Table 2.

Evaluation of LWC prediction models fitted by MLR

Spectrally-derived data	Feature band screening methods	Calibration (n = 454)		Validation (n = 120)
		R2	RMSE	R2	RMSE
R	None	0.9961	0.009	0.8539	0.0831
	CARS	0.9932	0.0118	0.9779	0.0323
	LASSO	0.9802	0.0202	0.9245	0.0598
	UVE	0.9945	0.0106	0.8911	0.0718
RLR	None	0.996	0.009	0.8719	0.0779
	CARS	0.9941	0.011	0.9754	0.0341
	LASSO	0.9927	0.0123	0.9349	0.0555
	UVE	0.9948	0.0104	0.9139	0.0638
FDRL	None	0.9954	0.0097	0.9423	0.0523
	CARS	0.9936	0.0115	0.98	0.0307
	LASSO	0.9944	0.0107	0.9448	0.0511
	UVE	0.994	0.0111	0.9743	0.0349
SDRL	None	0.9943	0.0108	0.936	0.055
	CARS	0.9929	0.0121	0.9531	0.0471
	LASSO	0.994	0.0111	0.9301	0.0575
	UVE	0.9901	0.0142	0.9479	0.0497

The model with the highest R² value for the calibration dataset was shown in bold

R raw reflectance, RLR the reciprocal logarithm of reflectance, FDRL first-order differential of the reciprocal logarithm of reflectance, SDRL second-order differential of the reciprocal logarithm of reflectance, CARS competitive adaptive reweighted sampling, LASSO least absolute shrinkage and selection operator, UVE uninformative variable elimination