黄河三角洲不同盐渍度土壤有机质含量的高光谱预测研究

doi:10.3864/j.issn.0578-1752.2023.10.008

Abstract

Abstract:

【Objective】The aim of this study was to investigate the spectral response of soil organic matter and salt, to analyze the effects of different salt content on soil organic matter prediction models, and to establish a rapid and effective hyperspectral prediction model for organic matter content in saline soils.【Method】In this study, according to different salinity contents for indoor hyperspectral measurements, the soil samples were divided into four groups of non-saline (SA), slightly saline (SB), moderately saline (SC), and heavy saline (SD). Then, ANOVA was used to explore the degree of organic matter and salinity spectral response of soils with different salinity degrees respectively. The raw spectra reflectances (raw spectral reflectance, R) were subjected to first order differential reflectance (first order differential reflectance, FD), continuous statistical removal (continuous statistical removal, CR), logarithmic (logarithmic, Log) and multiple scatter correction (multipication scatter correction, MSC) transformations were applied to the raw spectra reflectance; finally, three soil organic matter prediction models, namely multiple linear regression (multiple linear regression, MLR), partial least squares regression (partial least squares regression, PLSR) and support vector regression (support vector machine, SVR), were constructed based on four sets of samples of saline soils combined with the four transformed spectra, respectively.【Result】Soil organic matter and salinity had significant spectral response in the range of 400-900 nm and the change pattern were the same basically, and the sensitive bands of the two overlap. Modeling by dividing different salinity levels could improve the prediction accuracy of soil organic matter, but the prediction accuracy of the model decreased with the increase of salinity content. FD treatment could better highlight the difference of spectral characteristics and improved the correlation between organic matter content and spectral reflectance. Comparing the results of the three models, the highest accuracy of the soil organic matter prediction model was established using FD treatment combined with SVR, and the coefficients of determination R² of the optimal result modeling set and validation set were 0.86 and 0.82, respectively, the root mean square error RMSE was 2.71 and 2.96 g·kg^-1, respectively, and the ratio of prediction to deviation RPD was 2.42.【Conclusion】Soil salinity and organic matter overlapped in the sensitive bands near the visible wavelength (400-900 nm), and the accuracy of the organic matter prediction model could be effectively improved by classifying different salinity levels.

Key words: saline soil, organic matter, spectral response, hyperspectral prediction

HOU HuaGang, WANG DanYang, MA SiQi, PAN JianJun, LI ZhaoFu. Hyperspectral Prediction of Organic Matter in Soils of Different Salinity Levels in the Yellow River Delta[J].Scientia Agricultura Sinica, 2023, 56(10): 1905-1919.

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盐渍化等级 Salinization grade	样本类型 Sample type	样本数 Sample size	最小值 Min (g·kg^-1)	最大值 Max (g·kg^-1)	均值 Mean (g·kg^-1)	标准差 SD (g·kg^-1)	变异系数 CV (%)
全部样本 Whole set	Ⅰ	209	1.70	35.08	16.43	7.99	48.63
全部样本 Whole set	Ⅱ	102	2.38	35.31	16.66	7.85	47.14
SA (0-2 dS·m^-1)	Ⅰ	52	1.70	29.57	16.74	7.43	44.35
SA (0-2 dS·m^-1)	Ⅱ	25	4.33	27.93	16.82	7.15	42.50
SB (2-4 dS·m^-1)	Ⅰ	47	4.89	35.08	18.73	8.04	42.95
SB (2-4 dS·m^-1)	Ⅱ	23	6.28	34.59	18.95	7.90	41.67
SC (4-8 dS·m^-1)	Ⅰ	46	3.70	34.56	18.23	7.73	42.44
SC (4-8 dS·m^-1)	Ⅱ	22	5.61	32.92	18.23	7.10	39.00
SD (>8 dS·m^-1)	Ⅰ	64	2.08	31.12	13.19	7.69	58.27
SD (>8 dS·m^-1)	Ⅱ	32	2.38	35.31	13.81	8.30	60.10

因素 Factor	水平Level
因素 Factor	1	2	3	4
EC (dS·m^-1)	0.11-1.97	2.03-3.97	4.02-7.97	8.13-93.32
SOM (g·kg^-1)	1.70-9.82	10.01-19.97	20.01-35.31

光谱处理 Spectral processing	特征波段 Characteristic band (nm)
光谱处理 Spectral processing	全部样本 Whole set	SA (0-2 dS·m^-1)	SB (2-4 dS·m^-1)	SC (4-8 dS·m^-1)	SD (>8 dS·m^-1)
R	428、607、1102 1719、2176	638、1075 2135、2256	435、625、859 2057、2361	427、596 610、2351	598、1082 1955
FD	526、672 846、868、1498	526、664、852 999、1347	526、879、1426 1506、2161	438、495、528 858、1272、1433	522、672、881 1374
CR	844、880、998 1074、1116	785、842、880 994、1074	823、859、880 1062	648、808、852 862、1008、1072	769、791、879 1020
Log	596、608 1005、1830	669、1453 1805、2316	435、626、694 2057、2105	427、596 610	597、1090 1955
MSC	596、2109 2216、2283、2332	576、2046 2257、2339、2384	582、1134、1342 2057、2106、2280	581、1146 2174、2284、2351	596、2181 2236、2282、2335

盐渍化等级 Salinization grade	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
盐渍化等级 Salinization grade	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全部样本 Whole set	R	0.67	4.65	0.69	4.38	1.79
	FD	0.69	4.48	0.67	4.50	1.75
	CR	0.42	6.17	0.47	5.72	1.37
	Log	0.61	5.02	0.64	4.76	1.65
	MSC	0.45	5.99	0.48	5.65	1.39
SA (0-2 dS·m^-1)	R	0.74	3.94	0.44	5.80	1.23
	FD	0.80	3.52	0.75	3.51	2.04
	CR	0.62	4.80	0.61	4.63	1.54
	Log	0.73	3.99	0.68	4.09	1.75
	MSC	0.80	3.52	0.57	4.72	1.51
SB (2-4 dS·m^-1)	R	0.68	4.83	0.62	5.15	1.53
	FD	0.73	4.40	0.63	5.01	1.58
	CR	0.57	5.52	0.41	6.13	1.29
	Log	0.68	4.83	0.48	5.86	1.35
	MSC	0.59	5.51	0.52	5.33	1.48
SC (4-8 dS·m^-1)	R	0.66	4.73	0.51	5.12	1.39
	FD	0.71	4.50	0.66	4.47	1.59
	CR	0.55	5.59	0.33	6.16	1.15
	Log	0.66	4.67	0.56	4.98	1.43
	MSC	0.56	5.42	0.54	4.95	1.44
SD (>8 dS·m^-1)	R	0.55	5.27	0.62	5.22	1.59
	FD	0.70	4.36	0.71	4.42	1.88
	CR	0.54	5.40	0.36	7.20	1.15
	Log	0.56	5.20	0.61	5.54	1.50
	MSC	0.53	5.48	0.47	5.96	1.39

盐渍化等级 Salinization grade	光谱处理 Spectral processing	建模集 Calibration set		验证集 Validation set
盐渍化等级 Salinization grade	光谱处理 Spectral processing	Rc²	RMSEc	Rv²	RMSEv	RPD
全部样本 Whole set	R	0.66	4.62	0.68	4.48	1.75
	FD	0.69	4.42	0.68	4.47	1.76
	CR	0.42	6.08	0.46	5.75	1.37
	Log	0.61	4.96	0.63	4.76	1.65
	MSC	0.45	5.91	0.47	5.68	1.38
SA (0-2 dS·m^-1)	R	0.71	3.95	0.51	5.15	1.39
	FD	0.76	3.61	0.74	3.60	1.99
	CR	0.62	4.51	0.61	4.63	1.54
	Log	073	3.80	0.68	4.09	1.75
	MSC	0.79	3.33	0.54	4.67	1.53
SB (2-4 dS·m^-1)	R	0.68	4.51	0.56	5.30	1.49
	FD	0.72	4.22	0.60	5.15	1.53
	CR	0.57	5.24	0.42	6.01	1.31
	Log	068	4.51	0.48	5.86	1.35
	MSC	0.58	5.12	0.53	5.34	1.48
SC (4-8 dS·m^-1)	R	0.66	4.47	0.51	5.12	1.39
	FD	0.67	4.42	0.67	4.16	1.71
	CR	0.52	5.31	0.33	6.16	1.15
	Log	0.66	4.46	0.56	4.98	1.43
	MSC	0.56	5.09	0.61	4.51	1.58
SD (>8 dS·m^-1)	R	0.55	5.11	0.61	5.30	1.57
	FD	0.70	4.20	0.70	4.47	1.86
	CR	0.54	5.20	0.36	7.20	1.15
	Log	0.56	5.03	0.61	5.57	1.49
	MSC	0.51	5.33	0.49	5.84	1.42

Hyperspectral Prediction of Organic Matter in Soils of Different Salinity Levels in the Yellow River Delta

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