6.2.1. Spectrophotometer and Optical Probe
A prototype of a soil probe was developed, based on the commercially available plant contact probe supplied by Analytical Spectral Devices, USA. A high-intensity light source (4.5 watt) of halogen lamp was fitted to the probe. To avoid direct contact of the quartz probe window with the soil, a round casing was developed to provide a fixed distance between the soil surface and the probe window. This modification also excludes the ambient light (see Figure 6.1). The object/probe window distance could be easily adjusted by changing a plastic (PVC) spacer ring (inner diameter = 75 mm) that fitted into the casing. For this study a distance of 30.5 mm between the soil surface and probe window was used.
6.2.2. Soil Samples and Site Locations
Soil diffuse reflectance spectra were acquired in situ at two times (May and October 2006) from 7 pasture field sites under permanent pasture and from 1-year, 3- years and 5-years pine-to-pasture conversions from the Taupo - Rotorua Volcanic Region of New Zealand. The first three sites, near Atiamuri, were 1-year conversion (38o 19.59 S, 176o 2.48 E), 5-year conversion (38o 20.05 S, 176o 3.22 E) and permanent pasture (38o 19.43 S, 176o 2.84 E) sites, mapped as Taupo sandy silts (Vucetich and Wells 1978) and classified as Pumice Soils (Hewitt 1998). Two sites, 3-years conversion (38o 8.89 S, 175o 46.75 E) and permanent pasture (38o 10.71 S, 175o 49.65 E), around Lichfield, Tokoroa, are classified as Allophanic Soil (Hewitt 1998). The last two sites, 5-years conversion (38o 0.14 S, 176o 41.56 E) and permanent pasture (37o 59.75 S, 176o 4.14 E) at Manawahe, are classified as Tephric Recent Soil (Hewitt 1998). Thirty soil samples were taken at each site, from 5 positions at 15-m intervals along each of three 75-m transects that were 20 m apart, and at 2 depths. Soil cores were collected with a corer (46 mm diameter), extruded and sliced (15 mm) at 37.5 and 112.5 mm. The diffuse reflectance of the sliced surface was recorded and then a 15-mm soil slice was taken for further laboratory measurements (Figure 6.1).
6.2.3. Measurement of Soil Properties
Soil water content was determined by measuring the difference between the weight of field-moist soil and air-dry soil (dried at 35oC). Air dry soils were then divided equally into two subsamples. The first subsample was ground into a fine powder using a ring grinder for total C and N analysis on a 1-g sample using a LECO FP-2000 CNS Analyser dry combustion method (Blakemore et al. 1987). The second subsample was kept for further analysis.
6.2.4. Reflectance Measurement and Spectral Pre-Processing
The diffuse reflectance spectra of each freshly cut surface were recorded from a flat sectioned horizontal soil surface of a soil core (46 mm diameter) using the purpose- built contact probe attached by fibre optic cable to an ASD FieldSpecPro spectroradiometer (Analytical Spectral Devices) (Figure 6.1). The instrument records spectra with sampling interval of 1.4 nm for the region 350-1000 nm and 2 nm for the region 1000-2500 nm. The data processing software associated with the ASD
FieldSpecPro spectroradiometer then interpolates this 1.4- and 2-nm-spaced data to produce 1-nm-spaced data. During reflectance recording, the soil core was rotated 360o with the speed of 18o/s in order to scan the whole target area. Thus the scanned target area summed up to 561 mm2. The instrument was set to average ten readings internally for each spectrum saved. In this manner ten averaged spectra were collected per soil sample. A Spectralon® reference panel was used as white reference to calibrate the equipment between each soil sample recording.
Figure 6.1 Using the soil probe to make reflectance measurement on sectioned soil cores.
Wavebands exhibiting obvious noise (350-470 nm and 2440-2500 nm) were excluded from further processing. A Savitzky-Golay filter with window sizes of 31 and polynomial orders of 4 was applied to the spectra, then the data was reduced to 381 spectral bands by down-sampling to every 5th waveband. The first derivative was calculated using the Savitzky-Golay algorithm with window sizes of 7 and polynomial orders of 5. These derivatives were then averaged for every soil sample. All spectral processing steps were carried out in SpectraProc which is software created for spectral pre-processing, discriminant analysis, PCA and database structure (Hueni and Tuohy 2006).
6.2.5. Development of Calibration Models
Calibration models for the relationship between the spectral first derivative and total soil C and total soil N values were developed using partial least squares regression
(PLSR) in MINITAB 14 (MINITAB Inc. 2003). Using PLSR-1, the chemical analysis data (single Y-variable) (total C or N) was regressed against the pre-processed soil reflectance data. The number of factors (components or latent variables) used in the calibration model were the number that minimised the PRESS (predicted residual error sum of square) in the leave-one- out cross validation procedure (Miller and Miller 2005). The number of components obtained was between 3 and 8 (see Table 6.3 and 6.4). Calibration models were then used to predict the C and N concentrations of a separate population of soil samples (the validation set).
To provide a large pool of data from which to draw the calibration and validation datasets, soil spectral and reference data from May and October (210 samples) were amalgamated. A preliminary PLSR analysis of the laboratory-measured C or N against the spectral data using all the samples identified 10 (C values) and 11 (N values) samples that had cross-validated standardised residuals outside ±2 (MINITAB Inc. 2003). Although this is about the number that should be expected from normally distributed data, they were removed from subsequent analysis to avoid the comparison between methods being influenced by a few extreme values.
The remaining samples were separated into calibration (A) and validation (B) sets using four methods. These were:
(I) Prior knowledge of pedological range. Near neighbour sample pairs (soil type, location, transect, site and depth) were alternately allocated to set A and B.
(II) Prior knowledge of sample chemical analysis range. All soil samples were ranked from the lowest to the highest C (or N) content and odd and even ranked numbers were allocated to set A and B, respectively.
(III) No prior knowledge of samples, Euclidean distance analysis, whole pool. A principal component analysis (PCA) of the pre-processed spectral data of total data pool was conducted and the scores of the first two PCs were plotted against each other. Standardised Euclidean distances of all sample points from the centroid were calculated (Miller and Miller 2005) and the samples were ranked from the lowest to the highest standardised Euclidean
distance. Odd and even ranked numbers were allocated to set A and B, respectively.
(IV) No prior knowledge of samples, Euclidean distance analysis by quadrant. PC1 and PC2 scores were plotted and each sample point was ranked by standardised Euclidean distance within its quadrant. Odd and even ranked numbers from all quadrants were allocated to set A and B, respectively. For all selection methods, the stability of the prediction model was evaluated by also using set B as the calibration set and set A as the validation set, a “vice versa test”. Selection methods of II and III were further evaluated by varying the ratio of samples allocated to the calibration and validation sets by 2:1 and 3:1.
6.2.6. Regression Model Accuracy
The ability of the PLSR model to predict soil properties was assessed using the following statistics: RMSE (root mean square error), which is the standard deviation of the difference between the measured and the predicted values of soil properties; RMSEP (root mean square error of prediction) is calculated from the validation data (Esbensen
et al. 2006):
(
)
N y ym v∑
− = 2 RMSEPwhere ymis the measured laboratory value, and yv is the predicted value from the PLSR model, and N is the number of samples; r2, which is the proportion of variance in ym accounted for by the PLSR model predicted values (yv); RPD (ratio of prediction to deviation), which is the ratio of the standard deviation of measured values of soil properties to the RMSE (this shows how much more accurate, as measured by the standard error, a prediction from the model is than simply quoting the overall mean); RER (ratio error range), which is the ratio of the range of measured values of soil properties to the RMSE:
( )
RMSEP STDEV RPD= ym ,( )
( )
RMSEP Min Max RER = ym − ymThe best prediction model is shown by the highest RPD, RER, r2 and the lowest RMSEP.