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

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

Modeling method Feature band screening method Number of modeled bands Modeling Validation
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