Advances in research on methods for optical remote sensing monitoring of soil salinization

doi:10.6046/zrzyyg.2023245

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Abstract

Soil salinization is identified as a major cause of decreased soil fertility, productivity, vegetation coverage, and crop yield. Optical remote sensing monitoring enjoys advantages such as macro-scale, timeliness, dynamics, and low costs, rendering this technology significant for the dynamic monitoring of soil salinization. However, there is a lack of reviews of the systematic organization of multi-scale remote sensing data, multi-type remote sensing feature parameters, and inversion models. This study first organized the optical remote sensing data sources and summarized the remote sensing data sources and scale platforms utilized in current studies on saline soil monitoring. Accordingly, this study categorized multi-source remote sensing data into three different platforms: satellite, aerial, and ground. Second, this study organized the mainstream characteristic parameters for modeling and two typical inversion methods, i.e., statistical regression and machine learning, and analyzed the current status of research on both methods. Finally, this study explored the fusion of remote sensing data sources and compared the pros and cons of various modeling methods. Furthermore, in combination with current hot research topics, this study discussed the prospects for the application of data assimilation and deep learning to soil salinization monitoring.

Keywords soil salinization soil salinity optical remote sensing inversion model characteristic parameter

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Issue Date: 23 December 2024

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	Zhenhai LUO
	Chao ZHANG
	Shaoyuan FENG
	Min TANG
	Rui LIU
	Jiying KONG

Cite this article:

Zhenhai LUO,Chao ZHANG,Shaoyuan FENG, et al. Advances in research on methods for optical remote sensing monitoring of soil salinization[J]. Remote Sensing for Natural Resources, 2024, 36(4): 9-22.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023245 OR https://www.gtzyyg.com/EN/Y2024/V36/I4/9

Tab.1 Main parameters and features of commonly used satellite remote sensing

Tab.2 UAV characteristics for agricultural remote sensing monitoring

Tab.3 Different types of airborne sensors and characteristics

Tab.4 Near-earth instruments for remote sensing monitoring of saline soils

特征参量	变量名称	公式^①	参考文献
盐分指数	盐分指数(salinity index, SI)	$B × R$	[9]
	盐分指数(SI1)	$G × R$	[17,51 52]
	盐分指数(SI2)	$G 2 + R 2 + N I R 2$	[17,51 52]
	盐分指数(SI3)	$G 2 + R 2$	[17,51 52]
	盐分指数(SI6)	$B × R G$	[52]
	盐分指数(SI7)	$N I R × R G$	[53]
	亮度指数(brightness index, BI)	$R 2 + N I R 2$	[51,54]
	归一化盐分指数(normalized salinity index, NDSI)	$R - N I R R + N I R$	[55]
	盐分指数(salinity index,SI-T)	$R N I R × 100$	[56]
	强度指数(intensity index, INT1)	$G + R 2$	[51,57]
植被指数	土壤调节植被指数(soil-adjusted vegetation index, SAVI)	$(N I R - R) × 1.5 N I R + R + 0.5$	[17,58]
	归一化植被指数(normalized vegetation index, NDVI)	$N I R - R N I R + R$	[16,17 59]
	重整化差异植被指数(renormalize differential vegetation index, RDVI)	$N I R - R N I R + R$	[60]
	绿色归一化差分植被指数(green normalized differential vegetation index, GNDVI)	$N I R - G N I R + G$	[60]
	三角植被指数(triangular vegetation index, TVI)	$N I R - R N I R + R + 0.5$	[60]
	差值植被指数(differential vegetation index, DVI)	$N I R - R$	[16,17]
	归一化差值绿色指数(normalized difference green index, NDGI)	$G - R G + R$	[61]
	增强化归一植被指数(enhanced normalized differential vegetation index, ENDVI)	$N I R + S W I R 2 - R N I R + S W I R 2 + R$	[11]
水分指数	水分指数(water index, WI)	$N I R S W I R$	[10]
水分指数	归一化水分指数(normalized differential water index, NDWI)	$N I R - S W I R N I R + S W I R$	[15,62]
温度	温度植被干旱指数(temperature vegetation drought index, TVDI)	$T S - T S m i n T S m a x - T S m i n$	[63]
温度	植被温度条件指数(vegetation temperature condition index, VTCI)	$L S T N D V I m a x - L S T N D V I L S T N D V I m a x - L S T N D V I m i n$	[64]

Tab.5 Commonly used spectral indices calculation formulas

建模类别	建模特征参量	反演目标变量	建模方法	结果	参考文献
统计回归模型	光谱参数、植被指数、垂直干旱指数、反射率	土壤盐分含量	相关性分析、多元线性回归(multiple linear regression, MLR)	植被指数中,土壤盐分响应最高决定系数R²=0.577	[11]
	盐度指数、植被指数、热湿指数	土壤盐分含量	PLSR	研究区四季土壤盐分含量平均值分别为8.00 g/kg,7.53 g/kg,7.83 g/kg和6.90 g/kg	[17]
	植被指数、盐度指数	土壤电导率	MLR	R²=0.77 R²=0.75	[23]
	盐度指数、电导率	土壤盐度空间变化	回归分析	R²=0.65	[24]
	盐度指数	土壤盐分含量	MLR	相关系数R>0.3	[26]
	植被指数、冠层温度、植被株高	作物株高、气孔导度、盐分含量	MLR	R²=0.64	[30]
	土壤有机质含量、反射率	土壤盐分含量	相关性分析、MLR	校准: R²=0.684 验证: R²=0.663	[37]
	土壤水分、电导率、热红外数据、作物生长数据	土壤盐分含量	回归分析	R²=0.86	[39]
	反射率、土壤湿度和质地	土壤盐分含量、土壤含水量	多元逐步回归	R²=0.47	[49]
	盐分指数、植被指数	土壤盐分含量	线性和非线性回归	R²=0.59	[53]
	盐度指数、植被指数	土壤电导率	PLSR	R²=0.52	[57]
	盐度指数、亮度指数、植被指数	土壤盐分含量	MLR、多元逐步回归	R²=0.992 均方根误差(root mean square error,RMSE)为0.195 g/kg	[66]
	反射率、	土壤盐分含量	PLSR	R²=0.749	[67]
	植被指数	土壤盐分含量	PLSR	R²=0.50~0.58	[68]
	植被指数	土壤盐分含量	线性回归	平均误差(mean error,ME)和RMSE分别为-0.61 ds/m和52.2 ds/m	[74]
	盐度指数、亮度指数、植被指数	土壤盐分含量	MLR	标准误差约为12.1 μs/cm	[76]
	电导率	土壤盐分含量	PLSR	相关系数R= 0.700	[77]
	pH值、电导率	土壤盐分含量	PLSR,MLR	土壤pH值和电导率模型的R²平均值分别为0.77和0.48	[78]
	电导率、反射率	土壤盐分含量	PCA,PLSR	PLSR和PCA模型的校准精度分别为R²= 0.862和R²= 0.537	[79]
	植被指数、盐度指数	土壤盐分含量	相关性分析	R²=0.739(无盐渍化)、0.469(轻度)、0.677(重度)	[25]
	电导率、盐度指数	土壤盐分含量	逻辑回归模型	R²=0.88,RMSE=20.85 ds/m	[27]
	植被指数、水分指数	土壤盐分含量	特征空间	MWI与土壤表层盐分含量相关性较高(R=0.844)	[73]
	植被指数、冠层温度	土壤盐分含量	统计分析、方差分析	F=0.245,P=0.865	[59]
	盐分指数	土壤盐分含量	监督最大似然分类法	分类精度约为90%	[52]
机器学习模型	反射率、电导率	土壤盐分含量	PLSR,SVM	相对差异百分比(relative percent difference,RPD)为3.35%	[3]
	植被指数、盐分指数、反射率	土壤盐分含量	MLR,PLSR,SVM,RF	RF拟合精度最高,训练: R²=0.870,验证: R²=0.766	[8]
	电导率、反射率	土壤盐分含量	PLSR,人工神经网络(artificial neural network,anN)	PLSR: R>0.81,RPD>2.1% ANN: R>0.92,RPD>2.3%	[16]
	反射率、光谱指数	土壤盐分含量	MLR,RF,SVM,BPNN	R²=0.770(裸土) R²=0.676(植被覆盖)	[33]
	盐分指数	土壤盐分含量	BPNN,SVR,RF	校准: R²=0.835,验证: R²=0.812,RPD=2.299%	[34]
	反射率、植被指数、盐分指数	土壤盐分含量	PLSR,BPNN,SVR,RF	建模: R²=0.724,RMSE=1.764 g/kg 验证: R²=0.745,RMSE=1.879 g/kg,RPD=2.211%	[35]
	反射率	土壤盐分含量	RF	R²=0.95	[42]
	植被指数	土壤盐分含量	BPNN,SVM,RF	R²=0.885	[60]
	盐分指数	土壤水盐信息	BPNN	R²=0.624	[80]
	植被指数、盐分指数、亮度指数	土壤盐分含量	BPNN	建模: R²=0.769 验证: R²=0.774	[81]
	地下水深度、灌溉水量、蒸发量	土壤电导率	SVM	建模: MRE=2.14%,验证: MRE=3.48%,预测: MRE=6.37%	[82]

Tab.6 Summary of research study on soil salinity inversion based on multi-platform and multi-source remote sensing data

[1]	李洪义. 滨海盐土三维土体电导率空间变异及可视化研究[D]. 杭州: 浙江大学, 2008.
[1]	Li H Y. Three dimensional variability and visualization of soil electrical conductivity in coastal saline land[D]. Hangzhou: Zhejiang University, 2008.
[2]	杨劲松. 中国盐渍土研究的发展历程与展望[J]. 土壤学报, 2008, 45(5):837-845.
[2]	Yang J S. Development and prospect of the research on salt-affected soils in China[J]. Acta Pedologica Sinica, 2008, 45(5):837-845.
[3]	Ali A A A, Weindorf D C, Chakraborty S, et al. Combination of proximal and remote sensing methods for rapid soil salinity quantification[J]. Geoderma, 2015, 239:34-46.
[4]	Sreenivas K, Venkataratnam L, Narasimha R P V. Dielectric properties of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(4):641-649.
[5]	Wang L, Wei Y. Estimating the total nitrogen and total phosphorus content of wetland soils using hyperspectral models[J]. Acta Ecologica Sinica, 2016, 36(16):5116-5125.
[6]	Rao B R M, Sharma R C, Ravi Sankar T, et al. Spectral behaviour of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(12):2125-2136.
[7]	刘全明. 含盐土壤盐渍化雷达反演模拟研究[J]. 测绘通报, 2014(9):43-46. doi: 10.13474/j.cnki.11-2246.2014.0290
[7]	Liu Q M. On radar inversion and simulation of salty soil salinization[J]. Bulletin of Surveying and Mapping, 2014(9):43-46. doi: 10.13474/j.cnki.11-2246.2014.0290
[8]	黄晓宇, 王雪梅, 卡吾恰提·白山. 基于Landsat8 OLI影像干旱区绿洲土壤含盐量反演[J]. 自然资源遥感, 2023, 35(1):189-197.doi: 10.6046/zrzyyg.2022047.
[8]	Huang X Y, Wang X M, Kawuqiati B. Inversion of soil salinity of an oasis in an arid area based on Landsat8 OLI images[J]. Remote Sensing for Natural Resources, 2023, 35(1):189-197.doi: 10.6046/zrzyyg.2022047.
[9]	周磊, 贺聪聪, 吕爱锋, 等. 柴达木盆地土壤盐渍化程度快速动态监测[J]. 测绘科学, 2021, 46(7):99-106,114.
[9]	Zhou L, He C C, Lyu A F, et al. Research on quick dynamic monitoring of soil salinization in Qaidam Basin[J]. Science of Surveying and Mapping, 2021, 46(7):99-106,114.
[10]	Zhang X, Huang B, Liu F. Information extraction and dynamic evaluation of soil salinization with a remote sensing method in a typical county on the Huang-Huai-Hai Plain of China[J]. Pedosphere, 2020, 30(4):496-507.
[11]	Wu D, Jia K, Zhang X, 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.
[12]	Sahbeni G, Ngabire M, Musyimi P K, et al. Challenges and opportunities in remote sensing for soil salinization mapping and monitoring:A review[J]. Remote Sensing, 2023, 15(10):2540.
[13]	Ma Y, Tashpolat N. Current status and development trend of soil salinity monitoring research in China[J]. Sustainability, 2023, 15(7):5874.
[14]	Benediktsson J A, Chanussot J, Moon W M. Very high-resolution remote sensing:Challenges and opportunities[J]. Proceedings of the IEEE, 2012, 100(6):1907-1910.
[15]	Mulla D J. Twenty five years of remote sensing in precision agriculture:Key advances and remaining knowledge gaps[J]. Biosystems Engineering, 2013, 114(4):358-371.
[16]	Farifteh J, Van der Meer F, Atzberger C, et al. Quantitative analysis of salt-affected soil reflectance spectra:A comparison of two adaptive methods (PLSR and ANN)[J]. Remote Sensing of Environment, 2007, 110(1):59-78.
[17]	Chi Y, Sun J, Liu W, et al. Mapping coastal wetland soil salinity in different seasons using an improved comprehensive land surface factor system[J]. Ecological Indicators, 2019, 107:105517.
[18]	Hick P T, Russell W. Some spectral considerations for remote sensing of soil salinity[J]. Soil Research, 1990, 28(3):417.
[19]	Daliakopoulos I N, Tsanis I K, Koutroulis A, et al. The threat of soil salinity:A European scale review[J]. Science of the Total Environment, 2016, 573:727-739.
[20]	Vaheddoost B, Guan Y, Mohammadi B. Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils[J]. Environmental Science and Pollution Research, 2020, 27(12):13131-13141.
[21]	Ghassemian H. A review of remote sensing image fusion methods[J]. Information Fusion, 2016, 32:75-89.
[22]	Wu W, Mhaimeed A S, Al-Shafie W M, et al. Mapping soil salinity changes using remote sensing in Central Iraq[J]. Geoderma Regional, 2014, 2:21-31.
[23]	Gorji T, Yildirim A, Hamzehpour N, et al. Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements[J]. Ecological Indicators, 2020, 112:106173.
[24]	Allbed A, Kumar L, Sinha P. Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques[J]. Remote Sensing, 2014, 6(2):1137-1157.
[25]	陈实, 徐斌, 金云翔, 等. 北疆农区土壤盐渍化遥感监测及其时空特征分析[J]. 地理科学, 2015, 35(12):1607-1615. doi: 10.13249/j.cnki.sgs.2015.012.1607
[25]	Chen S, Xu B, Jin Y X, et al. Remote sensing monitoring and spatial-temporal characteristics analysis of soil salinization in agricultural area of northern Xinjiang[J]. Scientia Geographica Sinica, 2015, 35(12):1607-1615. doi: 10.13249/j.cnki.sgs.2015.012.1607
[26]	陈俊英, 王新涛, 张智韬, 等. 基于无人机-卫星遥感升尺度的土壤盐渍化监测方法[J]. 农业机械学报, 2019, 50(12):161-169.
[26]	Chen J Y, Wang X T, Zhang Z T, et al. Soil salinization monitoring method based on UAV-satellite remote sensing scale-up[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12):161-169.
[27]	Farahmand N, Sadeghi V. Estimating soil salinity in the dried lake bed of urmia lake using optical sentinel-2 images and nonlinear regression models[J]. Journal of the Indian Society of Remote Sensing, 2020, 48(4):675-687. doi: 10.1007/s12524-019-01100-8
[28]	Huang H, Deng J, Lan Y, et al. Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery[J]. Sensors, 2018, 18(10):3299.
[29]	李冰, 刘镕源, 刘素红, 等. 基于低空无人机遥感的冬小麦覆盖度变化监测[J]. 农业工程学报, 2012, 28(13):160-165.
[29]	Li B, Liu R Y, Liu S H, et al. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(13):160-165.
[30]	Ivushkin K, Bartholomeus H, Bregt A K, et al. UAV based soil salinity assessment of cropland[J]. Geoderma, 2019, 338:502-512. doi: 10.1016/j.geoderma.2018.09.046
[31]	孙刚, 黄文江, 陈鹏飞, 等. 轻小型无人机多光谱遥感技术应用进展[J]. 农业机械学报, 2018, 49(3):1-17.
[31]	Sun G, Huang W J, Chen P F, et al. Advances in UAV-based multispectral remote sensing applications[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(3):1-17.
[32]	Xu L, Zheng C L, Wang Z C, et al. A digital camera as an alternative tool for estimating soil salinity and soil surface roughness[J]. Geoderma, 2019, 341:68-75.
[33]	Xie L, Feng X, Zhang C, et al. A framework for soil salinity monitoring in coastal wetland reclamation areas based on combined unmanned aerial vehicle (UAV) data and satellite data[J]. Drones, 2022, 6(9):257.
[34]	Wei G, Li Y, Zhang Z, et al. Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms[J]. PeerJ, 2020, 8:e9087.
[35]	Yu X, Chang C, Song J, et al. Precise monitoring of soil salinity in China’s Yellow River Delta using UAV-borne multispectral imagery and a soil salinity retrieval index[J]. Sensors, 2022, 22(2):546.
[36]	Zhu C, Ding J, Zhang Z, et al. SPAD monitoring of saline vegetation based on Gaussian mixture model and UAV hyperspectral image feature classification[J]. Computers and Electronics in Agriculture, 2022, 200:107236.
[37]	Sun M, Li Q, Jiang X, et al. Estimation of soil salt content and organic matter on arable land in the Yellow River Delta by combining UAV hyperspectral and landsat-8 multispectral imagery[J]. Sensors, 2022, 22(11):3990.
[38]	Das S, Christopher J, Apan A, et al. UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173:221-237.
[39]	Tian F, Hou M, Qiu Y, et al. Salinity stress effects on transpiration and plant growth under different salinity soil levels based on thermal infrared remote (TIR) technique[J]. Geoderma, 2020, 357:113961.
[40]	Bhardwaj A, Sam L, Akanksha, et al. UAVs as remote sensing platform in glaciology:Present applications and future prospects[J]. Remote Sensing of Environment, 2016, 175:196-204.
[41]	Nevalainen O, Honkavaara E, Tuominen S, et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging[J]. Remote Sensing, 2017, 9(3):185.
[42]	Hu J, Peng J, Zhou Y, et al. Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images[J]. Remote Sensing, 2019, 11(7):736.
[43]	乔纪纲, 艾彬, 邹春洋. 基于多源遥感的莺歌海滨岸环境特征分析[J]. 热带地理, 2011, 31(5):456-462.
[43]	Qiao J G, Ai B, Zou C Y. Analysis of coastal environment in yinggehai with multi-source remote sensing images[J]. Tropical Geography, 2011, 31(5):456-462.
[44]	Zhang H, Wang L, Tian T, et al. A review of unmanned aerial vehicle low-altitude remote sensing (UAV-LARS) use in agricultural monitoring in China[J]. Remote Sensing, 2021, 13(6):1221.
[45]	Mulder V L, de Bruin S, Schaepman M E, et al. The use of remote sensing in soil and terrain mapping: A review[J]. Geoderma, 2011, 162(1/2):1-19.
[46]	贡璐, 韩丽, 任曼丽, 等. 塔里木河上游典型绿洲土壤水盐空间分异特征[J]. 水土保持学报, 2012, 26(4):251-255,278.
[46]	Gong L, Han L, Ren M L, et al. Spatial variability of soil water-salt in a typical oasis on the upper reaches of the Tarim River[J]. Journal of Soil and Water Conservation, 2012, 26(4):251-255,278.
[47]	Deng K, Ding J, Yang A, et al. Modeling of the spatial distribution of soil profile salinity based on the electromagnetic induction technique[J]. Acta Ecologica Sinica, 2016, 36(20):6387.
[48]	Kahaer Y, Yang S, Tashpolat N, et al. Hyperspectral estimation of soil electrical conductivity based on fractional order differentially optimised spectral indices[J]. Acta Ecologica Sinica, 2019, 39(19):7237-7248.
[49]	Xu C, Zeng W, Huang J, et al. Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data[J]. Remote Sensing, 2016, 8(1):42.
[50]	蒲智, 于瑞德, 尹昌应, 等. 干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究[J]. 水土保持通报, 2012, 32(6):129-133.
[50]	Pu Z, Yu R D, Yin C Y, et al. Optimal hyperspectral indices for soil salt content estimation on typical saline soil in arid areas[J]. Bulletin of Soil and Water Conservation, 2012, 32(6):129-133.
[51]	Song C, Ren H, Huang C. Estimating soil salinity in the Yellow River Delta,Eastern China: An integrated approach using spectral and terrain indices with the generalized additive model[J]. Pedosphere, 2016, 26(5):626-635.
[52]	Abbas A, Khan S, Hussain N, et al. Characterizing soil salinity in irrigated agriculture using a remote sensing approach[J]. Physics and Chemistry of the Earth,Parts A/B/C, 2013, 55:43-52.
[53]	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.
[53]	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.
[54]	郭鹏, 李华, 陈红艳, 等. 基于光谱指数优选的土壤盐分定量光谱估测[J]. 水土保持通报, 2018, 38(3):193-199,205.
[54]	Guo P, Li H, Chen H Y, et al. Quantitative spectral estimation of soil salinity based on optimum spectral indices[J]. Bulletin of Soil and Water Conservation, 2018, 38(3):193-199,205.
[55]	Khan N, Rastoskuev V V, Shalina E, et al. Mapping salt-affected soils using remote sensing indicators:A simple approach with the use of GIS IDRISI[C]// 22nd Asian Conference on Remote Sensing, 2001, 5(9).
[56]	Tripathi N K, Rai B K, Dwivedi P. Spatial modeling of soil alkalinity in GIS environment using IRS data[C]// Proceedings of the 18th Asian Conference on Remote Sensing,Kuala Lumpur,Malaysia. 1997:20-24.
[57]	Triki F H, 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.
[58]	Huete A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3):295-309.
[59]	Ivushkin K, Bartholomeus H, Bregt A K, et al. Satellite thermography for soil salinity assessment of cropped areas in Uzbekistan[J]. Land Degradation & Development, 2017, 28(3):870-877.
[60]	Qi G, Zhao G, Xi X. Soil salinity inversion of winter wheat areas based on satellite-unmanned aerial vehicle-ground collaborative system in coastal of the Yellow River Delta[J]. Sensors, 2020, 20(22):6521.
[61]	Lyon J G, Yuan D, Lunetta R S, et al. A change detection experiment using vegetation indices[J]. Photogrammetric Engineering and Remote Sensing, 1998, 64(2):143-150.
[62]	McFeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
[63]	Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2/3):213-224.
[64]	Wan Z, Wang P, Li X. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains,USA[J]. International Journal of Remote Sensing, 2004, 25(1):61-72.
[65]	Tziolas N, Tsakiridis N, Ben-Dor E, et al. A memory-based learning approach utilizing combined spectral sources and geographical proximity for improved VIS-NIR-SWIR soil properties estimation[J]. Geoderma, 2019, 340:11-24.
[66]	Sidike A, Zhao S, Wen Y. Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 26:156-175.
[67]	Fan X, Liu Y, Tao J, et al. Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression[J]. Remote Sensing, 2015, 7(1):488-511.
[68]	Zhang T T, Zeng S L, Gao Y, et al. Using hyperspectral vegetation indices as a proxy to monitor soil salinity[J]. Ecological Indicators, 2011, 11(6):1552-1562.
[69]	Abdullah A Y M, Biswas R K, Chowdhury A I, et al. Modeling soil salinity using direct and indirect measurement techniques:A comparative analysis[J]. Environmental Development, 2019, 29:67-80. doi: 10.1016/j.envdev.2018.12.007
[70]	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.
[71]	Allbed A, Kumar L, Aldakheel Y Y. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region[J]. Geoderma, 2014, 230:1-8.
[72]	张思源, 岳楚, 袁国礼, 等. 基于ENDVI-SI3特征空间的盐渍化反演模型及风险评估[J]. 自然资源遥感, 2022, 34(4):136-143.
[72]	Zhang S Y, Yue C, Yuan G L, 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.
[73]	丁建丽, 瞿娟, 孙永猛, 等. 基于MSAVI-WI特征空间的新疆渭干河-库车河流域绿洲土壤盐渍化研究[J]. 地理研究, 2013, 32(2):223-232.
[73]	Ding J L, Qu J, Sun Y M, et al. The retrieval model of soil salinization information in arid region based on MSAVI-WI feature space:A case study of the delta oasis in Weigan-Kuqa watershed[J]. Geographical Research, 2013, 32(2):223-232.
[74]	Tajgardan T, Shataee S, Ayoubi S. Spatial prediction of soil salinity in the arid zones using ASTER data,Case study:North of Ag ghala,Golestan Province,Iran[C]// 28th Asian Conference on Remote Sensing 2007,ACRS 2007, 2007, 3:1712-1717.
[75]	Howari F M, Goodell P C, Miyamoto S. Spectral properties of salt crusts formed on saline soils[J]. Journal of Environmental Quality, 2002, 31(5):1453-1461. pmid: 12371161
[76]	Bouaziz M, Matschullat J, Gloaguen R. Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil[J]. Comptes Rendus Geoscience, 2011, 343(11/12):795-803.
[77]	陈文娇, 翁永玲, 范兴旺, 等. 基于光谱转换的土壤盐分反演与动态分析[J]. 东南大学学报(自然科学版), 2017, 47(6):1233-1238.
[77]	Chen W J, Weng Y L, Fan X W, et al. Soil salinity retrieval and dynamic analysis based on spectral band inter-calibration[J]. Journal of Southeast University (Natural Science Edition), 2017, 47(6):1233-1238.
[78]	Bai L, Wang C, Zang S, et al. Mapping soil alkalinity and salinity in northern Songnen Plain,China with the HJ-1 hyperspectral imager data and partial least squares regression[J]. Sensors, 2018, 18(11):3855.
[79]	Zhang X, Huang B. Prediction of soil salinity with soil-reflected spectra:A comparison of two regression methods[J]. Scientific Reports, 2019, 9(1):5067.
[80]	Wang J, Wang W, Hu Y, et al. Soil moisture and salinity inversion based on new remote sensing index and neural network at a Salina-alkaline wetland[J]. Water, 2021, 13(19):2762.
[81]	Zhang Z, Niu B, Li X, et al. Estimation and dynamic analysis of soil salinity based on UAV and sentinel-2A multispectral imagery in the coastal area,China[J]. Land, 2022, 11(12):2307.
[82]	Guan X, Wang S, Gao Z, et al. Dynamic prediction of soil salinization in an irrigation district based on the support vector machine[J]. Mathematical and Computer Modelling, 2013, 58(3/4):719-724.
[83]	Judkins G, Myint S. Spatial variation of soil salinity in the Mexicali Valley,Mexico:Application of a practical method for agricultural monitoring[J]. Environmental Management, 2012, 50(3):478-489.
[84]	吴霞, 王长军, 樊丽琴, 等. 基于多光谱遥感的盐渍化评价指数对宁夏银北灌区土壤盐度预测的适用性分析[J]. 国土资源遥感, 2021, 33(2):124-133.doi:10.6046/gtzyyg.2020210.
[84]	Wu X, Wang C J, Fan L Q, et al. An applicability analysis of salinization evaluation index based on multispectral remote sensing to soil salinity prediction in Yinbei irrigation area of Ningxia[J]. Remote Sensing for Land and Resources, 2021, 33(2):124-133.doi:10.6046/gtzyyg.2020210.
[85]	Alqasemi A S, Ibrahim M, Fadhil Al-Quraishi A M, et al. Detection and modeling of soil salinity variations in arid lands using remote sensing data[J]. Open Geosciences, 2021, 13(1):443-453.
[86]	Zare S, Fallah Shamsi S R, Ali Abtahi S. Weakly-coupled geo-statistical mapping of soil salinity to stepwise multiple linear regression of MODIS spectral image products[J]. Journal of African Earth Sciences, 2019, 152:101-114.
[87]	Jantaravikorn Y, Ongsomwang S. Soil salinity prediction and its severity mapping using a suitable interpolation method on data collected by electromagnetic induction method[J]. Applied Sciences, 2022, 12(20):10550.
[88]	蒋烨林, 王让会, 李焱, 等. 艾比湖流域不同土地覆盖类型土壤养分高光谱反演模型研究[J]. 中国生态农业学报, 2016, 24(11):1555-1564.
[88]	Jiang Y L, Wang R H, Li Y, et al. Hyper-spectral retrieval of soil nutrient content of various land-cover types in Ebinur Lake basin[J]. Chinese Journal of Eco-Agriculture, 2016, 24(11):1555-1564.
[89]	Liu Y, Qian J, Yue H. Comparison and evaluation of different dryness indices based on vegetation indices-land surface temperature/albedo feature space[J]. Advances in Space Research, 2021, 68(7):2791-2803.
[90]	王飞, 丁建丽, 伍漫春. 基于NDVI-SI特征空间的土壤盐渍化遥感模型[J]. 农业工程学报, 2010, 26(8):168-173,8.
[90]	Wang F, Ding J L, Wu M C. Remote sensing monitoring models of soil salinization based on NDVI-SI feature space[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8):168-173,8.
[91]	Guo B, Yang F, Fan Y, et al. Dynamic monitoring of soil salinization in Yellow River Delta utilizing MSAVI-SI feature space models with Landsat images[J]. Environmental Earth Sciences, 2019, 78(10):308.
[92]	冯娟, 丁建丽, 魏雯瑜. 基于Albedo-MSAVI特征空间的渭库绿洲土壤盐渍化研究[J]. 中国农村水利水电, 2018(2):147-152.
[92]	Feng J, Ding J L, Wei W Y. A study of soil salinization in Weigan and Kuqa Rivers Oasis based on albedo-MSAVI feature space[J]. China Rural Water and Hydropower, 2018(2):147-152.
[93]	Yao Y, Ding J, Wang S. Soil salinization monitoring in the Werigan-Kuqa Oasis,China,based on a three-dimensional feature space model with machine learning algorithm[J]. Remote Sensing Letters, 2021, 12(3):269-277.
[94]	Mutanga O, Adam E, Cho M A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm[J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 18:399-406.
[95]	Sui H, Chen D, Yan J, et al. Soil salinity estimation over coastal wetlands based on random forest algorithm and hydrological connectivity metric[J]. Frontiers in Marine Science, 2022, 9:895172.
[96]	胡婕. 基于多源遥感的干旱地区土壤盐分反演研究[D]. 杭州: 浙江大学, 2019.
[96]	Hu J. Estimation of soil salinity in arid area based on multi-source remote sensing[D]. Hangzhou: Zhejiang University, 2019.
[97]	Tan K, Ye Y Y, Du P J, et al. Estimation of heavy metal concentrations in reclaimed mining soils using reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2014, 34(12):3317-3322. pmid: 25881431
[98]	Cai S, Zhang R, Liu L, et al. A method of salt-affected soil information extraction based on a support vector machine with texture features[J]. Mathematical and Computer Modelling, 2010, 51(11/12):1319-1325.
[99]	Nawi N M, Khan A, Rehman M Z. A new back-propagation neural network optimized with cuckoo search algorithm[M]//Lecture Notes in Computer Science. Berlin,Heidelberg: Springer Berlin Heidelberg, 2013:413-426.
[100]	Wang X, Zhang F, Kung H T, et al. Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model[J]. International Journal of Remote Sensing, 2020, 41(3):953-973.
[101]	Du P, Xia J, Chanussot J, et al. Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest[C]// 2012 IEEE International Geoscience and Remote Sensing Symposium.Munich,Germany.IEEE, 2012:174-177.
[102]	王飞, 杨胜天, 丁建丽, 等. 环境敏感变量优选及机器学习算法预测绿洲土壤盐分[J]. 农业工程学报, 2018, 34(22):102-110.
[102]	Wang F, Yang S T, Ding J L, et al. Environmental sensitive variable optimization and machine learning algorithm using in soil salt prediction at oasis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(22):102-110.

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