Present situation and development trend in building remote sensing monitoring models of soil salinization

doi:10.6046/zrzyyg.2021395

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(1051 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

As a major form of soil degradation, soil salinization can greatly harm agricultural production and ecological environment. Remote sensing methods can acquire soil spectral characteristics in a rapid, macroscopic, and timely manner. Based on this, remote sensing monitoring models can be built for a wide range of soil salinization monitoring and assessment. Thus, summarizing and discussing the building methods for remote sensing monitoring models of soil salinization is of great significance to improve the precision of remote sensing monitoring of soil salinization and to monitor and control salinized soil. This study reviewed the recent literature related to remote sensing studies concerning soil salinization at home and abroad. Then, it summarized the steps such as factor selection, model building, and precision verification in the building of remote sensing monitoring models of soil salinization. Focusing on the current hot research topic, this study discussed the limitations and development trends. The main conclusions are as follows. The remote sensing monitoring models of soil salinization are important means for monitoring and forecasting salinized soil. In recent years, the hot research topic in this field is to improve the model precision using new data sources and models. Differences exist in the use of remote sensing data sources among different studies, but the modeling factors are all optimized from spectral sensitive bands, prior spectral indices, and remote sensing-derived data. The remote sensing monitoring models of soil salinization mainly include the linear regression model and the machine learning model. The remote sensing models built for different regions have different precision and applicability.

Keywords soil salinization remote sensing monitoring modeling factor model building precision verification

ZTFLH:

TP79

Issue Date: 27 December 2022

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Xingyou LI
	Fei ZHANG
	Zheng WANG

Cite this article:

Xingyou LI,Fei ZHANG,Zheng WANG. Present situation and development trend in building remote sensing monitoring models of soil salinization[J]. Remote Sensing for Natural Resources, 2022, 34(4): 11-21.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021395 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/11

Fig.1 Literature on soil salinization in CNKI and Web of Science database

指数类型		指数		指数公式^①	参考文献
盐分指数		盐分指数(salinity index,SI-T)		$S I - T = (R / N I R) × 100$	[44]
		盐分指数(normalized difference salinity index,NDSI)		$N D S I = (R - N I R) / (R + N I R)$	[45]
		盐分指数(salinity index1,SI1)		$S I 1 = R G$	[45]
		盐分指数(salinity index1,SI2)		$S I 2 = G 2 + R 2 + N I R 2$	[45]
		盐分指数(salinity index1,SI3)		$S I 3 = G 2 + R 2$	[45]
		盐分指数(salinity index,S1)		$S 1 = B / R$	[46]
		盐分指数(salinity index,S2)		$S 2 = (B - R) / (B + R)$	[46]
		盐分指数(salinity index,S3)		$S 3 = (G ? R) / B$	[46]
		盐分指数(salinity index,S5)		$S 5 = (B ? R) / G$	[46]
		盐分指数(salinity index,S6)		$S 6 = (R ? N I R) / G$	[46]
		盐分比指数(salinity ratio index,SAIO)		$S A I O = (R - N I R) / (G + N I R)$	[47]
		黏土指数(clay index,CLEX)		$C L E X = S W I R 1 / S W I R 2$	[47]
		石膏指数(gypsum index,GYEX)		$G Y E X = (S W I R 1 - N I R) / (S W I R 1 + N I R)$	[47]
		亮度指数(brightness index,BRI)		$B R I = G 2 + R 2$	[47]
		碳酸盐岩指数(carbonate index,CAEX)		$C A E X = R / G$	[47]
植被指数	简单比值指数(simple ratio vegetation index,SR)		$S R = N I R / R$			[48]
	冠层响应盐指数(canopy response salinity index,CRSI)		$C R S I = (N I R ? R) - (G ? R) (N I R ? R) + (G ? R)$			[49]
	归一化植被指数(normalized difference infrared index,NDVI)		$N D V I = (N I R - R) / (N I R + R)$			[45]
	增强植被指数(enhanced vegetation index,EVI)		$E V I = 2.5 (N I R - R N I R + 6 R - 7.5 B + 1)$			[50]
	差值植被指数(difference vegetation index,DVI)		$D V I = N I R - R$			[51]
	修改土壤调节植被指数(modified soil adjusted vegetation index,MSAVI)		$M S A V I = (2 N I R - 1) - (2 N I R + 1) 2 - 8 (N I R - R) 2$			[52]
	大气阻抗植被指数(atmospherically resistant vegetation index,ARVI)		$A R V I = N I R - (2 R - B) N I R + (2 R + B)$			[52]
	广义植被归一化指数(generalized difference vegetation index,GDVI)		$G D V I = (N I R 2 - R 2) / (N I R 2 + R 2)$			[53]
	双波段增强植被指数(two-band enhanced vegetation index,EVI2)		$E V I 2 = 2.5 (N I R - R) / (N I R + 2.4 R + 1)$			[54]
	扩展植被归一化指数(extended NDVI,ENDVI)		$E N D V I = N I R + S W I R 2 - R N I R + S W I R 2 + R$			[55]
	扩展植被增强指数(extented enhanced vegetation index, EEVI)		$E E V I = 2.5 (N I R + S W I R 1 - R) N I R + 2.5 (S W I R 1 + 6 N I R + R - 7.5 S W I R 1 - R) B + 1$			[55]

Tab.1 Modeling exponential formula

[1]	Nawar S, Buddenbaum H, Hill J, et al. Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods (PLSR and MARS)[J]. Remote Sensing, 2014, 6(11):10813-10834. doi: 10.3390/rs61110813 url: http://www.mdpi.com/2072-4292/6/11/10813
[2]	刘恩, 王军涛, 常步辉, 等. 小开河引黄灌区土壤盐渍化定量遥感反演[J]. 中国农村水利水电, 2019(12):20-24.
[2]	Liu E, Wang J T, Chang B H, et al. Quantitative remote sensing inversion of soil salinization in Xiaokaihe Yellow River irrigation district[J]. China Rural Water and Hydropower, 2019(12):20-24.
[3]	Wen W, Timmermans J, Chen Q, et al. A review of remote sensing challenges for food security with respect to salinity and drought threats[J]. Remote Sensing, 2020, 13(1):3010006.
[4]	Chen H, Ma Y, Zhu A, et al. Soil salinity inversion based on differentiated fusion of satellite image and ground spectra[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 101:102360. doi: 10.1016/j.jag.2021.102360 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243421000672
[5]	Metternicht G I, Zinck J A. Remote sensing of soil salinity:Potentials and constraints[J]. Remote Sensing of Environment, 2003, 85(1):1-20. doi: 10.1016/S0034-4257(02)00188-8 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425702001888
[6]	Perri S, Suweis S, Holmes A, et al. River basin salinization as a form of aridity[J]. Proceedings of the National Academy of Sciences, 2020, 117(30):17635-17642. doi: 10.1073/pnas.2005925117 url: https://pnas.org/doi/full/10.1073/pnas.2005925117
[7]	Wang Z, Zhang F, Zhang X, et al. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index[J]. Science of the Total Environment, 2021, 775(12):145807. doi: 10.1016/j.scitotenv.2021.145807 url: https://linkinghub.elsevier.com/retrieve/pii/S0048969721008743
[8]	吴亚坤, 刘广明, 苏里坦, 等. 多源数据的区域土壤盐渍化精确评估[J]. 光谱学与光谱分析, 2018, 38(11):214-219.
[8]	Wu Y K, Liu G M, Su L T, et al. Accurate evaluation of regional soil salinization using multi-source data[J]. Spectroscopy and Spectral Analysis, 2018, 38(11):214-219.
[9]	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.
[9]	Yan L 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.
[10]	Ivushkin K, Bartholomeus H, Bregt A K, et al. Satellite thermography for soil salinity assessment of cropped areas in Uzbekistan[J]. Land Degradation and Development, 2017, 28(3):870-877. doi: 10.1002/ldr.2670 url: https://onlinelibrary.wiley.com/doi/10.1002/ldr.2670
[11]	Abduldaem S A, Majed I, Ayad M F Q, et al. Detection and modeling of soil salinity variations in arid lands using remote sensing data[J]. Open Geosciences, 2021, 13(1):443-453. doi: 10.1515/geo-2020-0244 url: https://www.degruyter.com/document/doi/10.1515/geo-2020-0244/html
[12]	Aldabaa A A A, Weindorf D C, Chakraborty S, et al. Combination of proximal and remote sensing methods for rapid soil salinity quantification[J]. Geoderma, 2015,239-240:34-46.
[13]	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):69-80.
[14]	Qi G, Chang C, Yang W, et al. Soil salinity inversion in coastal corn planting areas by the satellite-UAV-ground integration approach[J]. Remote Sensing, 2021, 13(16):13163100.
[15]	Zhou X, Zhang F, Liu C, et al. Soil salinity inversion based on novel spectral index[J]. Environmental Earth Sciences, 2021, 80(16):1-13. doi: 10.1007/s12665-020-09327-2 url: https://doi.org/10.1007/s12665-020-09327-2
[16]	关红, 武丹. 干旱区土壤盐渍化遥感估测模型研究——以宁夏平罗县为例[J]. 宁夏工程技术, 2017, 16(4):363-366.
[16]	Guan H, Wu D. Research on models for the remote sensing of soil salinization in the arid region:A case of Pingluo,Ningxia[J]. Ningxia Engineering Technology, 2017, 16(4):363-366.
[17]	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(2):156-175. doi: 10.1016/j.jag.2013.06.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243413000743
[18]	李德仁, 童庆禧, 李荣兴, 等. 高分辨率对地观测的若干前沿科学问题[J]. 中国科学:地球科学, 2012, 42(6):805-813.
[18]	Li D R, Tong Q X, Li R X, et al. Current issues in high-resolution earth observation technology[J]. Scientia Sinica(Terrae), 2012, 42(6):805-813.
[19]	Sahana M, Rehman S, Patel P P, et al. Assessing the degree of soil salinity in the Indian Sundarban Biosphere Reserve using measured soil electrical conductivity and remote sensing data-derived salinity indices[J]. Arabian Journal of Geosciences, 2020, 13(24):1289. doi: 10.1007/s12517-020-06310-w url: https://doi.org/10.1007/s12517-020-06310-w
[20]	Zhuang Q, Shao Z, Huang X, et al. Evolution of soil salinization under the background of landscape patterns in the irrigated northern slopes of Tianshan Mountains,Xinjiang,China[J]. Catena, 2021, 206(8):105561. doi: 10.1016/j.catena.2021.105561 url: https://linkinghub.elsevier.com/retrieve/pii/S0341816221004197
[21]	Wang J, Peng J, Li H, et al. Soil salinity mapping using machine learning algorithms with the Sentinel-2 MSI in arid areas,China[J]. Remote Sensing, 2021, 13(2):13020305.
[22]	Bouaziz M, Gloaguen R, Faust D. Modeling of soil salinity based on remote sensing processing and geochemical analysis in southern Tunisia[C]// EGU General Assembly Conference, 2013:1785.
[23]	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. doi: 10.1007/s11053-021-09925-2 url: https://doi.org/10.1007/s11053-021-09925-2
[24]	Li Z, Li Y, An X, et al. Spatial prediction of soil salinity in a semiarid oasis:Environmental sensitive variable selection and model comparison[J]. Chinese Geographical Science, 2019, 29(5):784-797. doi: 10.1007/s11769-019-1071-x url: https://doi.org/10.1007/s11769-019-1071-x
[25]	Mahajan G R, Das B, Gaikwad B, et al. Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near-infrared hyperspectral data[J]. Catena, 2020, 198:105041. doi: 10.1016/j.catena.2020.105041 url: https://linkinghub.elsevier.com/retrieve/pii/S0341816220305919
[26]	Farifteh J, VanderMeer F D, 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. doi: 10.1016/j.rse.2007.02.005 url: https://linkinghub.elsevier.com/retrieve/pii/S003442570700082X
[27]	Wang L, Zhang B, Shen Q, et al. Estimation of soil salt and ion contents based on hyperspectral remote sensing data:A case study of Baidunzi Basin,China[J]. Water, 2021, 13(4):559. doi: 10.3390/w13040559 url: https://www.mdpi.com/2073-4441/13/4/559
[28]	Farifteh J, Farshad A, George R J. Assessing salt-affected soils using remote sensing,solute modelling,and geophysics[J]. Geoderma, 2006, 130(3-4):191-206. doi: 10.1016/j.geoderma.2005.02.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0016706105000522
[29]	Ding J, Yang S, Shi Q, et al. Using apparent electrical conductivity as indicator for investigating potential spatial variation of soil salinity across seven oases along Tarim River in southern Xinjiang,China[J]. Remote Sensing, 2020, 12(16):2601. doi: 10.3390/rs12162601 url: https://www.mdpi.com/2072-4292/12/16/2601
[30]	魏龙, 王维真, 吴月茹, 等. 土壤水盐介电模型对比与分析[J]. 遥感技术与应用, 2017, 32(6):1022-1030.
[30]	Wei L, Wang W Z, Wu Y R, et al. Comparative analysis of soil water and salt dielectric model[J]. Remote Sensing Technology and Application, 2017, 32(6):1022-1030.
[31]	姚远, 丁建丽, 阿尔达克·克里木, 等. 基于实测高光谱和电磁感应数据的区域土壤盐渍化遥感监测研究[J]. 光谱学与光谱分析, 2013, 33(7):1917-1921.
[31]	Yao Y, Ding J L, Ardaq K, et al. Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data[J]. Spectroscopy and Spectral Analysis, 2013, 33(7):1917-1921.
[32]	Elnaggar A A, Noller J S. Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas[J]. Remote Sensing, 2009, 2(1):151-165. doi: 10.3390/rs2010151 url: http://www.mdpi.com/2072-4292/2/1/151
[33]	Zahraa M A, Bannari A, Hassan R, et al. Validation and comparison of physical models for soil salinity mapping over an arid landscape using spectral reflectance measurements and Landsat-OLI data[J]. Remote Sensing, 2021, 13(3):494. doi: 10.3390/rs13030494 url: https://www.mdpi.com/2072-4292/13/3/494
[34]	张飞, 丁建丽, 何祺胜. 塔里木盆地北缘绿洲盐渍化地地物光谱特征分析[J]. 东北林业大学学报, 2008(6):37-42.
[34]	Zhang F, Ding J L, He Q S. Analysis of spectral characteristics of salinized land in oasis in the northern margin of Tarim Basin[J]. Journal of Northeast Forestry University, 2008(6):37-42.
[35]	马驰. 基于Sentinel-2A遥感数据对吉林白城市土壤含盐量的反演[J]. 干旱区研究, 2020, 37(3):591-597.
[35]	Ma C. Inversion of soil salt content based on Sentinel-2A remote sensing in Baicheng City,Jilin Province[J]. Arid Zone Research, 2020, 37(3):591-597.
[36]	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. doi: 10.1016/j.crte.2011.09.003 url: https://linkinghub.elsevier.com/retrieve/pii/S1631071311001945
[37]	关红, 贾科利, 张至楠, 等. 盐渍化土壤光谱特征分析与建模[J]. 国土资源遥感, 2015, 27(2):100-104.doi:10.6046/gtzyyg.2015.02.16. doi: 10.6046/gtzyyg.2015.02.16
[37]	Guan H, Jia K L, Zhang Z N, et al. Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis[J]. Remote Sensing for Natural Resources, 2015, 27(2):100-104.doi:10.6046/gtzyyg.2015.02.16. doi: 10.6046/gtzyyg.2015.02.16
[38]	李彪, 王耀强. 土壤盐渍化雷达反演模拟研究[J]. 干旱区资源与环境, 2015, 29(8):180-184.
[38]	Li B, Wang Y Q. Radar inversion and simulation of salty soil salinization[J]. Journal of Arid Land Resources and Environment, 2015, 29(8):180-184.
[39]	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
[40]	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.
[40]	Zhang Z T, Wei G F, Yao Z H, et al. Soil salt lnversion model based on UAV multispectral remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12):151-160.
[41]	周晓红, 张飞, 张海威, 等. 艾比湖湿地自然保护区土壤盐分多光谱遥感反演模型[J]. 光谱学与光谱分析, 2019, 39(4):1229-1235.
[41]	Zhou X H, Zhang F, Zhang H W, et al. A study of soil salinity inversion based on multispectral remote sensing index in Ebinur Lake wetland nature reserve[J]. Spectroscopy and Spectral Analysis, 2019, 39(4):1229-1235.
[42]	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. doi: 10.1007/s11053-021-09925-2 url: https://doi.org/10.1007/s11053-021-09925-2
[43]	Wang X, Zhang F, Ding J, et al. Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR),Northwest China,based on a Bootstrap-BP neural network model and optimal spectral indices[J]. Science of the Total Environment, 2018, 615:918. doi: 10.1016/j.scitotenv.2017.10.025 url: https://linkinghub.elsevier.com/retrieve/pii/S0048969717327122
[44]	Tripathi N K, Rai B K, Dwivedi P. Spatial modeling of soil alkalinity in GIS environment using IRS data[C]// 18th Asian Conference on Remote Sensing,Kualalampur, 1997:81-86.
[45]	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):96-109. doi: 10.1016/j.agwat.2004.09.038 url: https://linkinghub.elsevier.com/retrieve/pii/S0378377405000910
[46]	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, 30:1-8. doi: 10.1016/0016-7061(83)90053-8 url: https://linkinghub.elsevier.com/retrieve/pii/0016706183900538
[47]	Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F, et al. Digital mapping of soil salinity in Ardakan region,central Iran[J]. Geoderma, 2014, 213:15-28. doi: 10.1016/j.geoderma.2013.07.020 url: https://linkinghub.elsevier.com/retrieve/pii/S0016706113002565
[48]	Chen J M. Evaluation of vegetation indices and modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996, 22(3):229 -242. doi: 10.1080/07038992.1996.10855178 url: http://www.tandfonline.com/doi/abs/10.1080/07038992.1996.10855178
[49]	Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment, 2001, 76(2):156-172. doi: 10.1016/S0034-4257(00)00197-8 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425700001978
[50]	Liu H Q, Huete A R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2):457-465. doi: 10.1109/TGRS.1995.8746027 url: https://ieeexplore.ieee.org/document/8746027/
[51]	Jiang Z, Huete A R, Chen J, et al. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction[J]. Remote Sensing of Environment, 2006, 101(3):366-378. doi: 10.1016/j.rse.2006.01.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425706000290
[52]	Qi J, Chehbouni A, Huete A R, et al. A modified soil adjusted vegetation index[J]. Remote Sensing of Environment, 1994, 48(2):119-126. doi: 10.1016/0034-4257(94)90134-1 url: https://linkinghub.elsevier.com/retrieve/pii/0034425794901341
[53]	Wu W, Mhaimeed A S, Al-Shafie W M, et al. Mapping soil salinity changes using remote sensing in Central Iraq[J]. Geoderma Reg ional, 2014, 2(3):21-31.
[54]	Jiang H, Ding J, Tashpolat T, et al. Extracting salinized soil information in arid areas using ETM+ data[J]. Acta Pedologica Sinica, 2008, 45(2):222-228.
[55]	Chen H, Zhao G, Chen J, 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.
[56]	Scudiero E, Skaggs T H, Corwin D L. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance[J]. Remote Sensing of Environment, 2015, 169:335-343. doi: 10.1016/j.rse.2015.08.026 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425715301127
[57]	Jin X, Vekerdy Z, Zhang Y, et al. Soil salt content and its relationship with crops and groundwater depth in the Yinchuan Plain (China) using remote sensing[J]. Arid Land Research and Management, 2012, 26(3):227-235. doi: 10.1080/15324982.2012.681339 url: http://www.tandfonline.com/doi/abs/10.1080/15324982.2012.681339
[58]	张海威, 张飞, 李哲, 等. 艾比湖流域盐渍土含水量光谱特征分析与建模[J]. 中国水土保持科学, 2017, 15(1):8-14.
[58]	Zhang H W, Zhang F, Li Z, et al. Modeling and analysis of spectral characteristic of soil water content in the salinized soil of Ebinur Lake Watershed[J]. Science of Soil and Water Conservation, 2017, 15(1):8-14.
[59]	Morshed M M, Islam M T, Jamil R. Soil salinity detection from satellite image analysis:An integrated approach of salinity indices and field data[J]. Environmental Monitoring and Assessment, 2016, 188(2):119. doi: 10.1007/s10661-015-5045-x pmid: 26815557
[60]	Jiang H, Shu H. Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis,Xinjiang,China[J]. Earth Science Informatics, 2018, 12(1):43-56. doi: 10.1007/s12145-018-0358-2 url: https://doi.org/10.1007/s12145-018-0358-2
[61]	马国林, 丁建丽, 韩礼敬, 等. 基于变量优选与机器学习的干旱区湿地土壤盐渍化数字制图[J]. 农业工程学报, 2020, 36(19):124-131.
[61]	Ma G L, Ding J L, Han L J, et al. Digital mapping of soil salinization in arid area wetland based on variable optimized selection and machine learning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(19):124-131.
[62]	段素素, 依力亚斯江·努尔麦麦提, 郭莉丹, 等. 基于全极化微波遥感的干旱区典型绿洲盐渍化信息提取[J]. 湖北农业科学, 2018, 57(2):110-114.
[62]	Duan S S, Eliasjan N, Guo L D, et al. Extraction of salinization information of typical oasis in arid region based on fully polarized microwave remote sensing[J]. Hubei Agricultural Sciences, 2018, 57(2):110-114.
[63]	Wang N, Xue J, Peng J, et al. Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods:A case study from Southern Xinjiang,China[J]. Remote Sensing, 2020, 12(24):4118. doi: 10.3390/rs12244118 url: https://www.mdpi.com/2072-4292/12/24/4118
[64]	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 url: http://link.springer.com/10.1007/s12517-015-2004-3
[65]	毛鸿欣, 贾科利, 张旭. 基于实测高光谱和Sentinel-2B影像的银川平原土壤盐分反演[J]. 云南大学学报(自然科学版), 2021, 43(5):929-941.
[65]	Mao H X, Jia K L, Zhang X. Inversion of soil salinity in Yinchuan Plain based on measured hyperspectral data and Sentinel-2B images[J]. Journal of Yunnan University(Natural Sciences Edition), 2021, 43(5):929-941.
[66]	贾萍萍, 尚天浩, 张俊华, 等. 利用多源光谱信息反演宁夏银北地区干湿季土壤含盐量[J]. 农业工程学报, 2020, 36(17):125-134.
[66]	Jia P P, Shang T H, Zhang J H, et al. Inversion of soil salinity in dry and wet seasons based on multi-source spectral data in Yinbei area of Ningxia,China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(17):125-134.
[67]	Tripathi A, Tiwari R K. A simplified sub-surface soil salinity estimation using synergy of Sentinel-1 SAR and Sentinel-2 multispectral satellite data,for early stages of wheat crop growth in Rupnagar,Punjab,India[J]. Land Degradation and Development, 2021, 32(14):3905-3919. doi: 10.1002/ldr.4009 url: https://onlinelibrary.wiley.com/doi/10.1002/ldr.4009
[68]	Scudiero E, Skaggs T H, Corwin D L. Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance[J]. Ecological Indicators, 2016, 70:276-284. doi: 10.1016/j.ecolind.2016.06.015 url: https://linkinghub.elsevier.com/retrieve/pii/S1470160X16303156
[69]	Taghizadeh-Mehrjardi R, Toomanian N, Shamshirband S, et al. Predicting and mapping of soil salinity using machine learning algorithms in central arid regions of Iran[C]// EGU General Assembly Conference, 2020:18516.
[70]	Wang Z, Zhang X, Zhang F, et al. Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives:A case study in the Ebinur Lake Wetland National Nature Reserve,Northwest China[J]. Ecological Indicators, 2020, 119:1470-1600.
[71]	Wang X, Zhang F, Kung H, 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. doi: 10.1080/01431161.2019.1654142 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1654142
[72]	Erkin N, Zhu L, Gu H, et al. Method for predicting soil salinity concentrations in croplands based on machine learning and remote sensing techniques[J]. Journal of Applied Remote Sensing, 2019, 13(3):034520.
[73]	Xiao D, Wan L. Remote sensing inversion of saline and Alkaline Land based on an improved seagull optimization algorithm and the two-hidden-layer extreme learning machine[J]. Natural Resources Research, 2021, 30(5):3795-3818. doi: 10.1007/s11053-021-09876-8 url: https://doi.org/10.1007/s11053-021-09876-8
[74]	Liu Y, Pan X, Wang C, et al. Can subsurface soil salinity be predicted from surface spectral information? From the perspective of structural equation modelling[J]. Biosystems Engineering, 2016, 152:138-147. doi: 10.1016/j.biosystemseng.2016.06.008 url: https://linkinghub.elsevier.com/retrieve/pii/S1537511015302981
[75]	Wu Y, Wang W, Zhao S, et al. Dielectric properties of saline soils and an improved dielectric model in C-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1):440-452. doi: 10.1109/TGRS.2014.2323424 url: http://ieeexplore.ieee.org/document/6823694/
[76]	Garcia E L A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing[J]. Soil Science Society of America Journal, 2008, 72(1):201-211. doi: 10.2136/sssaj2007.0013 url: http://doi.wiley.com/10.2136/sssaj2007.0013
[77]	丁建丽, 姚远, 王飞. 基于三维光谱特征空间的干旱区土壤盐渍化遥感定量研究[J]. 土壤学报, 2013, 50(5):853-861.
[77]	Ding J L, Yao Y, Wang F. Quantitative remote sensing of soil salinization in arid regions based on three dimensional spectrum eigen spaces[J]. Acta Pedologica Sinica, 2013, 50(5):853-861.
[78]	李艳华, 丁建丽, 孙永猛, 等. 基于三维特征空间的土壤盐渍化遥感模型[J]. 水土保持研究, 2015, 22(4):113-117,121.
[78]	Li Y H, Ding J L, Sun Y M, et al. Remote sensing monitoring models of soil salinization based on the three dimensional feature space of MSAVI-WI-SI[J]. Research of Soil and Water Conservation, 2015, 22(4):113-117,121.
[79]	Guo B, Yang F,Han,B, et al. A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat8 OLI imagery based on VI-SI feature space[J]. Remote Sensing Letters, 2019, 10(8):796-805. doi: 10.1080/2150704X.2019.1610981 url: https://www.tandfonline.com/doi/full/10.1080/2150704X.2019.1610981

[1]	YU Hang, AN Na, WANG Jie, XING Yu, XU Wenjia, BU Fan, WANG Xiaohong, YANG Jinzhong. High-resolution remote sensing-based dynamic monitoring of coal mine collapse areas in southwestern Guizhou: A case study of coal mine collapse areas in Liupanshui City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 310-318.
[2]	DAI Yunhao, GUAN Yao, FENG Chunyong, JIANG Min, HE Xinghong. Extraction and analysis of soil salinization information of Alar reclamation area based on spectral index modeling[J]. Remote Sensing for Natural Resources, 2023, 35(1): 205-212.
[3]	WANG Juan, WANG Zhihong, ZHANG Jianguo, CHU Na, LI Si, YIN Zhan. Remote sensing monitoring and impact intensity assessment of human activities in Henan national nature reserves[J]. Remote Sensing for Natural Resources, 2022, 34(4): 235-242.
[4]	XIN Rongfang, LI Zongren, ZHANG Kun, ZHANG Xing, HUANG Li, LIU Baoshan. Remote sensing monitoring of the dynamic changes in geologic hazards in the Huangshui River basin of Qinghai Province[J]. Remote Sensing for Natural Resources, 2022, 34(4): 254-261.
[5]	TONG Jing, YANG Jinzhong, DU Xin, DU Xiaomin, LI Chunbo, AN Na. Remote sensing-based monitoring of the treatment and redevelopment of the brownfields: A case study of brownfields in the risk control and rehabilitation list of Zhejiang Province[J]. Remote Sensing for Natural Resources, 2022, 34(3): 235-239.
[6]	ZUO Lu, SUN Leigang, LU Junjing, XU Quanhong, LIU Jianfeng, MA Xiaoqian. MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region[J]. Remote Sensing for Natural Resources, 2022, 34(2): 203-214.
[7]	YU Bing, TAN Qingxue, LIU Guoxiang, LIU Fuzhen, ZHOU Zhiwei, HE Zhiyong. Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification[J]. Remote Sensing for Natural Resources, 2021, 33(4): 26-33.
[8]	WU Xia, WANG Zhangjun, FAN Liqin, LI Lei. 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 & Resources, 2021, 33(2): 124-133.
[9]	CHEN Dong, YAO Weiling. Automatic numbering and method improvement of mine patches based on ArcPy and custom ArcToolbox[J]. Remote Sensing for Land & Resources, 2021, 33(2): 262-269.
[10]	WANG Jie, LIU Xiaoyang, YANG Jinzhong, ZHOU Yingjie, An Na, WANG Zhihui. Typical model analysis of mine geological environment restoration and management in Zhejiang Province based on domestic high-resolution satellite data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 216-221.
[11]	DIAO Mingguang, LIU Wenjing, LI Jing, LIU Fang, WANG Yanzuo. Dynamic change detection method of vector result data in mine remote sensing monitoring[J]. Remote Sensing for Land & Resources, 2020, 32(3): 240-246.
[12]	Haigang SHI, Chunli LIANG, Jianyong ZHANG, Chunlei ZHANG, Xu CHENG. Remote sensing survey of the influence of coastline changes on the thermal discharge in the vicinity of Tianwan Nuclear Power Station[J]. Remote Sensing for Land & Resources, 2020, 32(2): 196-203.
[13]	Xi LIU, Lina HAO, Xianhua YANG, Jie HUANG, Zhi ZHANG, Wunian YANG. Research and implementation of rapid statistical methods for mine remote sensing monitoring indicators[J]. Remote Sensing for Land & Resources, 2020, 32(2): 259-265.
[14]	Jie WANG, Yaqiu YIN, Hang YU, Cunhao JIANG, Yu WAN. Remote sensing monitoring of mine geological environment in Zhejiang Province based on RS and GIS[J]. Remote Sensing for Land & Resources, 2020, 32(1): 232-236.
[15]	Yuling ZHAO, Jinzhong YANG, Yaqiu YIN, Hang ZHAO, Jinbao HE, Han ZHANG. Research on remote sensing monitoring of zirconium-titanium sand mine exploitation and strategies of ecological restoration on the eastern beach of Hainan Island[J]. Remote Sensing for Land & Resources, 2019, 31(4): 143-150.

Viewed

Full text

Abstract

Cited

Shared

Discussed