Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Year 2019, Volume: 6 Issue: 1, 33 - 49, 12.04.2019

Taha Gorji Aylin Yıldırım Elif Sertel , Ayşegül Tanık

https://doi.org/10.30897/ijegeo.500452

Cited By: 27

Abstract

Soil salinization
is one of the severe land-degradation problems due to its adverse effects on
land productivity. Each year several hectares of lands are degraded due to
primary or secondary soil salinization, and as a result, it is becoming a major
economic and environmental concern in different countries.  Spatio-temporal mapping of soil salinity is
therefore important to support decision-making procedures for lessening adverse
effects of land degradation due to the salinization. In that sense, satellite-based
technologies provide cost effective, fast, qualitative and quantitative spatial
information on saline soils.

 

The main
objective of this work is to highlight the recent remote sensing (RS) data and
methods to assess soil salinity that is a worldwide problem. In addition, this
study indicates potential linkages between salt-affected land and the
prevailing climatic conditions of the case study areas being examined. Web of Science
engine is used for selecting relevant articles. "Soil salinity" is
used as the main keyword for finding "articles" that are published
from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote
sensing", "satellite" and "aerial" were used to filter
the articles. After that, 100 case studies from 27 different countries were
selected. Remote sensing based researches were further overviewed regarding to
their location, spatial extent, climate regime, remotely sensed data type,
mapping methods, sensing approaches together with the reason of salinity for
each case study. In addition, soil salinity mapping methods were examined to
present the development of different RS based methods with time. Studies are
shown on the Köppen-Geiger climate classification map. Analysis of the map
illustrates that 63% of the selected case study areas belong to arid and
semi-arid regions. This finding corresponds to soil characteristics of arid
regions that are more susceptible to salinization due to extreme temperature,
high evaporation rates and low precipitation.

Keywords

Soil Salinity, Remote Sensing, Mapping Methods, Sensing Approaches

References

• Abbas, A., Khan, S., Hussain, N., Hanjra, M.A., Akbar, S. (2013). Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Phys. Chem. Earth, 55–57, 43–52.
• Abdellatif, D. (2017). Optical tool for salinity detection by remote sensing spectroscopy: application on Oran watershed. J. of Applied Remote Sensing, 11(3), 036010, 1-21.
• Afrasinei, G.M., Melis, M.T., Buttau, C., Bradd, J.M., Arras, C., Ghiglieri, G. (2018). Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area , Algeria). J. of Applied Remote Sensing, 11(1), 016025, 1-28.
• Akramkhanov, A., Martius, C., Park, S.J., Hendrickx, J.M.H. (2011). Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163, 55–62.
• Akramkhanov, A., Vlek, P.L.G. (2012). The assessment of spatial distribution of soil salinity risk using neural network. Environ. Monit. Assess., 184, 2475–2485.
• Alavi Panah, S.K., Goossens, R., Matinfar, H.R., Mohamadi, H., Ghadiri, M., Irannegad, H., Alikhah Asl, M. (2008). The efficiency of Landsat TM and ETM+ thermal data for extracting soil information in arid regions. J. Agric. Sci. Technol., 10, 439–460.
• Aldabaa, A.A.A., Weindorf, D.C., Chakraborty, S., Sharma, A., Li, B. (2015). Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma, 239–240, 34–46.
• Aldakheel, Y.Y. (2011). Assessing NDVI spatial pattern as related to irrigation and soil salinity management in Al-Hassa Oasis, Saudi Arabia. J. Indian Soc. Remote Sens., 39, 171–180.
• Alexakis, D.D., Daliakopoulos, I.N., Panagea, I.S., Tsanis, I.K. (2018). Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece. Geocarto Int., 6049, 1–18. Allbed, A., Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv. Remote Sens., 2, 373–385.
• Allbed, A., Kumar, L., Aldakheel, Y.Y. (2014a). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: applications in a date palm dominated region. Geoderma, 230–231, 1–8.
• Allbed, A., Kumar, L., Sinha, P. (2014b). Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques. Remote Sens., 6, 1137–1157.
• An, D., Zhao, G., Chang, C., Wang, Z., Li, P. (2016). Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta. Int. J. Remote Sens., 37, 455–470.
• Arnous, M.O., El-Rayes, A.E., Green, D.R. (2015). Hydrosalinity and environmental land degradation assessment of the East Nile Delta region. Egypt. J. Coast. Conserv., 19, 491–513.
• Arnous, M.O., Green, D.R. (2015). Monitoring and assessing waterlogged and salt-affected areas in the Eastern Nile Delta region, Egypt, using remotely sensed multi-temporal data and GIS. J. Coast. Conserv., 19, 369–391.
• Arrouays, D., Lagacherie, P., Hartemink, A.E. (2017). Digital soil mapping across the globe. Geoderma Reg. 9, 1–4.
• Bai, L., Wang, C., Zang, S., Zhang, Y., Hao, Q., Wu, Y. (2016). Remote sensing of soil alkalinity and salinity in the Wuyu’er-Shuangyang River Basin, Northeast China. Remote Sens., 8, 163.
• Bannari, A., Guédon, A.M. (2016). Communications in soil science and plant analysis mapping slight and moderate saline soils in irrigated agricultural land using advanced land imager sensor (EO-1) data and semi-empirical models. Commun. Soil Sci. Plant Anal., 47, 1883–1906.
• Bannari, A., Guedon, A.M., El-Harti, A., Cherkaoui, F.Z., El-Ghmari, A. (2008). Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor. Commun. Soil Sci. Plant Anal. 39, 2795–2811.
• Bhatt, M.J., Patel, A.D., Bhatti, P.M., Pandey, A.N. (2008). Effect of soil salinity on growth, water status and nutrient accumulation in seedlings of ziziphus mauritiana (RHAMNACEAE). Journal of Fruit and Ornamental Plant Research, 16, 383–401.
• Bouaziz, M., Matschullat, J., Gloaguen, R. (2011). Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil. Comptes Rendus-Geosci., 343, 795–803.
• Brunner, P., Li, H.T., Kinzelbach, W., Li, W.P. (2007). Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data. Int. J. Remote Sens., 28, 3341–3361.
• Carol, E.S., Kruse, E.E., Cellone, F.A. (2015). Salinization of soils in marshes. Case study: Humedal of Samborombón Bay, (Argentina Salinización de suelos en marismas. Caso de estudio: Humedal de la Bahía Samborombón, Argentina). Rev. la Fac. Ciencias Agrar., 47, 97–107.
• Casterad, A. (2018). Assessment of soil salinity during the first years of transition from flood to sprinkler irrigation. Sensors., 18, 616. Chen, D., Chen, H.W. (2013). Using the Köppen classi fi cation to quantify climate variation and change : An example for 1901–2010. Environmental Development, 6, 69–79.
• Chuangye, S., Hongxu, R.E.N., Chong, H. (2016). Estimating soil salinity in the Yellow River Delta, Eastern China—an integrated approach using spectral and terrain indices with the generalized additive model. Pedosph. An Int. J., 26, 626–635.
• Daliakopoulos, I.N., Tsanis, I.K., Koutroulis, A., Kourgialas, N.N., Varouchakis, A.E., Karatzas, G.P., Ritsema, C.J. (2016). The threat of soil salinity: A European scale review. Sci. Total Environ., 573, 727–739.
• Das, S., Choudhury, M.R., Das, S. (2016). Earth observation and geospatial techniques for soil salinity and land capabilityaAssessment over Sundarban Bay of Bengal Coast, India. Geodesy and Cartography. 65, 163–192.
• Ding, J. L., Wu, C. M., Tiyip, T. (2011). Study on Soil Salinization Information in Arid Region Using Remote Sensing Technique. Agric. Sci. China, 10, 404–411.
• Ding, J., Yu, D. (2014). Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan–Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments. Geoderma, 235–236, 316–322.
• Dutkiewicz, A., Lewis, M., Ostendorf, B. (2009). Evaluation and comparison of hyperspectral imagery for mapping surface symptoms of dryland salinity. Int. J. Remote Sens., 30, 693–719.
• Dutkiewicz, A. (2006). Evaluating hyperspectral imagery for mapping surface symptoms of dryland salinity with hyperspectral imagery. (Ph.D Thesis). Discipline of Soil and Land Systems School of Earth and Environmental Sciences The University of Adelaide, Australia.
• Ekercin, S., Ormeci, C. (2008). Estimating soil salinity using satellite remote sensing data and real-time field sampling. Environmental Engineering Science, 25(7), 981-988.
• El, A., Lhissou, R., Chokmani, K., Ouzemou, J., Hassouna, M., Mostafa, E., El, A. (2016). Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. Int. J. Appl. Earth Obs. Geoinf., 50, 64–73.
• Eldeiry, A.A., Garcia, L.A. (2008). Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci. Soc. Am. J., 72, 201-211.
• Eldeiry, A.A., Garcia, L.A., Reich, R.M. (2008). Soil salinity sampling strategy using spatial modeling techniques, remote sensing, and field data. J. Irrig. Drain. Eng., 134, 768–777.
• Elhag, M., Bahrawi, J.A. (2017). Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques. Geosci. Instrum. Method. Data Syst., 6, 149–158. Elhag, M. (2016). Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems, Saudi Arabia. Journal of Sensors, Article ID 7596175, 8 p.
• Elnaggar, A. A., Noller, J.S. (2009). Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sens., 2, 151–165. Fallah Shamsi, S.R., Zare, S., Abtahi, S.A. (2013). Soil salinity characteristics using moderate resolution imaging spectroradiometer (MODIS) images and statistical analysis. Arch. Agron. Soil Sci., 59, 471–489.
• Fan, X., Liu, Y., Tao, J., Weng, Y. (2015). Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression. Remote Sens., 7, 488–511.
• Fan, X., Pedroli, B., Liu, G., Liu, Q., Liu, H., Shu, L. (2012). Soil salinity development in the yellow river delta in relation to groundwater dynamics. Land Deg. and Development., 23, 175–189.
• Fan, X., Weng, Y., Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. Int. J. Appl. Earth Obs. Geoinf., 52, 32–41. Goldshleger, N., Ben-Dor, E., Lugassi, R., Eshel, G. (2010). Soil degradation monitoring by remote sensing: examples with three degradation processes. Soil Sci. Soc. Am. J., 74, 1433-1445.
• Goldshleger, N., Livne, I., Chudnovsky, A., Ben-Dor, E. (2012). New results in integrating passive and active remote sensing methods to assess soil salinity: a case study from Jezre’el Valley, Israel. Soil Science, 177(6), 392-401.
• Gorji, T., Alganci, U., Sertel, E., Tanik, A. (2018). Comparing two different spatial interpolation approaches to characterize spatial variability of soil properties in Tuz Lake Basin – Turkey, Fresenius Environmental Bulletin Journal, in press.
• Gorji, T., Sertel, E., Tanik, A. (2017a). Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecol. Indic., 74, 384–391.
• Gorji, T., Sertel, E., Tanik, A. (2017b). Recent Satellite Technologies for Soil Salinity Assessment with Special Focus on Mediterranean Countries, Fresenius Environmental Bulletin Journal, 26(1), 196-203.
• Gorji, T., Tanik, A., Sertel, E. (2015). Soil Salinity Prediction, Monitoring and Mapping Using Modern Technologies, Procedia Earth and Planetary Science. 15, 507 – 512.
• Goto, K., Goto, T., Nmor, J.C., Minematsu, K., Gotoh, K. (2014). Evaluating salinity damage to crops through satellite data analysis: application to typhoon affected areas of southern Japan. Nat. Hazards, 75, 2815–2828.
• Grunwald, S., Vasquesy, G.M., Rivero, R.G. (2015). Fusion of soil and remote sensing data to model soil properties. Advances in Agronomy, 131, 1-109.
• Guo, Y., Shi, Z., Zhou, L. Qing, Jin, X., Tian, Y. Feng, Teng, Fen, H. (2013). Integrating remote sensing and proximal sensors for the detection of soil moisture and salinity variability in coastal areas. J. Integr. Agric., 12, 723–731. Guo, Y., Shi, Z., Li, H.Y., Triantafilist, J. (2013). Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China. Soil Use and Management, 29, 445–456.
• Gutierrez, M., Johnson, E. (2010). Temporal variations of natural soil salinity in an arid environment using satellite images. J. South Am. Earth Sci., 30, 46–57.
• Hamzeh, S., Naseri, A. A., AlaviPanah, S.K., Mojaradi, B., Bartholomeus, H.M., Clevers, J.G.P.W., Behzad, M. (2013). Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices. Int. J. Appl. Earth Obs. Geoinf., 21, 282–290.
• Hereher, M.E., Ismael, H. (2016). The application of remote sensing data to diagnose soil degradation in the Dakhla depression–Western Desert, Egypt. Geocarto Int. 31, 527–543.
• Iqbal, F. (2011). Detection of salt-affected soil in rice-wheat area using satellite image. African J. Agric. Res., 6, 4973–4982.Ivits, E., Cherlet, M., Tóth, T., Ska, K.E.L.Ń., Tóth, G. (2013). Characterisation of productivity limitation of salt-affected lands in different climatic regions of Europe using remote sensing derived productivity indicators. Land Deg. and Development., 24, 438–452.
• Ivushkin, K., Bartholomeus, H., Bregt, A.K., Pulatov, A. (2017). Satellite thermography for soil salinity assesment of cropped areas in Uzbekistan. Land Deg. and Development., 28, 870–877.
• Jabbar, M.T., Zhou, J. (2012). Assessment of soil salinity risk on the agricultural area in Basrah Province, Iraq: Using remote sensing and GIS techniques. J. Earth Sci., 23, 881–891.
• Jacobus, S., Niekerk, A.V. (2016a). Identification of WorldView-2 spectral and spatial factors in detecting salt accumulation in cultivated fields. Geoderma, 273, 1–11.
• Jacobus, S., Niekerk, A.V. (2016b). An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level. Int. J. Appl. Earth Obs. Geoinf., 49, 138–150.
• Jiang, H., Xu, J. (2018). Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China. Journal of Applied Remote Sensing, 11, 016043-1.
• Jin, P., Li, P., Wang, Q., Pu, Z. (2015). Developing and applying novel spectral feature parameters for classifying soil salt types in arid land. Ecol. Indic., 54, 116–123.
• Jin, X.M., Vekerdy, Z., Zhang, Y.K., Liu, J.T. (2012). Soil salt content and its relationship with crops and groundwater depth in the Yinchuan Plain (China) using remote sensing. Arid L. Res. Manag., 26, 227–235.
• Judkins, G., Myint, S. (2012). Spatial variation of soil salinity in the Mexicali Valley, Mexico: application of a practical method for agricultural monitoring. Environ. Manage., 50, 478–489.
• Justin, G.K., Suresh, K. (2015). Hyperspectral remote sensing in characterizing soil salinity severity using SVM technique-a case study of alluvial plains. Int. J. Adv. Remote Sens.and GIS, 4, 1344–1360.
• Kobryn, H.T., Lantzke, R., Bell, R., Admiraal, R. (2015). Remote sensing for assessing the zone of benefit where deep drains improve productivity of land affected by shallow saline groundwater. J. Environ. Manage.,150, 138–148.
• Laiskhanov, S.U., Otarov, A., Savin, I.Y., Tanirbergenov, S.I., Mamutov, Z.U., Duisekov, S.N., Zhogolev, A. (2016). Dynamics of soil salinity in irrigation areas in South Kazakhstan. Polish J. Env. Studies, 25, 2469–2475.
• Liu, L., Ji, M., Buchroithner, M. (2018). A case study of the forced invariance approach for soil salinity estimation in vegetation-covered terrain using airborne hyperspectral imagery. Int. J. Geo-Inf, 7(2), 48.
• Lobell, D.B., Lesch, S.M., Corwin, D.L., Ulmer, M.G., Anderson, K.A., Potts, D.J., Doolittle, J.A., Matos, M.R., Baltes, M.J. (2010). Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI. J. Environ. Qual., 39, 35-41.
• Lobell, D.B., Ortiz-monasterio, J.I. (2007). Identification of saline soils with multiyear remote sensing of crop yields. Soil Science Society of America Journal,71, 777–783.
• Ma, L., Ma, F., Li, J., Gu, Q., Yang, S., Wu, D., Feng, J., Ding, J. (2017). Characterizing and modeling regional-scale variations in soil salinity in the arid oasis of Tarim Basin, China. Goederma, 305, 1–11.
• Ma, L., Yang, S. (2018). Modeling variations in soil salinity in the oasis of Junggar. Land Degr. and Development, 29(3), 551–562. Manchanda, M.L., Kudrat, M., Tiwari, A.K. (2002). Soil survey and mapping using remote sensing. Trop. Ecol., 43, 61–74.
• Mandal, A.K., Sharma, R.C. (2011). Delineation and Characterization of Waterlogged Salt-affected Soils in IGNP Using Remote Sensing and GIS. J. Indian Soc. Remote Sens. 39, 39–50.
• Matinfar, H.R., Alavi Panah, S.K., Zand, F., Khodaei, K. (2013). Detection of soil salinity changes and mapping land cover types based upon remotely sensed data. Arab. J. Geosci., 6, 913–919.
• Mayak, S., Tirosh, T., Glick, B.R. (2004). Plant growth-promoting bacteria confer resistance in tomato plants to salt stress. Plant Physiol. Biochem., 42, 565–572.
• Meimei, Z., Ping, W. (2011). Using HJ - I satellite remote sensing data to surveying the saline soil distribution in Yinchuan Plain of China. African J. Agric. Reseearch, 6, 6592–6597.
• Melendez-Pastor, I., Navarro-Pedreño, J., Koch, M., Gómez, I. (2010). Applying imaging spectroscopy techniques to map saline soils with ASTER images. Geoderma, 158, 55–65.
• Meng, L., Zhou, S., Zhang, H., Bi, X. (2016). Estimating soil salinity in different landscapes of the Yellow River Delta through Landsat OLI / TIRS and ETM + Data. J. Coast. Conserv., 20(4), 271–279.
• Metternicht, G.I., Zinck, J.A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sens. Environ., 85, 1–20.
• Mitran, T., Ravisankar, T., Fyzee, M.A., Suresh, J.R., Sujatha, G., Sreenivas, K. (2015). Retrieval of soil physicochemical properties towards assessing salt-affected soils using Hyperspectral Data. Geocarto Int., 30, 701–721.
• Moreira, L.C.J., Teixeira, A.D.S., Galvão, L.S. (2015). Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil. GIScience Remote Sens., 52, 416–436.
• Morshed, M., Islam, T., Jamil, R. (2016). Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data. Envi. Monitoring and Assessment, 188:119, 2-10. Mulder, V.L., Bruin, S. De, Schaepman, M.E., Mayr, T.R. (2011). The use of remote sensing in soil and terrain mapping—A review. Geoderma,162, 1–19. Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. (2014). Modeling and mapping of soil salinity with reflectance spectroscopy and landsat data using two quantitative methods (PLSR and MARS). Remote Sens., 6, 10813–10834.
• Nurmemet, I., Ghulam, A., Tiyip, T., Elkadiri, R., Ding, J.L., Maimaitiyiming, M., Abliz, A., Sawut, M., Zhang, F., Abliz, A., Sun, Q. (2015). Monitoring soil salinization in Keriya River Basin, Northwestern China using passive reflective and active microwave remote sensing data. Remote Sens., 7, 8803–8829.
• Odeh, I.O.A., Onus, A. (2008). Spatial analysis of soil salinity and soil structural stability in a semi-arid region of New South Wales, Australia. Environ. Manage., 42, 265–278.
• Pang, G., Wang, T., Liao, J., Li, S. (2014). Quantitative model based on field-derived spectral characteristics to estimate soil salinity in Minqin County, China. Soil Sci. Soc. Am. J., 78(2), 546-555.
• Periasamy, S., Shanmugam, R.S. (2017). Multispectral and microwave remote sensing models to survey, Land Deg. and Development, 28(4), 1412–1425.
• Phonphan, W., Tripathi, N.K., Tipdecho, T., Eiumnoh, A. (2014). Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network. Geocarto Int., 29, 842–859.
• Quan, Q., Shen, B., Xie, J.C., Luo, W., Wang, W.Y. (2013). Assessing soil salinity in the fields of western China using spatial modeling and remote sensing. Acta Agric. Scand. Sect. B-Soil Plant Sci., 63, 289–296.
• Rahmati, M., Hamzehpour, N. (2017). Quantitative remote sensing of soil electrical conductivity using ETM + and ground measured data. Int. J. Remote Sens., 38, 123–140.
• Rukhovich, D.I., Pankova, E.I., Chernousenko, G.I., Koroleva, P. V. (2010). Long-term salinization dynamics in irrigated soils of the Golodnaya Steppe and methods of their assessment on the basis of remote sensing data. Eurasian Soil Sci., 43, 682–692.
• Satir, O., Berberoglu, S. (2016). Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crop. Res., 192, 134–143.
• Saghafi, M. (2017). Application of remote sensing indices for mapping salt-affected areas by using field data methods. International Journal of Advanced and Applied Sciences, 4, 181–187.
• Scudiero, E., Corwin, D.L., Anderson, R.G., Yemoto, K., Clary, W., Luke, Z., Todd, W. (2017). Remote sensing is a viable tool for mapping soil salinity in agricultural lands. California Agriculture, 71(4), 231-238.
• Scudiero, E., Skaggs, T.H., Corwin, D.L. (2015). Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sens. Environ.,169, 335–343.
• Setia, R., Lewis, M., Marschner, P., Raja Segaran, R., Summers, D., Chittleborough, D. (2013). Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imagery. Land Deg. and Development., 24, 375–384.
• Shrestha, D.P., Farshad, A. (2009). Mapping salinity hazard: an integrated application of remote sensing and modeling-based techniques, Chapter 13. In: Remote Sensing of Soil Salinization: Impact on Land Management. NY,USA: CRC Press. pp. 257-272.
• Sidike, A., Zhao, S., Wen, Y. (2014). Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra. Int. J. Appl. Earth Obs. Geoinf., 26, 156–175.
• Triki Fourati, H., Bouaziz, M., Benzina, M., Bouaziz, S. (2015). Modeling of soil salinity within a semi-arid region using spectral analysis. Arab. J. Geosci., 8(12), 11175–11182.
• Vermeulen, D., Niekerk, A.V. (2016). Evaluation of a WorldView-2 image for soil salinity monitoring in a moderately affected irrigated area. J. Appl. Remote Sens., 10(2), 026025.
• Vermeulen, D., Niekerk, A.V. (2017). Geoderma Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates. Geoderma, 299, 1–12.
• Wang, F., Chen, X., Luo, G.P., Ding, J.L., Chen, X.F. (2013). Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery. J. Arid Land, 5, 340–353.
• Wang, X., Zhang, F., Ding, J., Kung, H., Latif, A., Johnson, V.C. (2018). 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. Sci. Total Environ., 615, 918–930.
• Weng, Y.L., Gong, P., Zhu, Z.L. (2010). A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data. Pedosphere, 20, 378–388.
• Wu, J., Vincent, B., Yang, J., Bouarfa, S., Vidal, A. (2008). Remote sensing monitoring of changes in soil salinity: a case study in inner Mongolia, China. Sensors, 8, 7035–7049.
• Wu, W., Al-shafie, W.M., Mhaimeed, A.S., Ziadat, F., Nangia, V., Payne, W.B. (2014). Soil salinity mapping by multiscale remote sensing in Mesopotamia, Iraq. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4442–4452.
• Xiaoxia, S., Yunhao, C., Jianwei, Y., Jing, L., Cheng, P. (2007). Simulating and forecasting soil-salinisation evolution: A case study on Changling County, Jilin province, China. New Zeal. J. Agric. Res., 50, 975–981.
• Yahiaoui, I., Douaoui, A., Zhang, Q., Ziane, A. (2015). Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis. J. Arid Land, 7, 794–805.
• Ya-kun, W., Jin-song, Y., & Xiao-Ming, L. (2009). Study on spatial variability of soil salinity based on spectral indices and EM38 readings. Spectroscopy and Spectral Analysis, 29(4), 1023-1027.
• Yong-hua, Q., Xiao- liang, D., Hong-yong, G., Aiping, C., Yong-qing, A. Jin- ling, S., Hongm I., Tao, H. (2009). Quantitative retrieval of soil salinity using hyperspectral data in the region of Inner Mongolia hetao irrigation district. Spectroscopy and Spectral Analysis, 29(5), 1362-1366.Yu, R., Liu, T., Xu, Y., Zhu, C., Zhang, Q., Qu, Z., Liu, X., Li, C. (2010). Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China. Agric. Water Manag., 97, 1952–1960.
• Zeng, W.,Zhang, D., Fang,Y.Wu, J., Huang, J. (2018). Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data. J. Appl. Remote Sens., 12(2), 022204.
• Zhang, T.-T., Qi, J.-G., Gao, Y., Ouyang, Z.-T., Zeng, S.-L., Zhao, B. (2015). Detecting soil salinity with MODIS time series VI data. Ecol. Indic., 52, 480–489.
• Zinck, J.A., Metternicht, G. (2009). Soil salinity and salinization hazard, Chapter 1. In: Remote Sensing of Soil Salinization: Impact on Land Management. NY,USA: CRC Press, pp. 3-60.