孔雀河灌区土壤盐分遥感反演模型及空间分布特征

张冲; 孙志坚; 彭博锐; 刘延锋

doi:10.19509/j.cnki.dzkq.tb20250185

孔雀河灌区土壤盐分遥感反演模型及空间分布特征

doi: 10.19509/j.cnki.dzkq.tb20250185

1.
中国地质大学（武汉）环境学院，武汉 430078
2.
中国地质科学院水文地质环境地质研究所，石家庄 050061

基金项目: 国家自然科学基金项目（42272306）

详细信息

作者简介:
张冲：E-mail：20181004366@cug.edu.cn

通讯作者:
E-mail：liuyf@cug.edu.cn

中图分类号: S151.9；X53
计量
- 文章访问数: 18
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-23
- 录用日期: 2025-06-30
- 修回日期: 2025-06-26
- 网络出版日期: 2025-06-30

Remote sensing inversion model and spatial distribution characteristics of soil salinity in the Kongque River irrigation area

1.
School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430078, China
2.
The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China

More Information

Author Bio:
E-mail：20181004366@cug.edu.cn

Corresponding author: E-mail：liuyf@cug.edu.cn

摘要

摘要:
干旱地区土壤盐渍化问题突出，快速精准监测土壤盐分对区域生态保护与农业可持续发展至关重要。为提高干旱区土壤盐分遥感反演精度，以孔雀河灌区为研究区，结合实地调查点位采集土壤样品，基于ASD FieldSpec 4地物光谱仪实测土壤高光谱数据校正Landsat 8卫星遥感数据，利用校正后的光谱指数通过随机森林算法构建土壤盐分遥感反演模型来估计孔雀河灌区表层土壤盐分。结果表明：土壤光谱反射率随盐渍化程度加重呈递增趋势；经ASD高光谱校正后部分盐分指数与土壤盐分相关性显著提高；随机森林算法构建土壤盐分遥感反演模型，建模集R²为0.847，验证集R²为0.713，比原始数据建模集和验证集的R²显著提高；孔雀河灌区西部土壤盐渍化最严重，自WS向EN土壤盐渍化程度逐渐减小，南部地区以轻、中度盐渍土为主，中、北部地区主要为非盐渍土。基于高光谱校正的随机森林反演模型精度良好，可为孔雀河灌区及同类干旱区土壤盐渍化动态监测提供可靠技术支撑。
- 土壤盐分 /
- 遥感反演 /
- 地质光谱仪ASD /
- 高光谱 /
- 光谱指数 /
- 随机森林 /
- 孔雀河灌区 /
- 土壤盐渍化
Abstract:
Objective
Soil salinization is a prominent environmental issue in arid regions, and rapid and accurate monitoring of soil salinity is critical for regional ecological conservation and sustainable agricultural development.
Methods
To improve the accuracy of remote sensing inversion of soil salinity in arid regions, the Kongque River irrigation area was selected as the study area. Soil samples were collected from field survey points. Based on measured soil hyperspectral data acquired with the ASD FieldSpec 4 spectroradiometer, Landsat 8 satellite remote sensing data were calibrated, and the calibrated spectral indices were then used to construct a soil salinity remote sensing inversion model with the random forest algorithm to estimate surface soil salinity in the Kongque River irrigation area.
Results
The results showed that soil spectral reflectance increased with increasing salinization degree. After ASD hyperspectral calibration, the correlations between some salinity indices and soil salinity were significantly improved. The random forest was used to construct a remote sensing inversion model for soil salinity. The R² was 0.847 for the training set and 0.713 for the validation set, both significantly higher than the R² values of the training and validation sets obtained from the original data. Soil salinization was most severe in the western part of the Kongque River irrigation area and gradually decreased from southwest to northeast. Slightly and moderately saline soils were mainly distributed in the southern part, while non-saline soils were mainly distributed in the central and northern parts.
Conclusion
The hyperspectrally calibrated random forest inversion model shows good accuracy and can provide reliable technical support for dynamic monitoring of soil salinization in the Kongque River irrigation area and similar arid regions.
- soil salinity /
- remote sensing inversion /
- ASD /
- hyperspectral /
- spectral index /
- random forest /
- Kongque River irrigation area the Xinjiang Uygur Autonomous Region /
- soil salinization

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图 1 孔雀河灌区野外调查点图

Figure 1. Distribution of field survey points in Kongque River irrigation area

下载: 全尺寸图片幻灯片

图 2 土壤电导率EC_1:5与EC_FS关系图

Figure 2. Relationship between EC_1:5 and EC_FS

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图 3 不同盐渍化程度土壤反射率

Figure 3. Soil reflectance under different salinization degrees

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图 4 R_ASD与Landsat 8卫星B₅波段反射率($R_{{\mathrm{L}}{\text{-}}{\mathrm{B}}_5} $)散点图

$R_{{\mathrm{ASD}}{\text{-}}{\mathrm{B}}_5} $. ASD重采样后对应的Landsat 8波段B₅反射率

Figure 4. Scatter plot of B₅-band reflectance for R_ASD and Landsat 8

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图 5 土壤遥感反演模型(a)与克里金插值(b)反演土壤盐渍化

Figure 5. Soil salinization maps derived from soil remote sensing inversion model (a) and Kriging interpolation (b)

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表 1 盐分、植被指数

Table 1. Salinity and vegetation indices

		计算公式	参考文献
盐分指数	BI	(B₄²+B₅²)^0.5	[30]
	NDSI	(B₄−B₅)/(B₄+B₅)
	SI	(B₂B₄)^0.5
	SI₁₁	B₆/B₇	[31]
	ASTER	(B₆−B₇)/(B₆+B₇)
	IS-VIR	2B₃−(B₄+B₅)
	S₁	B₂/B₄	[32]
	S₂	(B₂−B₄)/(B₂+B₄)
	S₃	B₃B₄/B₂
	S₄	(B₂B₄)^0.5
	S₅	B₂B₄/B₃
	S₆	B₄B₅/B₃
	SI₁	(B₃B₄)^0.5	[33]
	SI₂	(B₃²+B₄²+B₅²)^0.5
	SI₃	(B₄²+B₃²)^0.5
	INT₁	(B₃₊B₄)/2
	INT₂	(B₃₊B₄+B₅)/2
植被指数	SRI	B₅/B₄	[34]
	NDVI	(B₅−B₄)/(B₅+B₄)
	NDWI	(B₃−B₅)/(B₃+B₅)
	EVI	2.5[(B₅−B₄)/(B₅+6B₄−7.5B₂+1)]	[35]
	DVI	B₅−B₄
	TVI	0.5[(120(B₅−B₃)-200(B₄−B₃)]
	SRVI	(1+L) (B₅−B₄)/(B₅+B₄+L)
	MSAVI	0.5{2B₅−1-[(2B₅+1)2-8(B₅−B₄)]^0.5}	[36]
	ARVI	[B₅−(2B₄−B₂)]/(B₅+2B₄−B₂)	[37]
	GRVI	B₅/B₃	[38]
	ENDVI	(B₅+B₇−B₄)/(B₅+B₇+B₄)	[39]
	ERVI	(B₅+B₇)/B₄
	EDVI	B₅+B₆−B₄
	GNDVI	(B₅−B₃)/(B₅+B₃)	[40]
	GDVI	(B₅²−B₄²)/(B₅²+B₄²)	[41]
	NLI	(B₅²−B₄)/(B₅²+B₄)
	OSAVI	1.16(B₅−B₄)/(B₅+B₄+0.16)
注：BI. 亮度指数；NDSI. 归一化盐度指数；ASTER.ASTER. 盐度指数；IS-VIR. 可见光−红外盐度指数；SI，SI₁₁，S₁，S₂，S₃，S₄，S₅，S₆，SI₁，SI₂，SI₃. 盐度指数；INT₁，INT₂. 交互指数；SRI. 盐度比值指数；NDVI. 归一化植被指数；NDWI. 归一化水体指数；EVI. 增强型植被指数；DVI. 差值植被指数；TVI. 三角植被指数；SRVI. 土壤调整植被指数；MSAVI. 修正型土壤调整植被指数；ARVI. 大气阻抗植被指数；GRVI. 绿度比值植被指数；ENDVI. 增强型归一化差异植被指数；ERVI. 增强型比值植被指数；EDVI. 增强型差值植被指数；GNDVI. 绿度归一化植被指数；GDVI. 绿度差值植被指数；NLI. 归一化叶面积指数；OSAVI. 优化土壤调整植被指数；B₂，B₃，B₄，B₅，B₆，B₇分别为Landsat 8卫星波段反射率；L为土壤调节因素，一般取0.5；下同

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表 2 土壤盐分与盐分指数和植被指数皮尔逊相关性

Table 2. Pearson correlation of soil salinity with salinity indices and vegetation indices

盐分指数	相关系数	植被指数	相关系数
BI	0.014	SRI	−0.138
SI	0.082	NDVI	−0.183
SI₁	0.098	EVI	−0.209^*
SI₂	0.033	DVI	−0.206^*
SI₃	0.101	TVI	−0.221^*
S₁	−0.266^**	SRVI	−0.195
S₂	−0.270^**	MSAVI	−0.193
S₃	0.155	ARVI	−0.209^*
S₄	0.099	NDWI	0.149
S₅	0.086	GRVI	−0.127
S₆	−0.002	ENDVI	−0.102
NDSI	0.183	ERVI	−0.102
SI₁₁	−0.185	EDVI	−0.067
ASTER	−0.202^*	GNDVI	−0.149
IS-VIR	0.108	GDVI	−0.202
INT₁	0.099	NLI	−0.184
INT₂	0.051	OSAVI	−0.189
注：表示显著性检验p<0.05；*表示p<0.01。

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表 3 R_ASD，Landsat 8波段与土壤盐分的皮尔逊相关性

Table 3. Pearson correlation of soil salinity with R_ASD and Landsat 8 bands

波段来源	B₂	B₃	B₄	B₅	B₆	B₇
Landsat 8	0.038	0.075	0.128	−0.094	0.132	0.185
R_ASD	−0.252	−0.241	−0.244	−0.313^*	−0.382^**	−0.383^**
注：表示显著性检验p<0.05；*表示p<0.01；R_ASD. ASD所测反射率；下同

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表 4 R_ASD与Landsat 8各波段皮尔逊相关性

Table 4. Pearson correlation between R_ASD and Landsat 8 bands

波段	B₂	B₃	B₄	B₅	B₆	B₇
皮尔逊相关性	0.168	0.223	0.206	0.545^**	0.140	0.067

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表 5 校正前后部分盐分指数皮尔逊相关系数

Table 5. Pearson correlation coefficients of selected salinity indices before and after correction

盐分指数	校正前	校正后
BI	0.014	0.192
SI₂	0.033	0.184
S₆	−0.002	0.225^*
IS-VIR	0.108	−0.190
NDSI	0.183	−0.083
INT₂	0.051	0.182

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表 6 土壤盐分遥感反演模型精度统计表

Table 6. Accuracy statistics of remote sensing inversion models for soil salinity

建模指数	建模集		验证集
建模指数	R²	RMSE/(dS·m⁻¹)	R²	RMSE/(dS·m⁻¹)	RPD
EVI，DVI，TVI，ARVI，S₁，S₂，ASTER	0.833	1.703	0.528	2.812	1,496
EVI，DVI，TVI，ARVI，S₁，S₂，S₆，ASTER	0.847	1.630	0.713	2.246	1.873
注：R². 决定系数；RMSE. 均方根误差；RPD. 相对分析误差

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