Estimation of Soil Total Phosphorus Content in Coastal Areas Based on Hyperspectral Reflectance

Dan-ping WEI; Guang-hui ZHENG

doi:10.3964/j.issn.1000-0593(2022)02-0517-07

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 2 >Page 517 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 2, 517 (2022)

Estimation of Soil Total Phosphorus Content in Coastal Areas Based on Hyperspectral Reflectance

Dan-ping WEI^* and Guang-hui ZHENG^*;

Author Affiliations

School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

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DOI: 10.3964/j.issn.1000-0593(2022)02-0517-07 Cite this Article

Dan-ping WEI, Guang-hui ZHENG. Estimation of Soil Total Phosphorus Content in Coastal Areas Based on Hyperspectral Reflectance[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 517 Copy Citation Text

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Fig. 1. Sampleing location

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Fig. 2. Scatter plot of determination coefficients for partial least square regression calibration and prediction

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土壤属性	最大值/ (g·kg^-1)	最小值/ (g·kg^-1)	平均值/ (g·kg^-1)	标准差/ (g·kg^-1)	变异系数/ %	峰值K	偏度S
TP	1.30	0.22	0.68	0.19	28.02	1.01	2.19
SOM	52.56	1.39	9.18	8.74	95.21	6.72	0.61

Table 1. Statistics of soil TP and SOM contents

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统计参数	R		SNV		MSC		FD		SD		RL		CR
统计参数	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集
最小值/(g·kg^-1)	0.22	0.46	0.22	0.51	0.22	0.51	0.22	0.47	0.22	0.44	0.22	0.50	0.22	0.46
最大值/(g·kg^-1)	1.30	1.28	1.30	1.14	1.30	1.14	1.30	1.14	1.30	1.14	1.30	1.28	1.30	1.14
平均值/(g·kg^-1)	0.67	0.76	0.68	0.72	0.68	0.72	0.68	0.72	0.69	0.67	0.67	0.74	0.68	0.71
标准差/(g·kg^-1)	0.19	0.17	0.20	0.13	0.20	0.13	0.20	0.13	0.20	0.17	0.20	0.17	0.20	0.16
变异系数/%	29.11	22.19	30.26	17.99	30.26	17.99	30.16	18.63	28.83	25.15	29.33	22.36	29.48	22.43

Table 2. Sample set partitioning based on KS

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统计参数	R		SNV		MSC		FD		SD		RL		CR
统计参数	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集	建模集	预测集
最小值/(g·kg^-1)	0.22	0.49	0.22	0.45	0.22	0.45	0.22	0.50	0.22	0.47	0.22	0.49	0.22	0.46
最大值/(g·kg^-1)	1.30	0.84	1.30	0.84	1.30	0.83	1.30	0.83	1.30	0.84	1.30	0.84	1.30	1.08
平均值/(g·kg^-1)	0.69	0.68	0.68	0.70	0.68	0.70	0.68	0.72	0.69	0.66	0.69	0.68	0.68	0.69
标准差/(g·kg^-1)	0.21	0.13	0.21	0.11	0.21	0.11	0.21	0.10	0.21	0.12	0.21	0.12	0.20	0.14
变异系数/%	30.01	18.61	30.58	15.57	30.57	15.67	30.89	13.50	29.91	17.82	30.09	18.07	29.83	20.20

Table 3. Sample set partitioning based on SPXP

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Partitioning methods		RMSE_c	$R_{c}^{2}$	RMSE_p	$R_{p}^{2}$	RPD	LVs
	Min	0.09	0.29	0.07	0.01	1.02	3
RS	Max	0.16	0.78	0.19	0.82	2.40	13
	Mean	0.11	0.67	0.13	0.51	1.49	9.14
KS		0.11	0.67	0.13	0.42	1.34	9
SPXY		0.11	0.69	0.11	0.26	1.19	9

Table 4. Statistics of partial least square regression

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Partitioning methods		RMSE_c	$R_{c}^{2}$	RMSE_p	$R_{p}^{2}$	RPD
	Min	0.06	0.73	0.10	0.00	0.86
RS	Max	0.11	0.92	0.27	0.61	1.42
	Mean	0.09	0.84	0.17	0.20	1.11
KS		0.11	0.80	0.16	0.41	1.09
SPXY		0.10	0.83	0.09	0.46	1.38

Table 5. Statistics of support vector machine

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光谱变换	RS			KS			SPXY
光谱变换	$R_{c}^{2}$	$R_{p}^{2}$	LVs	$R_{c}^{2}$	$R_{p}^{2}$	LVs	$R_{c}^{2}$	$R_{p}^{2}$	LVs
R	0.67	0.50	9.13	0.67	0.42	9	0.69	0.26	9
SNV	0.64	0.47	7.12	0.67	0.28	7	0.63	0.23	6
MSC	0.66	0.48	7.94	0.68	0.29	8	0.67	0.33	8
FD	0.63	0.46	4.38	0.70	0.33	6	0.71	-0.40	6
SD	0.68	0.44	5.01	0.68	0.46	5	0.68	0.27	5
RL	0.67	0.48	8.53	0.66	0.59	9	0.70	0.12	9
CR	0.61	0.48	4.93	0.59	0.35	4	0.58	0.49	4

Table 6. PLSR calibration and prediction results of different spectral transformation methods and sample-set partitioning methods

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光谱变换	RS		KS		SPXY
光谱变换	$R_{c}^{2}$	$R_{p}^{2}$	$R_{c}^{2}$	$R_{p}^{2}$	$R_{c}^{2}$	$R_{p}^{2}$
R	0.84	0.20	0.80	0.41	0.83	0.46
SNV	0.89	0.37	0.89	0.55	0.84	0.49
MSC	0.89	0.37	0.89	0.56	0.88	0.79
FD	0.91	0.46	0.92	0.82	0.93	0.76
SD	0.90	0.43	0.90	0.45	0.92	0.78
RL	0.85	0.21	0.80	0.31	0.84	0.49
CR	0.90	0.44	0.90	0.69	0.90	0.71

Table 7. SVM calibration and prediction results of different spectral transformation methods and sample-set partitioning methods

Dan-ping WEI, Guang-hui ZHENG. Estimation of Soil Total Phosphorus Content in Coastal Areas Based on Hyperspectral Reflectance[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 517

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