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: 128
- PDF下载量: 23
- 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，以新疆维吾尔自治区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SW向NE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 /
- 地物光谱仪ASD /
- 高光谱校正 /
- 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 of the Xinjiang Uygur Autonomous Region 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 calibration /
- spectral index /
- random forest /
- Kongque River irrigation area /
- soil salinization

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图 1 孔雀河灌区野外调查点

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

下载: 全尺寸图片幻灯片

图 2 研究区采样点土壤电导率EC_1:5与EC_FS关系

EC_FS. 点位2265FS测量的5～30 cm电导率

Figure 2. Relationship between EC_1:5 and EC_FS of sampling point in the study area

下载: 全尺寸图片幻灯片

图 3 研究区不同盐渍化程度土壤的反射率

Figure 3. Soil reflectance under different salinization degrees in the study area

下载: 全尺寸图片幻灯片

图 4 研究区R_ASD与$R_{{\mathrm{L}}{\text{-}}{\mathrm{B}}_5} $散点图

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

Figure 4. Scatter plot of $R_{{\mathrm{ASD}}{\text{-}}{\mathrm{B}}_5} $ and $R_{{\mathrm{L}}{\text{-}}{\mathrm{B}}_{5}} $ in the study area

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图 5 土壤遥感反演模型(a)与克里金插值法反演(b)土壤盐渍化分布

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

下载: 全尺寸图片幻灯片

表 1 盐分、植被指数

Table 1. Salinity and vegetation indices

盐分、植被指数		计算公式	参考文献
盐分指数	亮度指数(BI)	$ (R_{{\mathrm{B}}_4}^2 +R_{{\mathrm{B}}_5}^2)^{0.5} $	[30]
	归一化盐分指数(NDSI)	$(R_{{\mathrm{B}}_4}- R_{{\mathrm{B}}_5})/(R_{{\mathrm{B}}_4}+R_{{\mathrm{B}}_5})$
	盐分指数(SI)	$(R_{{\mathrm{B}}_2}R_{{\mathrm{B}}_4})^{0.5} $
	盐分指数11(SI₁₁)	$R_{{\mathrm{B}}_6}/R_{{\mathrm{B}}_7} $	[31]
	ASTER盐分指数(ASTER)	$(R_{{\mathrm{B}}_6}-R_{{\mathrm{B}}_7})/(R_{{\mathrm{B}}_6}+R_{{\mathrm{B}}_7}) $
	可见光−红外盐分指数(IS-VIR)	$2R_{{\mathrm{B}}_3}-(R_{{\mathrm{B}}_4}+R_{{\mathrm{B}}_5}) $
	S₁盐分指数(S₁)	$R_{{\mathrm{B}}_2}/R_{{\mathrm{B}}_4} $	[32]
	S₂盐分指数(S₂)	$(R_{{\mathrm{B}}_2}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_2}+R_{{\mathrm{B}}_4}) $
	S₃盐分指数(S₃)	$R_{{\mathrm{B}}_3}R_{{\mathrm{B}}_4}/R_{{\mathrm{B}}_2} $
	S₄盐分指数(S₄)	$(R_{{\mathrm{B}}_2}R_{{\mathrm{B}}_4})^{0.5} $
	S₅盐分指数(S₅)	$R_{{\mathrm{B}}_2}R_{{\mathrm{B}}_4}/R_{{\mathrm{B}}_3} $
	S₆盐分指数(S₆)	$R_{{\mathrm{B}}_4}R_{{\mathrm{B}}_5}/R_{{\mathrm{B}}_3} $
	SI₁盐分指数(SI₁)	$(R_{{\mathrm{B}}_3}R_{{\mathrm{B}}_4})^{0.5} $	[33]
	SI₂盐分指数(SI₂)	$(R_{{\mathrm{B}}_3}^2+R_{{\mathrm{B}}_4}^2+R_{{\mathrm{B}}_5}^2)^{0.5} $
	SI₃盐分指数(SI₃)	$(R_{{\mathrm{B}}_4}^2+R_{{\mathrm{B}}_3}^2) ^{0.5}$
	强度指数1(INT₁)	$(R_{{\mathrm{B}}_3}+R_{{\mathrm{B}}_4})/2 $
	强度指数2(INT₂)	$(R_{{\mathrm{B}}_3}+R_{{\mathrm{B}}_4}+R_{{\mathrm{B}}_5}) /2$
植被指数	简单比值植被指数(SRI)	$R_{{\mathrm{B}}_5} /R_{{\mathrm{B}}_4}$	[34]
	归一化植被指数(NDVI)	$(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_4}) $
	归一化水体指数(NDWI)	$(R_{{\mathrm{B}}_3}-R_{{\mathrm{B}}_5}) /(R_{{\mathrm{B}}_3}+R_{{\mathrm{B}}_5})$
	增强型植被指数(EVI)	$2.5[(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}+6R_{{\mathrm{B}}_4}-7.5R_{{\mathrm{B}}_2}+1)] $	[35]
	差值植被指数(DVI)	$R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4} $
	三角植被指数(TVI)	$0.5[120(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_3})-200(R_{{\mathrm{B}}_4}-R_{{\mathrm{B}}_3})] $
	土壤调节植被指数(SAVI)	$(1+{L})(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_4}+{L}) $
	修正型土壤调节植被指数(MSAVI)	$0.5[2R_{{\mathrm{B}}_5}+1-\sqrt{(2R_{{\mathrm{B}}_5}+1)^2-8(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4}})] $	[36]
	大气阻抗植被指数(ARVI)	$[R_{{\mathrm{B}}_5}-(2R_{{\mathrm{B}}_4}-R_{{\mathrm{B}}_2}) ]/(R_{{\mathrm{B}}_5}+2R_{{\mathrm{B}}_4}-R_{{\mathrm{B}}_2})$	[37]
	绿度比值植被指数(GRVI)	$R_{{\mathrm{B}}_5}/R_{{\mathrm{B}}_3} $	[38]
	增强型归一化植被指数(ENDVI)	$(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_7}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_7}+R_{{\mathrm{B}}_4}) $	[39]
	增强型比值植被指数(ERVI)	$(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_7})/R_{{\mathrm{B}}_4} $
	增强型差值植被指数(EDVI)	$R_{{\mathrm{B}}_5} +R_{{\mathrm{B}}_6}-R_{{\mathrm{B}}_4}$
	绿度归一化植被指数(GNDVI)	$(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_3})/(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_3}) $	[40]
	广义差分植被指数(GDVI)	$(R_{{\mathrm{B}}_5}^2-R_{{\mathrm{B}}_4}^2)/(R_{{\mathrm{B}}_5}^2+R_{{\mathrm{B}}_4}^2) $	[41]
	非线性植被指数(NLI)	$(R_{{\mathrm{B}}_5}^2-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}^2+R_{{\mathrm{B}}_4}) $
	优化土壤调节植被指数(OSAVI)	$1.16(R_{{\mathrm{B}}_5}-R_{{\mathrm{B}}_4})/(R_{{\mathrm{B}}_5}+R_{{\mathrm{B}}_4}+0.16) $
注：$R_{{\mathrm{B}}_2}\sim R_{{\mathrm{B}}_7} $分别为Landsat 8卫星B₂～B₇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 研究区土壤盐分与盐分指数、植被指数的皮尔逊相关性

Table 2. Pearson correlation of soil salinity with salinity indices and vegetation indices in the study area

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**	SAVI	−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；下同

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表 3 研究区Landsat 8波段反射率，R_ASD与土壤盐分的皮尔逊相关系数

Table 3. Pearson correlation coefficient of soil salinity with Landsat 8 bands reflectance data and R_ASD in the study area

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：R_ASD. ASD所测反射率；下同

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表 4 研究区R_ASD与Landsat 8波段反射率的皮尔逊相关系数

Table 4. Pearson correlation coefficient between R_ASD and Landsat 8 bands reflectance data in the study area

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的皮尔逊相关系数

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

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

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. 电导率均方根误差；RPD. 相对分析误差

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参考文献(46)

[1]	李建国, 濮励杰, 朱明, 等. 土壤盐渍化研究现状及未来研究热点[J]. 地理学报, 2012, 67(9): 1233-1245. LI J G, PU L J, ZHU M, et al. The present situation and hot issues in the salt-affected soil research[J]. Acta Geographica Sinica, 2012, 67(9): 1233-1245. (in Chinese with English abstract)
[2]	SINGH A. Soil salinization management for sustainable development: A review[J]. Journal of Environmental Management, 2021, 277: 111383. doi: 10.1016/j.jenvman.2020.111383
[3]	杨劲松, 姚荣江, 王相平, 等. 中国盐渍土研究: 历程、现状与展望[J]. 土壤学报, 2022, 59(1): 10-27. doi: 10.11766/trxb202110270578 YANG J S, YAO R J, WANG X P, et al. Research on salt-affected soils in China: History, status quo and prospect[J]. Acta Pedologica Sinica, 2022, 59(1): 10-27. (in Chinese with English abstract) doi: 10.11766/trxb202110270578
[4]	SMITH P, POCH R M, LOBB D A, et al. Status of the world's soils[J]. Annual Review of Environment and Resources, 2024, 49: 73-104. doi: 10.1146/annurev-environ-030323-075629
[5]	DARVISHZADEH R, ATZBERGER C, SKIDMORE A K, et al. Leaf area index derivation from hyperspectral vegetation indicesand the red edge position[J]. International Journal of Remote Sensing, 2009, 30(23): 6199-6218. doi: 10.1080/01431160902842342
[6]	刘新, 郝媛媛, 花立民. 石羊河下游民勤盆地土壤盐分空间分异特征[J]. 干旱区研究, 2023, 40(10): 1615-1624. doi: 10.13866/j.azr.2023.10.08 LIU X, HAO Y Y, HUA L M. Spatial differentiation characteristics of soil salinity in Minqin Basin, downstream of Shiyang River, China[J]. Arid Zone Research, 2023, 40(10): 1615-1624. (in Chinese with English abstract) doi: 10.13866/j.azr.2023.10.08
[7]	王敬哲, 丁建丽, 葛翔宇, 等. 基于星地传感技术的土壤盐渍化监测进展与展望[J]. 遥感学报, 2024, 28(9): 2187-2208. WANG J Z, DING J L, GE X Y, et al. Monitoring soil salinization on the basis of remote sensing and proximal soil sensing: Progress and perspective[J]. National Remote Sensing Bulletin, 2024, 28(9): 2187-2208. (in Chinese with English abstract)
[8]	杨宁, 崔文轩, 张智韬, 等. 无人机多光谱遥感反演不同深度土壤盐分[J]. 农业工程学报, 2020, 36(22): 13-21. doi: 10.11975/j.issn.1002-6819.2020.22.002 YANG N, CUI W X, ZHANG Z T, et al. Soil salinity inversion at different depths using improved spectral index with UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(22): 13-21. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2020.22.002
[9]	SHOKRI N, HASSANI A, SAHIMI M. Multi-scale soil salinization dynamics from global to pore scale: A review[J]. Reviews of Geophysics, 2024, 62(4): e2023RG000804. doi: 10.1029/2023RG000804
[10]	刘福全, 杜崇, 韩旭, 等. 基于3S技术的大庆地区土壤盐渍化时空变化分析[J]. 灌溉排水学报, 2022, 41(5): 132-138. doi: 10.13522/j.cnki.ggps.2021494 LIU F Q, DU C, HAN X, et al. Spatiotemporal variation in soil salinization in Daqing calculated using the 3S technology[J]. Journal of Irrigation and Drainage, 2022, 41(5): 132-138. (in Chinese with English abstract) doi: 10.13522/j.cnki.ggps.2021494
[11]	谭世林, 胡雪菲, 岳胜如. 塔里木河流域绿洲土壤盐分遥感反演模型研究[J]. 测绘与空间地理信息, 2022, 45(6): 78-81. doi: 10.3969/j.issn.1672-5867.2022.06.023 TAN S L, HU X F, YUE S R. Remote sensing retrieval model of soil salinity in oasis of Tarim River Basin[J]. Geomatics & Spatial Information Technology, 2022, 45(6): 78-81. (in Chinese with English abstract) doi: 10.3969/j.issn.1672-5867.2022.06.023
[12]	付萍杰, 补沅坤, 马池杰, 等. 基于GEE的黄河三角洲地区土壤含盐量时空反演[J]. 测绘通报, 2024(11): 13-20. doi: 10.13474/j.cnki.11-2246.2024.1103 FU P J, BU Y K, MA C J, et al. Spatio-temporal inversion of soil salinity in the Yellow River Delta region based on GEE[J]. Bulletin of Surveying and Mapping, 2024(11): 13-20. (in Chinese with English abstract) doi: 10.13474/j.cnki.11-2246.2024.1103
[13]	YE H J, TANG S L, BAI Y, et al. Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea[J]. Regional Studies in Marine Science, 2024, 77: 103684. doi: 10.1016/j.rsma.2024.103684
[14]	HE Y J, YIN H Y, CHEN Y W, et al. Soil salinity estimation based on sentinel-1/2 texture features and machine learning[J]. IEEE Sensors Journal, 2024, 24(9): 15302-15310. doi: 10.1109/JSEN.2024.3377682
[15]	姚远, 丁建丽, 张芳, 等. 基于高光谱指数和电磁感应技术的区域土壤盐渍化监测模型[J]. 光谱学与光谱分析, 2013, 33(6): 1658-1664. YAO Y, DING J L, ZHANG F, et al. Research on model of soil salinization monitoring based on hyperspectral index and EM38[J]. Spectroscopy and Spectral Analysis, 2013, 33(6): 1658-1664. (in Chinese with English abstract)
[16]	孙亚楠, 李仙岳, 史海滨, 等. 基于多源数据融合的盐分遥感反演与季节差异性研究[J]. 农业机械学报, 2020, 51(6): 169-180. SUN Y N, LI X Y, SHI H B, et al. Remote sensing inversion of soil salinity and seasonal difference analysis based on multi-source data fusion[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(6): 169-180. (in Chinese with English abstract)
[17]	张贤龙, 张飞, 张海威, 等. 基于光谱变换的高光谱指数土壤盐分反演模型优选[J]. 农业工程学报, 2018, 34(1): 110-117. doi: 10.11975/j.issn.1002-6819.2018.01.015 ZHANG X L, ZHANG F, ZHANG H W, et al. Optimization of soil salt inversion model based on spectral transformation from hyperspectral index[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(1): 110-117. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2018.01.015
[18]	张孟, 韦玮, 张艳娜, 等. Hyperion高光谱遥感器的在轨自动化定标[J]. 光学学报, 2019, 39(5): 0528002. doi: 10.3788/AOS201939.0528002 ZHANG M, WEI W, ZHANG Y N, et al. On-orbit automated calibration of Hyperion hyperspectral remote sensor[J]. Acta Optica Sinica, 2019, 39(5): 0528002. (in Chinese with English abstract) doi: 10.3788/AOS201939.0528002
[19]	马驰. 基于HJ1A-HSI反演松嫩平原土壤盐分含量[J]. 干旱区研究, 2014, 31(2): 226-230. MA C. Quantitative retrieval of soil salt content in the Songnen Plain based on HJ1A-HSI images[J]. Arid Zone Research, 2014, 31(2): 226-230. (in Chinese with English abstract)
[20]	THENKABAIL P S, ENCLONA E A, ASHTON M S, et al. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests[J]. Remote Sensing of Environment, 2004, 90(1): 23-43. doi: 10.1016/j.rse.2003.11.018
[21]	KATTENBORN T, MAACK J, FAßNACHT F, et al. Mapping forest biomass from space: Fusion of hyperspectral EO1-Hyperion data and Tandem-X and WorldView-2 canopy height models[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35: 359-367. doi: 10.1016/j.jag.2014.10.008
[22]	厉彦玲, 赵庚星, 常春艳, 等. OLI与HSI影像融合的土壤盐分反演模型[J]. 农业工程学报, 2017, 33(21): 173-180. LI Y L, ZHAO G X, CHANG C Y, et al. Soil salinity retrieval model based on OLI and HSI image fusion[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(21): 173-180. (in Chinese with English abstract)
[23]	刘知, 李彦星, 杨波, 等. 山西省中条山中北段遥感示矿信息提取与区块优选[J]. 地质科技通报, 2026, 45(2): 274-292. doi: 10.19509/j.cnki.dzkq.tb20240617 LIU Z, LI Y X, YANG B, et al. Extraction of remote sensing ore-indicating information and block optimization in middle-northern segment of Zhongtiao Mountain, Shanxi Province[J]. Bulletin of Geological Science and Technology, 2026, 45(2): 274-292. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20240617
[24]	孙亚楠, 李仙岳, 史海滨, 等. 基于高光谱数据的盐荒地和耕地土壤盐分遥感反演优化[J]. 农业工程学报, 2022, 38(23): 101-111. SUN Y N, LI X Y, SHI H B, et al. Optimizing the inversion of soil salt in salinized wasteland using hyperspectral data from remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(23): 101-111. (in Chinese with English abstract)
[25]	周世勋, 尹娟, 王军涛, 等. 灌区土壤含盐量的高光谱估测与空间分布研究[J]. 灌溉排水学报, 2025, 44(2): 72-82. doi: 10.13522/j.cnki.ggps.2024066 ZHOU S X, YIN J, WANG J T, et al. Mapping soil salinity in irrigated areas using hyperspectral UAV imagery[J]. Journal of Irrigation and Drainage, 2025, 44(2): 72-82. (in Chinese with English abstract) doi: 10.13522/j.cnki.ggps.2024066
[26]	张茗惠, 张峻华, 盛统民, 等. 微咸水膜下滴灌棉花根系时空分布规律及三维动态生长模拟[J]. 地质科技通报, 2026, 45(1): 279-290. doi: 10.19509/j.cnki.dzkq.tb20240155 ZHANG M H, ZHANG J H, SHENG T M, et al. Spatial-temporal distribution patterns of cotton root system under brackish water mulched drip irrigation and three-dimensional dynamic growth simulation[J]. Bulletin of Geological Science and Technology, 2026, 45(1): 279-290. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20240155
[27]	WICKE B, SMEETS E, DORNBURG V, et al. The global technical and economic potential of bioenergy from salt-affected soils[J]. Energy & Environmental Science, 2011, 4(8): 2669-2681. doi: 10.1039/C1EE01029H
[28]	曹肖奕, 丁建丽, 葛翔宇, 等. 基于不同卫星光谱模拟的土壤电导率估算研究[J]. 干旱区地理, 2020, 43(1): 172-181. doi: 10.12118/j.issn.1000-6060.2020.01.20 CAO X Y, DING J L, GE X Y, et al. Estimation of soil conductivity based on spectral simulation of different satellites[J]. Arid Land Geography, 2020, 43(1): 172-181. (in Chinese with English abstract) doi: 10.12118/j.issn.1000-6060.2020.01.20
[29]	WU D, JIA K L, ZHANG X D, et al. Remote sensing inversion for simulation of soil salinization based on hyperspectral data and ground analysis in Yinchuan, China[J]. Natural Resources Research, 2021, 30(6): 4641-4656.
[30]	KHAN N M, RASTOSKUEV V V, SATO Y, et al. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators[J]. Agricultural Water Management, 2005, 77(1/2/3): 96-109. doi: 10.1016/j.agwat.2004.09.038
[31]	FOURATI H T, BOUAZIZ M, BENZINA M, et al. Modeling of soil salinity within a semi-arid region using spectral analysis[J]. Arabian Journal of Geosciences, 2015, 8(12): 11175-11182. doi: 10.1007/s12517-015-2004-3
[32]	姚志华, 陈俊英, 张智韬, 等. 覆膜对无人机多光谱遥感反演土壤含盐量精度的影响[J]. 农业工程学报, 2019, 35(19): 89-97. YAO Z H, CHEN J Y, ZHANG Z T, et al. Effect of plastic film mulching on soil salinity inversion by using UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 89-97. (in Chinese with English abstract)
[33]	DOUAOUI A E K, NICOLAS H, WALTER C. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J]. Geoderma, 2006, 134(1/2): 217-230. doi: 10.1016/j.geoderma.2005.10.009
[34]	SCUDIERO E, SKAGGS T H, CORWIN D L. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA[J]. Geoderma Regional, 2014, 2/3: 82-90.
[35]	张智韬, 魏广飞, 姚志华, 等. 基于无人机多光谱遥感的土壤含盐量反演模型研究[J]. 农业机械学报, 2019, 50(12): 151-160. doi: 10.6041/j.issn.1000-1298.2020.10.027 ZHANG Z T, WEI G F, YAO Z H, et al. Soil salt inversion model based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12): 151-160. (in Chinese with English abstract) doi: 10.6041/j.issn.1000-1298.2020.10.027
[36]	CHO K H, BEON M S, JEONG J C. Dynamics of soil salinity and vegetation in a reclaimed area in Saemangeum, Republic of Korea[J]. Geoderma, 2018, 321: 42-51. doi: 10.1016/j.geoderma.2018.01.031
[37]	冯娟, 丁建丽, 杨爱霞, 等. 干旱区土壤盐渍化信息遥感建模[J]. 干旱地区农业研究, 2018, 36(1): 266-273. doi: 10.7606/j.issn.1000-7601.2018.01.39 FENG J, DING J L, YANG A X, et al. Remote sensing modeling of soil salinization information in arid areas[J]. Agricultural Research in the Arid Areas, 2018, 36(1): 266-273. (in Chinese with English abstract) doi: 10.7606/j.issn.1000-7601.2018.01.39
[38]	奚雪, 赵庚星, 高鹏, 等. 基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究: 以黄三角垦利区为例[J]. 中国农业科学, 2020, 53(24): 5005-5016. doi: 10.3864/j.issn.0578-1752.2020.24.004 XI X, ZHAO G X, GAO P, et al. Inversion of soil salinity in coastal winter wheat growing area based on sentinel satellite and unmanned aerial vehicle multi-spectrum: A case study in Kenli district of the Yellow River Delta[J]. Scientia Agricultura Sinica, 2020, 53(24): 5005-5016. (in Chinese with English abstract) doi: 10.3864/j.issn.0578-1752.2020.24.004
[39]	陈红艳, 赵庚星, 陈敬春, 等. 基于改进植被指数的黄河口区盐渍土盐分遥感反演[J]. 农业工程学报, 2015, 31(5): 107-114. doi: 10.3969/j.issn.1002-6819.2015.05.016 CHEN H Y, ZHAO G X, CHEN J C, et al. Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 107-114. (in Chinese with English abstract) doi: 10.3969/j.issn.1002-6819.2015.05.016
[40]	GITELSON A A, KAUFMAN Y J, MERZLYAK M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298. doi: 10.1016/S0034-4257(96)00072-7
[41]	WU W. The generalized difference vegetation index (GDVI) for dryland characterization[J]. Remote Sensing, 2014, 6(2): 1211-1233. doi: 10.3390/w13060856
[42]	BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
[43]	王怡婧, 贾萍萍, 陈睿华, 等. 多源遥感对宁夏干湿季土壤含盐量敏感性分析与定量反演[J]. 生态学杂志, 2023, 42(9): 2286-2295. doi: 10.13292/j.1000-4890.202309.006 WANG Y J, JIA P P, CHEN R H, et al. Sensitivity analysis and quantitative inversion of multi-source remote sensing to soil salt content in dry and wet seasons in Ningxia[J]. Chinese Journal of Ecology, 2023, 42(9): 2286-2295. (in Chinese with English abstract) doi: 10.13292/j.1000-4890.202309.006
[44]	王飞, 丁建丽, 魏阳, 等. 基于Landsat系列数据的盐分指数和植被指数对土壤盐度变异性的响应分析: 以新疆天山南北典型绿洲为例[J]. 生态学报, 2017, 37(15): 5007-5022. WANG F, DING J L, WEI Y, et al. Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images: Applications in different oases in Xinjiang, China[J]. Acta Ecologica Sinica, 2017, 37(15): 5007-5022. (in Chinese with English abstract)
[45]	谷海斌, 王泽, 武红旗, 等. 不同开垦年限土壤养分和盐分时空分布特征研究[J]. 干旱地区农业研究, 2016, 34(3): 8-15. doi: 10.7606/j.issn.1000-7601.2016.03.02 GU H B, WANG Z, WU H Q, et al. Research on temporal and spatial distribution characteristics of soil nutrient and salt under different reclamation years[J]. Agricultural Research in the Arid Areas, 2016, 34(3): 8-15. (in Chinese with English abstract) doi: 10.7606/j.issn.1000-7601.2016.03.02
[46]	张雪, 陈耀轩, 张盼, 等. 孔雀河流域农业灌区土壤盐分空间分布及变异特征[J]. 土壤, 2016, 48(6): 1223-1228. doi: 10.13758/j.cnki.tr.2016.06.023 ZHANG X, CHEN Y X, ZHANG P, et al. Soil salinity spatial distribution and variation characteristics in irrigated land of Kongque River Basin[J]. Soils, 2016, 48(6): 1223-1228. (in Chinese with English abstract) doi: 10.13758/j.cnki.tr.2016.06.023