Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

Figures & data

Table 1. Methods for determining the main leaf functional traits of Spartina alterniflora.

Leaf functional trait	Measurement method
Moisture content (MC)	[(Wet weight − Dry weight)/Dry weight] × 100%
Soil plant analysis development (SPAD)	Chlorophyll meter (SPAD-502 Plus; Konica Minolta, Japan)
Specific leaf area (SLA)	Leaves and extracts the leaf area are scanned through ImageJ software. Specific leaf area: single leaf area/single leaf dry weight
Total nitrogen (TN)	Element analyser (Vario PYRO cube; Elemental, Germany)
Total phosphorus (TP)	Molybdenum Antimony Colorimetry (NY/T 2421-2013)
Total carbon (TC)	Element analyser (Vario PYRO cube; Elemental, Germany)

Table 2. Leaf functional traits of Spartina alterniflora.

Leaf functional trait	No. of Samples	Minimum	Maximum	Mean	Standard Deviation	Coefficient of Variation
FW (g)	238	0.68	5.84	2.64	0.84	31.92%
LT (mm)	238	0.29	0.74	0.52	0.09	17.79%
SPAD	238	14.97	54.00	42.32	5.38	12.72%
TN (g/kg)	238	9.23	36.17	14.60	2.90	19.85%
TP (g/kg)	238	0.90	3.19	1.90	0.38	20.05%
TC (g/kg)	238	258.74	545.84	360.96	42.10	11.66%
MC	238	21.55	83.82	68.18	5.75	8.43%
SLA (cm^2/g)	238	24.02	258.24	87.42	28.33	32.40%
N/P	238	4.45	21.14	8.00	2.29	28.65%
C/N	238	9.23	37.83	25.31	3.89	15.35%
C/P	238	111.69	424.71	198.63	50.48	25.36%

Note: FW: fresh weight; LT: leaf thickness; SPAD: soil plant analysis development; TN: total nitrogen; TP: total phosphorus; TC: total carbon; MC: moisture content; SLA: specific leaf area; N/P: TN/TP; C/N: TC/TN; C/P: TC/TP.

Figure 1. Analysis of random forest (RF) importance scores for the original (OR) and first-order differential (FD) hyperspectral data: (a) FW, (b) LT, (c) SPAD, (d) MC, (e) SLA, (f) TN, (g) TP, (h) TC, (i) C/N, (j) C/P, and (k) N/P.

Figure 2. Analysis of eXtreme Gradient Boosting (XGBoost) importance scores for the or and FD hyperspectral data: (a) C/N, (b) C/P, (c) N/P, (d) TC, (e) TN, (f) TP, (g) FW, (h) LT, (i) MC, (j) SLA, and (k) SPAD.

Table 3. Modelling accuracy verification.

Model	Leaf function trait	Origin		First-order differential
		R^2	RMSE	R^2	RMSE
RF	FW	0.53	0.63	0.65	0.62
	LT	0.24	0.08	0.39	0.08
	SPAD	0.20	4.40	0.44	3.81
	MC	0.53	3.32	0.62	3.08
	SLA	0.27	23.80	0.29	25.10
	TN	0.13	2.51	0.17	2.43
	TP	0.05	0.38	0.18	0.36
	TC	0.56	27.46	0.69	26.38
	C/N	0.06	3.61	0.20	3.32
	C/P	0.01	51.45	0.14	48.29
	N/P	0.01	2.37	0.02	2.26
SVM	FW	0.88	1.06	0.89	1.06
	LT	0.71	0.07	0.76	0.07
	SPAD	0.87	6.04	0.85	6.04
	MC	0.92	6.04	0.90	6.03
	SLA	0.47	33.19	0.28	33.06
	TN	0.82	3.31	0.81	3.30
	TP	0.85	0.42	0.86	0.42
	TC	0.21	48.23	0.41	44.55
	C/N	0.87	4.40	0.87	4.41
	C/P	0.30	59.72	0.26	59.73
	N/P	0.56	2.64	0.55	2.65
BPNN	FW	0.76	1.13	0.76	1.22
	LT	0.88	1.12	0.63	0.14
	SPAD	0.54	6.91	0.62	6.45
	MC	0.53	5.70	0.38	6.62
	SLA	0.23	41.07	0.03	52.15
	TN	0.47	4.56	0.28	4.68
	TP	0.60	0.56	0.49	0.51
	TC	0.69	56.21	0.72	56.88
	C/N	0.45	3.96	0.23	5.95
	C/P	0.51	66.20	0.48	58.70
	N/P	0.28	3.36	0.41	2.73
XGBoost	FW	0.57	0.57	0.65	0.51
	LT	0.20	0.08	0.30	0.08
	SPAD	0.16	4.56	0.32	4.08
	MC	0.45	3.72	0.59	3.02
	SLA	0.16	25.33	0.29	23.69
	TN	0.12	2.68	0.17	2.51
	TP	0.01	0.41	0.07	0.38
	TC	0.44	31.03	0.66	24.11
	C/N	0.01	4.04	0.03	3.84
	C/P	0.01	58.72	0.02	52.18
	N/P	0.01	2.55	0.02	2.42

Figure 3. Scatter plots of RF model accuracy.

Figure 4. Scatter plots of SVM modelling accuracy.

Figure 5. Scatter plots of BPNN modelling accuracy.

Figure 6. Scatter plots of XGBoost modelling accuracy.

Table 4. Inversion of the functional traits.

Medium	Index	Accuracy assessment	Reference
Generative adversarial network with a constrained factor (GAN-CF)	Aboveground biomass (AGB)	RMSE = 2.07 MAE = 1.85	Chen et al. (2022)
Modified chlorophyll absorption in reflectance index (MCARI)	Biomass	R^2 = 0.63	González Trilla et al. (2013)
	Percentage canopy cover (PCC)	R^2 = 0.75
	LAI	R^2 = 0.74
Vegetation index	Crude protein	RMSEP = 1.464 PRD = 2.690	Zhu (2023)
	Crude fibre	RMSEP = 2.322 PRD = 1.857
	Crude fat	RMSEP = 0.608 PRD = 1.794
FDNDVI [723, 525]	Chla	R^2 = 0.6037 RMSEP = 0.3425	Ai et al. (2015)
	Chlb	R^2 = 0.6246 RMSEP = 0.1225
	Cars	R^2 = 0.6058 RMSEP = 0.0692

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Data availability statement

Hyperspectral data and functional trait data used in this study are available from the corresponding author upon request.