Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset

Table 3

Unitary linear regression results of vegetation index and spectral differential characteristics.

Vegetation indices/spectral differential characteristics	Regression equations	Fitting R^2	Testing R^2	RMSE
NDVI	y = −230.43x + 190.6	0.66	0.62	15.90
RVSI	y = 2050.4x + 94.691	0.46	0.41	18.97
TVI	y = −3.1273x + 121.01	0.51	0.46	18.04
MCARI	y = −312.63x + 98.836	0.56	0.52	17.03
TCARI	y = −318.56x + 109.58	0.32	0.26	21.10
PRI	y = 939.73x + 9.7802	0.23	0.17	22.39
NRI	y = 218.71x + 46.115	0.60	0.56	16.24
GNDVI	y = −255.18x + 205.82	0.29	0.22	21.74
PSRI	y = 512.1x − 2.8595	0.63	0.60	15.64
SIPI	y = 246.27x − 241.23	0.63	0.59	15.72
NBNDVI	y = −237.09x + 190.71	0.67	0.63	14.85
SDb	y = −2863.5x + 86.013	0.37	0.32	20.42
Db	y = −74747x + 83.754	0.46	0.41	18.89
SDy	y = 5342x + 49.266	0.64	0.60	15.45
Dy	y = 227634x + 12.743	0.29	0.10	23.56
SDr	y = −438.57x + 123.95	0.42	0.37	19.54
Dr	y = −21667x + 126.12	0.48	0.44	18.45
SDg	y = −2696.1x + 85.146	0.30	0.23	21.63
SDg/SDb	y = 134.53x − 95.534	0.26	0.16	22.82
SDy/SDb	y = 0.0089x − 0.3791	0.61	0.58	15.88
(SDr − SDy)/(SDr + SDy)	y = −477.23x + 524.05	0.63	0.60	15.53
(SDg − SDb)/(SDg + SDb)	y = 304.84x + 38.803	0.28	0.19	22.25
WSI	y = 75.498x + 76.016	0.73	0.70	13.41

Correlation is significant at the 0.999 confidence level.