Fig. 1. Statistical characteristics of SMC

Fig. 2. Hyperspectral images based on different pretreatments. (a) Three-dimensional image; (b) R; (c) FDR; (d) SDR; (e) CR; (f) A; (g) FDA; (h) SDA

Fig. 3. Spectral curves based on different pretreatments. (a) R; (b) FDR; (c) SDR; (d) CR; (e) A; (f) FDA; (g) SDA

Fig. 4. Characteristic bands selected by different algorithms. (a)-(c) Characteristic bands of R after RF, GBRT, XGBoost screening; (d)-(f) characteristic bands of FDR after RF, GBRT, XGBoost screening; (g)-(i) characteristic bands of SDR after RF, GBRT, XGBoost screening; (J)-(l) characteristic bands of CR after RF, GBRT, XGBoost screening; (m)-(o) characteristic bands of RF, GBRT, XGBoost screening; (p)-(r) characteristic bands of FDA after RF, GBRT, XGBoost screening; (s)-(u) characteristic band of S

Fig. 5. SMC estimation results based on different preferred methods. (a)-(c) SMC estimation effect of R optimized by RF, GBRT and XGBoost; (d)-(f) SMC estimation effect of FDR optimized by RF, GBRT and XGBoost; (g)-(i) SMC estimation effect of SDR optimized by RF, GBRT and XGBoost; (j)-(l) SMC estimation effect of CR optimized by RF, GBRT and XGBoost; (m)-(o) SMC estimation effect of A optimized by RF, GBRT and XGBoost; (p)-(r) SMC estimation effect of FDA optimized by RF, GBRT and XGBoost; (s)-(u) SMC

Fig. 6. Distribution of characteristic bands

Table 1. GWR model of optimal variable SMC under different preferred methods