基于多光谱遥感的典型绿洲棉田春季土壤盐分反演及验证

doi:10.12118/j.issn.1000-6060.2021.477

Abstract

Abstract:

This study aimed to explore an effective method for extracting soil salinity from cotton fields in oasis and determine the characteristics and spatial distribution of soil salinization on a regional scale to provide reference for soil salinization control. The 31^st Regiment of the 2^nd Division of Xinjiang Production and Construction Corps was taken as the research area; Landsat 8 OLI multispectral images and field measurements of soil salinity in spring 2019 and 2021 were taken as data sources; and the band group, spectral index group, and total variable group were taken as the model input variable group. Multiple stepwise regression (MSR), partial least squares regression, extreme learning machine, support vector machine, and back propagation neural network (BPNN) were used to construct a remote sensing inversion model of soil salt based on the three input variable groups. Precision evaluation was conducted, and the effects of input variables and modeling methods on model accuracy were explored. The best inversion model of soil salt in spring was determined through comparison and quantitatively inverted surface soil salt content. Results showed that (1) the study area was mainly composed of non-salinized soil and slightly salinized soil, and the coefficient of variation of total samples was 0.67, implying moderate variability. The relationship between spectral reflectance and soil salinization is as follows: serious soil salinization leads to great spectral reflectance. (2) Significance tests were conducted on coastal band(b1), blue(b2), green(b3), and red(b4) and the salinity indices of SI1, SI2, SI3, SI4, S3, S4, and S5 (P<0.01). The obtained correlation coefficients were all above 0.4, which can represent soil salinity to a certain extent. (3) Among the six linear regression models, the MSR model established on the band group had the best inversion effect, and the BPNN inversion model established on the full variable group had the highest accuracy. (4) According to the inversion results, the soil in spring 2019 and 2021 was mainly nonsalinized soil accounting for 55.55% and 64.62% of the total tillage area, respectively, followed by mild salinized soil accounting for 44.31% and 35.17%, respectively. Soil salinization in 2021 was reduced compared with that in 2019.

Key words: multispectral remote sensing inversion, soil salinity, spectral reflectance, variable group, machine learning

LIU Xuhui,BAI Yungang,CHAI Zhongping,ZHANG Jianghui,DING Bangxin,JIANG Zhu. Inversion and validation of soil salinity based on multispectral remote sensing in typical oasis cotton field in spring[J].Arid Land Geography, 2022, 45(4): 1165-1175.

Figures/Tables 10

Fig. 1

Tab. 1

Spectral parameters and related formulas"

光谱参量		计算公式	参考文献
波段	b1	CA	[16]
	b2	B	[16]
	b3	G	[16]
	b4	R	[16]
	b5	NIR	[16]
	b6	SWIR1	[16]
	b7	SWIR2	[16]
盐分指数	NDSI	$R - N I R R + N I R$	[14]
	SI-T	$100 × R - N I R$	[14]
	SI1	$G × R$	[2]
	SI2	$G 2 + R 2 + N I R 2$	[2]
	SI3	$G 2 + R 2$	[2]
	SI4	$B - G 2 + G - R 2$	[10]
	S1	$B / R$	[2]
	S2	$B - R / B + R$	[2]
	S3	$G × R / B$	[2]
	S4	$B × R$	[2]
	S5	$B × R / G$	[2]
	S6	$R × N I R / G$	[2]

Tab. 1

Tab. 2

Fig. 2

Tab. 3

Tab. 4

Tab. 5

Fig. 3

Fig. 4

Tab. 6

References 37

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数据集	样本数/个				土壤含盐量/g·kg^-1			标准差	变异系数
数据集	总样本	非盐化土	轻度盐化土	中度盐化土	最小值	最大值	平均值	标准差	变异系数
总样本	77	50	21	6	0.37	11.82	3.02	2.03	0.67
建模集	51	33	14	4	0.42	11.82	3.05	2.07	0.68
验证集	26	17	7	2	0.37	9.64	2.97	1.95	0.66

变量组	建模方法	表达式	建模集（n=51）		验证集（n=26）
变量组	建模方法	表达式	R²	RMSE	R²	RMSE
波段组	MSR	Y=624.08b1-440.71b2-29.42	0.411	1.588	0.473	1.414
波段组	PLSR	Y=40.75b1+35.51b2+25.32b3+19.94b4-21.19	0.314	1.713	0.496	1.382
光谱指数组	MSR	Y=-94.05SI2+170.77SI4+246.21S5-14.30	0.341	1.679	0.377	1.544
光谱指数组	PLSR	Y=13.32SI1+6.02SI2+9.3SI3+26.55SI4+7.86S3+16S4+16.58S5-16.74	0.269	1.768	0.449	1.447
全变量组	MSR	Y=592.81b1-318.31b3-75.19SI2+462.30SI4-23.33	0.439	1.549	0.359	1.562
全变量组	PLSR	Y=15.29b1+13.33b2+9.5b3+7.48b4+8.53SI1+3.86SI2+5.96SI3+17.01SI4+5.03S3+10.25S4+10.62S5-18.73	0.290	1.742	0.470	1.418

变量组	建模方法	建模集（n=51）		验证集（n=26）
变量组	建模方法	R²	RMSE	R²	RMSE
波段组	ELM	0.617	1.279	0.533	1.998
	SVM	0.782	0.985	0.476	1.996
	BPNN	0.736	1.067	0.538	1.460
光谱指数组	ELM	0.621	1.274	0.508	1.986
	SVM	0.769	0.997	0.409	2.001
	BPNN	0.730	1.080	0.459	1.567
全变量组	ELM	0.540	1.403	0.490	1.565
	SVM	0.533	1.515	0.488	1.658
	BPNN	0.705	1.133	0.556	1.409

土壤类型	土壤含盐量/g·kg^-1	2019年		2021年
土壤类型	土壤含盐量/g·kg^-1	像元个数	面积占比/%	像元个数	面积占比/%
非盐化土（0~3 g·kg^-1）	0~1	4508	0.35	14	0.00
	1~2	92995	7.17	366240	28.25
	2~3	622671	48.03	471548	36.37
轻度盐化土（3~6 g·kg^-1）	3~4	428918	33.08	398125	30.71
	4~5	128548	9.92	47257	3.65
	5~6	16989	1.31	10556	0.81
中度盐化土（6~10 g·kg^-1）	6~10	1848	0.14	2737	0.21

Inversion and validation of soil salinity based on multispectral remote sensing in typical oasis cotton field in spring

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光谱参量		相关系数（r）
波段	b1	0.591^**
	b2	0.550^**
	b3	0.539^**
	b4	0.525^**
	b5	0.297
	b6	0.196
	b7	0.329
盐分指数	NDSI	0.304
	SI-T	0.197
	SI1	0.535^**
	SI2	0.452^**
	SI3	0.533^**
	SI4	0.416^**
	S1	-0.162
	S2	-0.163
	S3	0.497^**
	S4	0.545^**
	S5	0.540^**
	S6	0.269

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