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Soil Science Society of America Journal Abstract - Pedology

Multisensor On-The-Go Mapping of Soil Organic Carbon Content


This article in SSSAJ

  1. Vol. 75 No. 5, p. 1799-1806
    Received: Dec 16, 2010

    * Corresponding author(s): maria.knadel@agrsci.dk
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  1. Maria Knadel *a,
  2. Anton Thomsena and
  3. Mogens H. Grevea
  1. a Dep. of Agroecology and Environment Faculty of Agricultural Sciences Aarhus Univ. Blichers Allé 20 Postboks 50 Tjele DK-8830, Denmark


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.

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