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Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Xiaoshuai Pei1, Kenneth A Sudduth2, Kristen S Veum3

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Summary

In-situ diffuse reflectance spectroscopy (DRS) combined with other sensors accurately estimates soil properties. This multi-sensor approach offers a rapid, non-destructive field method for soil analysis.

Keywords:
diffuse reflectance spectroscopyin-situ sensingprecision agricultureprofile soil propertiesproximal soil sensing

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Area of Science:

  • Soil Science
  • Remote Sensing
  • Agricultural Engineering

Background:

  • Optical diffuse reflectance spectroscopy (DRS) is a laboratory technique for soil property estimation.
  • In-situ DRS offers potential for rapid, non-destructive, and cost-effective field measurements.
  • Accurate in-situ soil property assessment is crucial for precision agriculture and environmental monitoring.

Purpose of the Study:

  • To evaluate the in-situ estimation of various soil physical and chemical properties using visible and near-infrared (VNIR) spectra.
  • To assess the added value of combining VNIR spectra with depth, apparent electrical conductivity (ECa), and cone index (CI) data (DECS).
  • To identify optimal preprocessing and modeling techniques for accurate in-situ soil property estimation.

Main Methods:

  • A Veris P4000 instrument collected VNIR spectra, ECa, CI, and depth data to 1 m depth in two Missouri fields.
  • Soil core samples were analyzed in the laboratory for soil organic carbon (SOC), total nitrogen (TN), moisture, texture, cation exchange capacity (CEC), Ca, Mg, K, and pH.
  • Visible and near-infrared (VNIR) spectra were analyzed alone and in combination with DECS using partial least squares regression (PLSR), neural networks, regression trees, and random forests.
  • Gaussian smoothing filter preprocessing was evaluated alongside various calibration methods.

Main Results:

  • The combination of Gaussian smoothing filter preprocessing and PLSR yielded the best model performance for most soil properties.
  • The DECS approach improved the estimation of silt, sand, CEC, Ca, and Mg compared to VNIR spectra alone, with >5% improvement for Ca.
  • Field-specific differences in estimation accuracy were observed, indicating potential site-specific calibration needs.

Conclusions:

  • In-situ estimation of profile soil properties is feasible using a multi-sensor approach combining VNIR spectroscopy with ECa, CI, and depth data.
  • Partial least squares regression (PLSR) with Gaussian smoothing preprocessing is recommended for optimal results.
  • Further research may be needed to address field-specific variations in estimation accuracy for robust in-situ soil monitoring.