Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

Table 4 Regression analysis of the characteristic bands and LWC by different modeling methods

Modeling method	Feature band screening method	Number of modeled bands	Modeling			Validation
Modeling method	Feature band screening method	Number of modeled bands	R²	RMSE	RPD	R²	RMSE	RPD
PLSR	Full band	1901	0.87	2.11	2.73	0.82	1.78	2.09
	CA	100	0.86	2.10	2.52	0.84	1.61	2.43
	x-LW	28	0.86	2.61	1.96	0.84	1.73	2.04
RF	Full band	1901	0.88	2.80	1.26	0.83	1.69	2.31
	CA	100	0.90	2.49	1.63	0.81	1.75	2.15
	x-LW	28	0.88	1.57	3.10	0.80	1.86	1.92
ERF	Full band	1901	0.87	1.36	2.18	0.85	1.52	2.30
	CA	100	0.86	1.95	2.48	0.82	1.76	2.34
	x-LW	28	0.88	1.46	3.37	0.84	1.62	2.39
KNN	Full band	1901	0.82	2.10	1.61	0.83	1.61	2.20
	CA	100	0.85	2.00	2.25	0.80	1.79	2.16
	x-LW	28	0.84	2.04	1.80	0.80	1.74	1.83

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