Soil Profile Measurement of Carbon Contents using a Probe-type VIS-NIR Spectrophotometer

Abstract

An in-situ probe-based spectrophotometer has been developed. This system used two spectrometers to measure soil reflectance spectra from 450 nm to 2200 nm. It collects soil electrical conductivity (EC) and insertion force measurements in addition to the optical data.Six fields in Kansas were mapped with the VIS-NIR (visible-near infrared) probe module and sampled for calibration and validation. Results showed that VIS-NIR correlated well with carbon in all six fields, with RPD (the ratio of standard deviation to root mean square error of prediction) of 1.8 or better, RMSE of 0.14 to 0.22%, and R 2 of 0.69 to 0.89.From the investigation of carbon variability within the soil profile and by tillage practice, the 0-5 cm depth in a no-till field contained significantly higher levels of carbon than any other locations. Using the selected calibration model with the soil NIR probe data, a soil profile map of estimated carbon was produced, and it was found that estimated carbon values are highly correlated to the lab values.The array of sensors (VIS-NIR, electrical conductivity, insertion force) used in the probe allowed estimating bulk density, and three of the six fields were satisfactory. The VIS-NIR probe also showed the obtained spectra data were well correlated with nitrogen for all fields with RPD scores of 1.84 or better and coefficient of determination (R 2 ) of 0.7 or higher.

Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe

“…In a comprehensive review of visible, NIR and mid-infrared (MIR) spectroscopy, the median R 2 of 33 studies using vis-NIR for the prediction of SOC was 0.83; median R 2 values were 0.80 for in situ methods and 0.85 for laboratory methods (Soriano-Disla et al 2014). However, in this study and two studies using similar technology (Ben-Dor et al 2008;Kweon et al 2009), in situ calibration models surprisingly yielded better prediction performance than the median laboratory vis-NIR methods (Soriano-Disla et al 2014).…”

“…Our best spiked regional models had comparable prediction accuracy (R 2 00.69 to 0.86; RMSEP06.4 to 10.0 g kg (1 ) to local calibration models (R 2 00.69 to 0.89; RMSEP 00.8% to 1.27%) developed by Kweon et al (2009). This suggests that the loss of prediction accuracy for in situ methods relative to laboratory methods is insignificant compared with the larger sample sizes and the ability to characterize the spatial variability of SOC achievable with field-portable instruments (Viscarra Rossel et al 2006;Bellon-Maurel and McBratney 2011).…”

“…Calibration models based on laboratory collected spectra on ground and dried samples yield good prediction results for SOC and other soil properties (refer to reviews Bellon-Maurel and McBratney 2011;Soriano-Disla et al 2014). For field condition soils, a variety of approaches for estimating SOC have been tested including using moist samples (Chang et al 2005;Stenberg 2010;Nocita et al 2013), simulated in situ methods (Morgan et al 2009), static in situ methods (Ben-Dor et al 2008;Kusumo et al 2008;Kweon et al 2009;Wenjun et al 2014) and on-the-go in situ methods (Mouazen et al 2007;Christy 2008;Kweon and Maxton 2013). Particularly useful in a remote field environment is the novel static in situ method developed by BenDor et al (2008) where a spectrometer with a probe attachment is used to collect soil spectra down the profile of a drilled hole.…”

Peak functions for modeling high resolution soil profile data

“…If successfully calibrated, sensors can estimate soil properties at many more locations than reference lab procedures, and at much finer depth resolution. Examples of such penetrometer based sensing technologies include: cone index (CI) (Grunwald et al, 2001;Perumpral, 1987;Richards, 1941), shaft friction sleeve (Lunne et al, 1997), bulk apparent soil electrical conductivity (EC a ) (Drummond et al, 2000), acoustic cone penetrometer (Houlsby and Ruck, 1998;Villet et al, 1981), water content by time or frequency domain waveguides (Kosugi et al, 2009;Sun et al, 2004;Topp et al, 2003), visible spectrum video or digital cameras (Lieberman and Knowles, 1998;Rooney et al, 2001a,b), and near infrared (NIR) or visible/near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) (Hummel et al, 2004;Kweon et al, 2009). Several of these penetrometer technologies may be employed in combinations to rapidly and comprehensively characterize soil profiles (Rooney et al, 2002).…”

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

“…Studies have been published on predicting soil carbon or SOM (Kweon et al ., ; Knadel et al ., ; Kweon, ; Mahmood et al ., ), pH and macronutrients (Taylor et al ., ; Mahmood et al ., ; Schirrmann et al ., ), and soil texture and clay content (Taylor et al ., ; Mahmood et al ., ; Piikki et al ., ). Both Piikki et al .…”