Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery

D. F. Roberts, V. I. Adamchuk, J. F. Shanahan, R. B. Ferguson, J. S. Schepers

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies included uniform, interpolation, universal, field-specific, intercept-adjusted and multiple-layer prediction models. By adjusting regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields, the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested. The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal surface residue or where it has been excluded from the sensor's field-of-view. Alternatively, accuracy could be increased by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship between soil reflectance and soil OM content in the 0-1 cm soil depth or through the use of a subsurface active optical sensor.

Original languageEnglish (US)
Pages (from-to)82-102
Number of pages21
JournalPrecision Agriculture
Volume12
Issue number1
DOIs
StatePublished - Feb 2011

Keywords

  • Mean absolute error (MAE)
  • Near-infrared (NIR)
  • Organic matter (OM)
  • Root mean squared error (RMSE)
  • Visible (VIS)

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences

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