Effects of Subsetting by Carbon Content, Soil Order, and Spectral Classification on Prediction of Soil Total Carbon with Diffuse Reflectance Spectroscopy

Table 5

Detailed partial least squares regression model results for soil total carbon (C_𝑡) prediction from the subsets of mid-infrared diffuse reflectance spectra based on soil order. The range of values reflects the results of 10 random iterations of the models, and the number in parentheses is the mean. Detailed results are also given for full sample set models with no subsetting for comparison. For models with full cross validation (i.e., leave-one-out cross validation), the same samples used to calibrate the model were used to validate the model.

	Calibration				Validation
	𝑛^a	𝑅^2,b	RMSE (%)^c		𝑛	𝑅^2	RMSE (%)	RPD^d	RPIQ^e
Andisol soils	64–71	0.84–0.96	1.92–3.02		25–32	0.41–0.92	2.99–6.94	1.12–3.60	1.87–4.09
		(0.91)	(2.49)			(0.79)	(4.03)	(2.33)	(2.66)
High activity clay soils	67–72	0.96–0.99	0.96–2.71		29–34	0.95–0.99	1.70–3.60	4.34–9.81	0.92–4.38
		(0.98)	(1.74)			(0.96)	(2.65)	(5.57)	(2.44)
Low activity clay soils	75	0.98	0.24		Full cross validation	0.79	0.80	2.10	2.01
Organic-dominated soils	26	0.97	2.9		Full cross validation	0.86	6.7	2.52	4.78
Full sample set	215	0.94–0.98	1.94–3.50		92	0.91–0.96	2.74–3.91	3.38–5.07	3.22–5.27
		(0.96)	(2.78)			(0.94)	(3.39)	(4.07)	(3.89)
Full sample set	307	0.95	3.12		Full cross validation	0.94	3.52	3.94	3.68

^aNumber of samples.
^bCoefficient of determination.
^cRoot mean squared error.
^dResidual prediction deviation.
^eRatio of performance to interquartile distance.