variability in situ. We interrogated three fields in the Palouse region: the VisNIR penetrometer detected change but not magnitude of bulk density values achieving the highest accuracy when spectral and insertion force data were integrated. We generated interpolation maps from grid points identifying bulk density spatial autocorrelation although limited prediction accuracy was reported. This research showed promising results for the VisNIR penetrometer application in site-specific management.

Proximal soil sensing for estimation of site-specific soil hydraulic characteristics

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