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Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field.

Tekin Y, Ulusoy Y, Tümsavaş Z, Mouazen AM - ScientificWorldJournal (2014)

Bottom Line: Calibration model of SOC in full cross-validation resulted in a good accuracy (R (2) = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81).The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R (2) = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R (2) = 0.60, RMSEP = 0.20%, and RPD = 1.41).Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

View Article: PubMed Central - PubMed

Affiliation: Vocational School of Technical Science, Uludag University, 16059 Bursa, Turkey.

ABSTRACT
This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validation resulted in a good accuracy (R (2) = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R (2) = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R (2) = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

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SpectroSense SKL925 canopy sensor and area of measurement.
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Related In: Results  -  Collection


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fig2: SpectroSense SKL925 canopy sensor and area of measurement.

Mentions: The canopy sensor SpectroSense SKL925 (SKYE, UK) was used to measure the crop (wheat) NDVI (Figure 2). Vegetation indices can be calculated as the ratios of different wavebands of reflected solar radiation and are related to the abundance and activity of radiation absorbers such as water and plant chlorophyll. The sensor is fitted with a removable cosine correcting light acceptance head. When taking incident or downwelling light measurements, the head is left in place so that the sensor is fully cosine corrected (accepts light in accordance with Lambert's Cosine Law). Sensor 1 is fitted with the cosine correcting head to measure incident light. Sensor 2 is of a narrow angle and measures reflected light (Figure 2). Both incident and reflected light are measured simultaneously by 2 identical sensors, to eliminate fluctuations in solar radiation. Without the cosine head, both 2 and 4 channel sensors have a 25° cone field of view (12.5° off perpendicular). The area of ground in view to the sensor is then defined by the height above the ground, as shown in Figure 2.


Online measurement of soil organic carbon as correlated with wheat normalised difference vegetation index in a vertisol field.

Tekin Y, Ulusoy Y, Tümsavaş Z, Mouazen AM - ScientificWorldJournal (2014)

SpectroSense SKL925 canopy sensor and area of measurement.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109388&req=5

fig2: SpectroSense SKL925 canopy sensor and area of measurement.
Mentions: The canopy sensor SpectroSense SKL925 (SKYE, UK) was used to measure the crop (wheat) NDVI (Figure 2). Vegetation indices can be calculated as the ratios of different wavebands of reflected solar radiation and are related to the abundance and activity of radiation absorbers such as water and plant chlorophyll. The sensor is fitted with a removable cosine correcting light acceptance head. When taking incident or downwelling light measurements, the head is left in place so that the sensor is fully cosine corrected (accepts light in accordance with Lambert's Cosine Law). Sensor 1 is fitted with the cosine correcting head to measure incident light. Sensor 2 is of a narrow angle and measures reflected light (Figure 2). Both incident and reflected light are measured simultaneously by 2 identical sensors, to eliminate fluctuations in solar radiation. Without the cosine head, both 2 and 4 channel sensors have a 25° cone field of view (12.5° off perpendicular). The area of ground in view to the sensor is then defined by the height above the ground, as shown in Figure 2.

Bottom Line: Calibration model of SOC in full cross-validation resulted in a good accuracy (R (2) = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81).The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R (2) = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R (2) = 0.60, RMSEP = 0.20%, and RPD = 1.41).Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

View Article: PubMed Central - PubMed

Affiliation: Vocational School of Technical Science, Uludag University, 16059 Bursa, Turkey.

ABSTRACT
This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validation resulted in a good accuracy (R (2) = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R (2) = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R (2) = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

Show MeSH
Related in: MedlinePlus