. 2024 Dec 18;20:184. doi: 10.1186/s13007-024-01312-1

Table 3.

Evaluation of LWC prediction models fitted by PLSR

Spectrally-derived data	Feature band screening methods	Calibration (n = 454)		Validation (n = 120)
Spectrally-derived data	Feature band screening methods	R²	RMSE	R²	RMSE
R	None	0.9934	0.0116	0.9656	0.0404
	CARS	0.9925	0.0124	0.9763	0.0335
	LASSO	0.9802	0.0202	0.9245	0.0598
	UVE	0.9932	0.0118	0.9688	0.0384
RLR	None	0.9938	0.0113	0.9497	0.0488
	CARS	0.9933	0.0117	0.9806	0.0303
	LASSO	0.9913	0.0133	0.9356	0.0552
	UVE	0.9936	0.0114	0.9742	0.0349
FDRL	None	0.9931	0.0119	0.9543	0.0465
	CARS	0.9925	0.0124	0.9839	0.0276
	LASSO	0.9923	0.0126	0.9431	0.0519
	UVE	0.9925	0.0124	0.9853	0.0264
SDRL	None	0.9896	0.0146	0.9182	0.0622
	CARS	0.9897	0.0145	0.9508	0.0482
	LASSO	0.9895	0.0147	0.9115	0.0647
	UVE	0.9882	0.0155	0.9722	0.0363

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