Multisensor On-The-Go Mapping of Soil Organic Carbon Content
- Maria Knadel *a,
- Anton Thomsena and
- Mogens H. Grevea
Detailed information on field-scale variability of soil organic C (SOC) is essential for improved C management. Conventional sampling methods are costly because of large spatial variability and the high sampling density required. To reduce costs, automated in situ methods are needed. We compared mapping SOC using a mobile sensor platform (MSP) and conventional grid sampling on a highly variable agricultural field in Denmark. Sixty-four samples collected on a 25-m grid were used to generate a reference map of SOC distribution using kriging. Mobile sensory data (visible–near infrared spectra, electrical conductivity [EC], and temperature) obtained with a MSP were used to create a map of predicted C. To predict SOC, a calibration model was developed based on 15 representative samples. The best calibration model using a second Savitzky–Golay derivative on spectral data with EC as auxiliary data resulted in values as follows: root mean square error of prediction = 5.94; R2 = 0.84; and ratio of standard error of prediction to SD [RPD] = 2.3. This study showed that the quality of those maps can be improved and spatial sampling intensities can be reduced by incorporating auxiliary data as a source of secondary information. An increased RPD value (2.3) was obtained for the sensor fusion measurements in comparison with those obtained using spectral data only (RPD = 1.9). The map based on MSP measurements detected more of the local SOC variation. High values for the error of prediction may have originated from the large SOC range (1.44–42.9%), the small number of calibration samples, and a sampling strategy that was not optimal. We concluded that more samples should be used when mapping highly variable fields.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
Copyright © 2011. . Copyright © by the Soil Science Society of America, Inc.