基于光谱指数建模的沙井子灌区土壤盐分反演

doi:10.12118/j.issn.1000-6060.2023.584

Abstract

Abstract:

This study aims to rapidly and accurately obtain surface soil salinity information in arid regions. To achieve this, Shajingzi irrigation district, Aksu Prefecture, Xinjiang, China was taken as the research area, ground-collected soil salinity data at depths of 0-10 cm and 10-20 cm were used along with corresponding Landsat 9 OLI remote-sensing images to extract band reflectance values. This led to the creation of two- and three-band spectral indices, which resulted in the development of four remote-sensing salt detection indices (SDI1, SDI2, SDI3, and SDI4) under conditions of low vegetation cover. These four indices were then evaluated for their accuracy in estimating soil salinity at different depths. The results showed that: (1) The classification accuracies of the four indices were 73.56%, 66.35%, 43.75%, and 74.52%, respectively, for a soil depth of 0-10 cm and 61.06%, 62.50%, 66.35%, and 64.42%, respectively, for a soil depth of 10-20 cm. These findings suggest that the optimal inversion depth for soil layers in the irrigation district is 0-10 cm. (2) Among the four indices, SDI4, which was constructed with a three-band spectrum, outperformed the others, which are two-band spectrum-based. SDI4 effectively estimates the degree of soil salinization in the Shajingzi irrigation district, providing valuable technical references for managing and preventing soil salinization in irrigated areas.

Key words: soil salinization, remote sensing monitoring, spectral index, Shajingzi irrigation district

XIE Junbo, WANG Xingpeng, HE Shuai, LIU Yang, ZHONG Zhibo, LI Yan, HONG Guojun. Soil salinity inversion in the Shajingzi irrigation district based on spectral index modeling[J].Arid Land Geography, 2024, 47(7): 1199-1209.

Figures/Tables 11

Fig. 1

Tab. 1

Band combination and spectral index"

波段组合类型	光谱指数	公式
两波段光谱指数（2DVI）	盐分指数（SI）	$B × R$
	差值环境植被指数（DVI）	$N I R - R$
	土壤调解植被指数（SAVI）	$N I R - R × 1.5 N I R$
	修改型土壤调节植被指数（MSAVI）	$2 × N I R - 1 + 2 × N I R + 1 2 - 8 × N I R - R 2 2$
三波段光谱指数（3DVI）	绿蓝波段归一化差值植被指数（GBNDVI）	$N I R - R - B N I R + R + B$

Tab. 1

Fig. 2

Tab. 2

Scatterplot fitting of the spectral indices"

二维散点图	光谱指数拟合公式	拟合度（R²）
SI-GBNDVI	线性：y= -1.0585x+0.2001	0.7984
	二次：y= -2.7603x²-0.7608x+0.1787	0.9352
	指数：y=0.1915e^3.724^x	0.9118
	对数：y= -0.3572 $-$ 0.2747logx+0.1978logx²	0.7676
	几何：y= -2.772x^0.0776+2.3768	0.7488
SI-SAVI	线性：y= -2.9893x+0.5704	0.7967
	二次：y=14.767x²-5.7517x+0.6786	0.8372
	指数：y=0.69e^-10.31^x	0.8270
	对数：y= -0.039-0.1694logx-0.0016logx²	0.5624
	几何：y= -0.4402x^0.2033+0.3955	0.5598
SI-DVI	线性：y= -3.5664x+0.5468	0.4582
	二次：y=35.913x²-9.1993x-0.7483	0.5471
	指数：y=0.609e^-11.76^x	0.5253
	对数：y= -0.0489-0.2178logx-0.0885logx²	0.5205
	几何：y= -0.1537x^0.9426+0.0924	0.5285
SI-MSAVI	线性：y= -3.2091x-2.636	0.7372
	二次：y=18.488x²+30.464x+12.675	0.7888
	指数：y=0.316e^-10.99^x	0.7767
	对数：y= -1.041-0.1793logx-0.0228logx²	0.5473
	几何：y= -0.328x^0.2764-0.717	0.5462

Tab. 2

Fig. 3

Tab. 3

Models of salinization detection"

盐渍化监测模型	公式
SDI1模型	$S I 2 + M S A V I - 1 2$
SDI2模型	$S I 2 + D V I - 1 2$
SDI3模型	$S I 2 + S A V I - 1 2$
SDI4模型	$S I 2 + G B N D V I - 1 2$

Tab. 3

Tab. 4

Fig. 5

Tab. 5

Tab. 6

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盐渍化监测模型	4个模型的值域区间
盐渍化监测模型	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐渍土
SDI1模型	1.71~1.90	1.90~1.95	1.95~2.02	2.02~2.12	2.12~2.26
SDI2模型	0.78~0.96	0.96~1.03	1.03~1.13	1.13~1.25	1.25~1.44
SDI3模型	0.70~0.92	0.92~0.97	0.97~1.06	1.06~1.21	1.21~1.42
SDI4模型	0.38~0.90	0.90~1.09	1.09~1.20	1.20~1.37	1.37~1.65

Soil salinity inversion in the Shajingzi irrigation district based on spectral index modeling

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Figures/Tables 11

References 35

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盐渍化监测模型	正确分类样本点个数	错误分类样本点个数						总体精度/%
盐渍化监测模型	正确分类样本点个数	非盐渍土	轻度盐渍土	中度盐渍土	重度盐渍土	盐渍土	共计	总体精度/%
SDI1	144	13	33	5	5	8	64	73.56
SDI2	138	9	40	6	3	12	70	66.35
SDI3	91	0	76	16	22	0	117	43.75
SDI4	155	13	25	5	4	6	53	74.52

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