Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra

De-fang LUO; Jie PENG; Chun-hui FENG; Wei-yang LIU; Wen-jun JI; Nan WANG

doi:10.3964/j.issn.1000-0593(2021)10-3069-08

Journals >Spectroscopy and Spectral Analysis >Volume 41 >Issue 10 >Page 3069 > Article

Spectroscopy and Spectral Analysis
Vol. 41, Issue 10, 3069 (2021)

Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra

De-fang LUO^1、*, Jie PENG^{1、1; *;}, Chun-hui FENG^1、1;, Wei-yang LIU^1、1;, Wen-jun JI^2、2;, and Nan WANG^3、3;

Author Affiliations

¹1. College of Plant Sciences, Tarim University, Alar 843300, China

²2. College of Land Resources Management, China Agricultural University, Beijing 100083, China

³3. College of Environment and Resources, Zhejiang University, Hangzhou 310058, China

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DOI: 10.3964/j.issn.1000-0593(2021)10-3069-08 Cite this Article

De-fang LUO, Jie PENG, Chun-hui FENG, Wei-yang LIU, Wen-jun JI, Nan WANG. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. Spectroscopy and Spectral Analysis, 2021, 41(10): 3069 Copy Citation Text

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$VNIR (a) and MIR (b) reflectance spectra of soil organic matter and fractions$

Fig. 1. VNIR (a) and MIR (b) reflectance spectra of soil organic matter and fractions

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Fig. 2. Correlations between soil organic matter content and visible-near infrared (a), mid-infrared (b) reflectivity

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Fig. 3. Scatter plots of predicted vs. measured values for single model (a) and combination model (b)

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测定指标	单位	样本数	平均值/ (g·kg^-1)	标准差/ (g·kg^-1)	最大值/ (g·kg^-1)	最小值/ (g·kg^-1)	峰度/ (g·kg^-1)	变异系数/%
有机质	(g·kg^-1)	93	27.24	12.50	68.02	12.52	1.43	45.87
胡敏素	(g·kg^-1)	93	21.81	10.87	59.50	9.44	2.61	49.86
富里酸	(g·kg^-1)	93	1.65	1.00	4.90	0.05	1.68	60.56
胡敏酸	(g·kg^-1)	93	2.52	1.48	8.59	0.72	2.63	58.75

Table 1. Statistics of soil organic matter and fractions

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光谱	模型	精度指标	有机质/(g·kg^-1)		胡敏素/(g·kg^-1)		胡敏酸/(g·kg^-1)		富里酸/(g·kg^-1)
光谱	模型	精度指标	建模	预测	建模	预测	建模	预测	建模	预测
VIS-NIR	PLSR	R²	0.80	0.77	0.83	0.81	0.82	0.81	0.85	0.85
		RMSE	5.17	5.73	4.33	4.37	0.53	0.67	0.37	0.40
		RPIQ	2.39	2.37	2.73	2.72	2.92	2.87	3.46	2.81
	RF	R²	0.83	0.81	0.93	0.92	0.93	0.92	0.80	0.76
		RMSE	4.14	3.96	2.44	2.66	0.34	0.34	0.35	0.36
		RPIQ	3.36	2.36	4.87	4.61	4.75	3.93	4.00	3.10
	SVM	R²	0.75	0.73	0.80	0.77	0.71	0.70	0.83	0.80
		RMSE	4.59	5.74	4.11	4.15	0.62	0.65	0.32	0.39
		RPIQ	2.70	2.37	2.96	2.86	3.18	2.95	4.09	2.90
	MIR	PLSR	R²	0.78	0.75	0.82	0.81	0.74	0.74	0.90	0.88
			RMSE	5.42	5.55	4.41	4.45	0.60	0.66	0.30	0.40
RPIQ			2.95	2.42	2.68	2.67	2.67	2.56	4.38	2.82
RF		R²	0.87	0.86	0.89	0.87	0.88	0.86	0.94	0.94
		RMSE	3.87	2.51	2.64	3.79	0.42	0.33	0.22	0.24
		RPIQ	3.89	1.91	4.51	2.75	3.62	4.56	6.08	4.41
SVM		R²	0.80	0.78	0.78	0.77	0.74	0.74	0.80	0.76
		RMSE	5.04	5.24	3.94	4.40	0.62	0.71	0.36	0.44
		RPIQ	2.46	2.59	2.99	2.70	2.49	2.71	3.64	2.59
VIS-NIR-MIR		PLSR	R²	0.80	0.78	0.85	0.81	0.83	0.80	0.86	0.80
			RMSE	4.78	5.01	3.63	4.35	0.53	0.67	0.39	0.71
	RPIQ		2.79	2.71	3.25	2.73	2.83	2.87	3.63	2.07
	RF	R²	0.92	0.90	0.88	0.87	0.89	0.89	0.87	0.87
		RMSE	2.90	4.08	2.96	2.84	0.41	0.33	0.31	0.31
		RPIQ	4.52	3.51	4.14	2.63	3.65	4.58	3.99	4.28
	SVM	R²	0.86	0.85	0.87	0.85	0.84	0.83	0.85	0.83
		RMSE	3.81	4.07	3.60	3.69	0.44	0.52	0.30	0.41
		RPIQ	3.25	3.34	3.28	3.22	3.68	3.67	4.28	3.44

Table 2. Construction and verification of PLSR, SVM, and RF models based on different spectra

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