Comparison of Soil Organic Matter Estimation Using a Ground-Based Active Sensor and Aerial Imagery.

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 compare the use of an active sensor with a wide-band aerial image to estimate surface soil organic matter (OM) content as measured by means of conventional soil sampling.  Grid soil samples, active sensor soil mapping, and bare soil aerial images were collected from six fields in central Nebraska prior to the 2007 and 2008 growing seasons.  Six different OM prediction strategies were developed and tested by randomly dividing samples into calibration and validation datasets.  Strategies included Uniform, Surfacing, 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 (r² = 0.78, RMSE = 4.5 g kg^-1, MAE = 3.4 g kg^-1).  Across all fields, any method tested provided more accurate OM prediction compared to Uniform and Universal OM prediction models.  Increased accuracy in mapping soil OM using an active sensor or aerial image may be obtained by acquiring the data when minimal surface residue is present or has been removed from the sensor field-of-view, accounting for soil moisture content through the use of supplementary sensors at the time of data collection, 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.