Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment

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Abstract

Soil salinization is the most severe environmental risk in arid and semi-arid areas. The remote sensing method that constructs a characteristic space based on characteristic parameters provides an effective and economical tool and technique for the timely monitoring and inversion of soil salinization. Presently, the normalized difference vegetation index (NDVI) and the salinity index (SI) are mainly selected as the characteristic parameters for salinization inversion, while refined analysis and regional applicability are lacking. This study investigated Urad Front Banner in Inner Mongolia based on the Landsat8 OLI data. The ENDVI-SI3 characteristic space was constructed using the enhanced normalized difference vegetation index (ENDVI) that introduced the shortwave infrared band and the salinity index 3 (SI3) with the best inversion effect for semi-arid areas. Accordingly, the improved salinization monitoring index (ISMI) model was built. The results show that the correlation coefficient between ISMI and soil salt content was up to 0.82, and the inversion precision of the ISMI model was higher than that of NDVI, EDNVI, and SI3 (-0.66, -0.70, and 0.75, respectively). Based on the ISMI, this study achieved the quantitative inversion analysis and risk assessment of soil salinization in Urad Front Banner. This study provides an approach for selecting the optimal characteristic parameters of the characteristic space in the salinization inversion of semi-arid areas.

Keywords salinization remote sensing model ENDVI SI3 feature space risk assessment

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Issue Date: 27 December 2022

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	Siyuan ZHANG
	Chu YUE
	Guoli YUAN
	Shuai YUAN
	Wenqiang PANG
	Jun LI

Cite this article:

Siyuan ZHANG,Chu YUE,Guoli YUAN, et al. Salinization inversion model based on ENDVI-SI3 characteristic space and risk assessment[J]. Remote Sensing for Natural Resources, 2022, 34(4): 136-143.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021349 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/136

Fig.1 Geographical position of Urad Front Banner

Fig.2 ENDVI-SI3 two-dimensional scatter plot

指数	全称	公式^①	相关系数
NDVI	归一化植被指数	$N D V I = N I R - R N I R + R$	-0.66
ENDVI	增强型归一化植被指数	$E N D V I = N I R - R + S W I R 2 N I R + R + S W I R 2$	-0.70
EVI	增强植被指数	$E V I = 2.5 N I R - R N I R + 6 R - 7.5 B + 1$	-0.54
EEVI	扩展型增强植被指数	$E E V I = 2.5 N I R - R + S W I R 2 N I R + 6 R - 7.5 B + 1$	-0.46
MSAVI	修正土壤调节植被指数	$M S A V I = 12 [(2 N I R - 1) - 2 (N I R + 1) - 8 (N I R - R)$	-0.67
SI	盐分指数	$S I = B R$	0.72
ESI	增强型盐分指数	$E S I 1 = B R + S W I R 1 S W I R 1$	0.73
SI3	盐分指数3	$S I 3 = G 2 + R 2$	0.75
ESI3	增强型盐分指数3	$E S I 3 = (G 2 + R 2 + S W I R) 2 S W I R 1$	0.64
S2	盐分指数	$S 2 = B - R B + R$	0.62
ES2	增强型盐分指数	$E S 2 = B - R + S W I R 2 B + R + S W I R 2$	-0.61
SI-T	盐分指数	$S I - T = R N I R × 100$	0.68
ESI-T	增强型盐分指数	$E S I - T = R N I R + S W I R 2$	0.73
NDSI	归一化盐分指数	$N D S I = R - N I R R + N I R$	0.43
ENDSI	增强型归一化盐分指数	$E N D S I = R - N I R R + N I R + S W I R 2$	0.61

Tab.1 Results of index correlation analysis

Tab.2 Classification of saline soil based on ISMI

Fig.3 Spatial distribution of saline soil in Urad Front Banner based on ISMI

变量	相关系数r	显著水平P	样本数	灰色关联度 $γ$	权重 $w$
镁离子/(g·kg^-1)	0.836	<0.001	66	0.909	0.126
含盐量/(g·kg^-1)	0.820	<0.001	66	0.917	0.127
氯离子/(g·kg^-1)	0.772	<0.001	66	0.897	0.124
钠离子/(g·kg^-1)	0.752	<0.001	66	0.884	0.123
硫酸根/(g·kg^-1)	0.635	<0.001	66	0.833	0.115
钙离子/(g·kg^-1)	0.524	<0.001	66	0.949	0.132
有机质/(g·kg^-1)	-0.327	0.007	66	0.924	0.128
有效磷/(mg·kg^-1)	0.289	0.019	66	0.900	0.125
钾离子/(g·kg^-1)	0.228	0.065	66	0.949	—
pH	0.212	0.087	66	0.958	—
全氮/(g·kg^-1)	-0.203	0.103	66	0.927	—
容重/(g·cm^-3)	-0.196	0.115	66	0.952	—
碳酸氢根/(g·kg^-1)	0.061	0.629	66	0.939	—

Tab.3 Correlation screening and weighting of salinization risk assessment factors

Fig.4 Functional relationship between SRAV and ISMI

Fig.5 Spatial distribution of soil salinization risk assessment in Urad Front Banner

Tab.4 Grading for risk assessment of soil salinization in Urad Front Banner

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