Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Work and Laboratory Analysis
2.3. Images Data
2.4. Images Data Preprocessing
2.4.1. OLI Reference Scene Preprocessing
2.4.2. Multi-Sensors Data Inter-Calibration
2.5. Image Data Processing
- EC-Predicted: Predicted electrical conductivity semi-empirical model,
- Cst: Scaling factor,
- ρSWIR-1: Reflectance in MSI and OLI SWIR-1 channel, and
- ρSWIR-2: Reflectance in MSI and OLI SWIR-2 channel.
2.6. Climatic Data
2.7. Statistical Analysis and Model Validation
3. Results Analysis and Discussion
3.1. Salinity Model Validation: Visual and Statistical Analysis
3.2. Landsat Series Datasets Inter-Calibration
3.3. Spatiotemporal Salinity Maps and Change Trend Analysis
3.4. Climate Change Impact Assessment
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- McBratney, A.; Field, D.J.; Koch, A. The dimensions of soil security. Geoderma 2014, 213, 203–213. [Google Scholar] [CrossRef] [Green Version]
- Daliakopoulos, I.N.; Tsanis, I.K.; Koutroulis, A.; Kourgialas, N.N.; Varouchakis, A.E.; Karatzas, G.P.; Ritsema, C.J. The threat of soil salinity: A European scale review. Sci. Total Environ. 2016, 573, 727–739. [Google Scholar] [CrossRef] [PubMed]
- Dagar, J.C.; Sharma, P.C.; Chaudhari, S.K.; Jat, H.S.; Ahamad, S. Climate Change vis-a-vis Saline Agriculture: Impact and Adaptation Strategies. In Innovative Saline Agriculture; Dagar, J.C., Sharma, P.C., Sharma, D.K., Singh, A.K., Eds.; Springer: Berlin, Germany, 2016; Volume 518, pp. 5–55. [Google Scholar]
- Meimei, Z.; Yanping, S.; Ping, W. Using HJ-I satellite remote sensing data to surveying the saline soil distribution in Yinchuan Plain of China. Afr. J. Agric. Res. 2011, 6, 6592–6597. [Google Scholar]
- Mashimbye, Z.E. Remote Sensing of Salt-Affected Soil. Ph.D. Thesis, Faculty of Agri-Sciences, Stellenbosch University, Stellenbosch, South Africa, 2013; p. 151. [Google Scholar]
- Nosetto, M.; Acosta, M.; Jayawickreme, D.; Ballesteros, S.; Jackson, R. Land use and topography shape soil and groundwater salinity in central Argentina. Agric. Water Manag. 2013, 129, 120–129. [Google Scholar] [CrossRef]
- 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. Remote Sens. 2014, 6, 1137–1157. [Google Scholar] [CrossRef] [Green Version]
- Yahiaoui, I.; Douaoui, A.; Zhang, Q.; Ziane, A. Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis. J. Arid Land 2015, 7, 794–805. [Google Scholar] [CrossRef]
- Korolyuk, T.V. Soil forming factors: Their role in the formation of saline soils on the plains of Western and Central Ciscaucasia. Eurasian Soil Sci. 2015, 48, 689. [Google Scholar] [CrossRef]
- Mandal, A.; Neenu, S. Impact of Climate Change on Soil Biodiversity: A review. Agric. Rev. 2012, 33, 283–292. [Google Scholar]
- Teh, S.; Koh, H. Climate Change and Soil Salinization: Impact on Agriculture, Water and Food Security. Int. J. Agric. For. Plant. 2016, 2, 1–9. [Google Scholar]
- Gorji, T.; Yıldırım, A.; Sertel, E.; Tanık, A. Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes. Int. J. Environ. Geoinf. 2019, 6, 33–49. [Google Scholar] [CrossRef] [Green Version]
- Karmakar, R.; Das, I.; Dutta, D.; Rakshit, A. Potential Effects of Climate Change on Soil Properties: A Review. Sci. Int. 2016, 4, 51–73. [Google Scholar] [CrossRef] [Green Version]
- Cai, X.; Zhang, X.; Noel, P.H.; Shafiee-Jood, M. Impacts of Climate Change on Agricultural Water Management: A Review. WIREs Water 2015, 2, 439–455. [Google Scholar] [CrossRef]
- Arrouays, D.; Lagacherie, P.; Hartemink, A.E. Digital soil mapping across the globe. Geoderma Reg. 2017, 9, 1–4. [Google Scholar] [CrossRef]
- Szabolcs, I. Salinization of soils and water and its relation to desertification. Desertif. Control Bull. 1992, 21, 32–37. [Google Scholar]
- Jucevica, E.; Melecis, V. Global warming affect collembola community: A long-term study. Pedobiology 2006, 50, 177–184. [Google Scholar] [CrossRef]
- Castro, H.F.; Classen, A.T.; Austin, E.E.; Norby, R.J.; Schad, C.W. Soil Microbial Community Responses to Multiple Experimental Climate Change Drivers. Applied and Environmental Microbiology. Am. Soc. Microbiol. 2010, 76, 999–1007. [Google Scholar]
- Shahid, S.A.; Behnassi, M. Climate Change Impacts in the Arab Region: Review of Adaptation and Mitigation Potential and Practices. In Vulnerability of Agriculture, Water and Fisheries to Climate Change: Toward Sustainable Adaptation Strategies; Behnassi, M., Muteng’e, M.S., Ramachandran, G., Shelat, K.N., Eds.; Springer: Berlin, Germany, 2014; Chapter 2; pp. 15–58. [Google Scholar]
- Hartemink, A. On the relation between soils and climate. In Proceedings of the 20th World Congress of Soil Science, Jeju, South Korea, 8–13 June 2014. [Google Scholar]
- Kapur, S.; Aydın, M.; Akça, E.; Reich, P. Climate Change and Soils. In The Soils of Turkey; World Soils Book Series; Kapur, S., Akça, E., Günal, H., Eds.; Springer: Berlin, Germany, 2018; Chapter 4; pp. 45–55. [Google Scholar]
- Santis, I.P. Soil salinization and land desertification. In Soil Degradation and Desertification in Mediterranean Environments; Rubio, L., Calvo, A., Eds.; Geoforma Log.: Trieste, Italy, 1996; Chapter 290; pp. 105–129. [Google Scholar]
- Shahid, S.A.; Zaman, M.; Heng, L.L. Soil Salinity: Historical Perspectives and a World Overview of the Problem. In Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques; Zaman, M., Ed.; Springer Nature AG: Basel, Switzerland, 2018; Chapter 2; pp. 43–53. [Google Scholar]
- Jacobson, T.; Adams, R.M. Salt and silt in ancient Mesopotamian agriculture. Science 1958, 128, 1251–1258. [Google Scholar] [CrossRef]
- Kurylyk, B.; MacQuarrie, K. The Uncertainty Associated with Estimating Future Groundwater Recharge: A Summary of Recent Research and an Example from a Small Unconfined Aquifer in a Northern Humid-Continental Climate. J. Hydrol. 2013, 492, 244–253. [Google Scholar] [CrossRef]
- Hillel, D. Salinity Management for Sustainable Irrigation; The World Bank: Washington, DC, USA, 2000; p. 92. [Google Scholar]
- Baumhardt, R.L.; Stewart, B.A.; Sainju, U.M. North American Soil Degradation: Processes, Practices and Mitigating Strategies. Sustainability 2015, 7, 2936–2960. [Google Scholar] [CrossRef] [Green Version]
- Naing-OO, A.; Boonthaiiwai, C.; Saenjan, P. Food Security and Socio-economic Impacts of Soil Salinization in Northeast Thailand. Int. J. Environ. Rural Dev. 2013, 4, 76–81. [Google Scholar]
- Ghassemi, F.; Jakeman, A.J.; Nix, H.A. Salinization of Land and Water Resources: Human Causes, Extent Management and Case Studies; UNSW Press: Sydney, Australia; CAB International: Wallingford, UK, 1995; p. 544. [Google Scholar]
- Machado, R.M.; Serralheiro, R.P. Soil Salinity: Effect on Vegetable Crop Growth, Management Practices to Prevent and Mitigate Soil Salinization. Horticulturae 2017, 3, 30. [Google Scholar] [CrossRef]
- Burt, R. Soil Survey Staff, Method 3B6a. Soil Survey Laboratory Methods Manual; USDA-NRCS. GPO: Washington, DC, USA, 2004; Volume 42.
- Zhang, H.K.; Schroder, J.L.; Pittman, J.J.; Wang, J.J.; Payton, M.E. Soil Salinity Using Saturated Paste and 1:1 Soil to Water Extracts. Soil Sci. Soc. Am. J. 2005, 69, 1146–1151. [Google Scholar] [CrossRef]
- Ben-Dor, E.; Irons, J.R.; Epema, G.F. Soil Reflectance. In Manual of Remote Sensing: Remote Sensing for Earth Sciences, 3rd ed.; Rencz, A.N., Ryerson, R.A., Eds.; John Wiley & Son Inc.: New York, NY, USA, 1999; Chapter 2; pp. 111–187. [Google Scholar]
- Ben-Dor, E.; Metternicht, G.; Goldshleger, N.; Mor, E.; Mirlas, V.; Basson, U. Review of Remote Sensing-Based Methods to Assess Soil Salinity. In Remote Sensing of Soil Salinization: Impact on Land Management; Metternicht, G., Zinck, J.A., Eds.; CRC Press Taylor and Francis Group: Boca Raton, FL, USA, 2009; Chapter 13; pp. 39–60. [Google Scholar]
- Metternicht, G.; Zinck, J.A. Remote Sensing of Soil Salinization: Impact on Land Management; CRC Press Taylor and Francis Group: Boca Raton, FL, USA, 2009; p. 374. [Google Scholar]
- Nawar, S.; Buddenbaum, H.; Hill, J.; Kozak, J. Modeling and Mapping of Soil Salinity with Reflectance Spectroscopy and Landsat Data Using Two Quantitative Methods (PLSR and MARS). Remote Sens. 2014, 6, 10813–10834. [Google Scholar] [CrossRef] [Green Version]
- Wu, W.; Al-Shafie, W.M.; Mhaimeed, A.S.; Ziadat, F.; Nangia, V.; Payne, W.B. Soil Salinity Mapping by Multiscale Remote Sensing in Mesopotamia, Iraq. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2014, 7, 4442–4452. [Google Scholar] [CrossRef]
- Fan, X.; Weng, Y.; Tao, J. Towards decadal soil salinity mapping using Landsat time series data. Int. J. Appl. Earth Obs. Geoinf. 2016, 52, 32–41. [Google Scholar] [CrossRef]
- Wang, J.; Ding, J.; Abulimiti, A.; Cai, L. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ 2018, 6, e4703. [Google Scholar] [CrossRef] [Green Version]
- Bannari, A.; Guédon, A.M.; El-Ghmari, A. 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. 2016, 47, 1883–1906. [Google Scholar] [CrossRef]
- Bannari, A.; El-Battay, A.; Hameid, N.; Tashtoush, F. Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data. Adv. Remote Sens. 2017, 6, 260–291. [Google Scholar] [CrossRef] [Green Version]
- Bannari, A.; El-Battay, A.; Bannari, R.; Rhinane, H. Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape. Remote Sens. 2018, 10, 855. [Google Scholar] [CrossRef] [Green Version]
- Bannari, A. Synergy Between Sentinel-MSI and Landsat-OLI to Support High Temporal Frequency for Soil Salinity Monitoring in an Arid Landscape. In Research Developments in Saline Agriculture, edited by Jagdish Chander Dagar, Rajender Kumar Yadav, and Parbodh Chander Sharma; Springer Nature Singapore Pte Ltd.: Gateway East, Singapore, 2019; Chapter 3; pp. 67–93. [Google Scholar]
- Al-Ali, Z.; Bannari, A.; Hameid, N.; El-Battay, A. Physical Models for Soil Salinity Mapping Over Arid Landscape Using Landsat-Oli and Field Data: Validation and Comparison. In Proceedings of the IGARSS 2019, Yokohama, Japan, 28 July–2 August 2019; pp. 7081–7084. [Google Scholar]
- Davis, E.; Wang, C.; Dow, K. Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: A case study of agricultural lands in coastal North Carolina. Int. J. Remote Sens. 2019, 40, 6134–6153. [Google Scholar] [CrossRef]
- Abuelgasim, A.; Ammad, R. Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data. Remote Sens. Appl. Soc. Environ. 2019, 13, 415–425. [Google Scholar] [CrossRef]
- Wang, J.; Ding, J.; Yu, D.; Ma, X.; Zhang, Z.; Ge, X.; Teng, D.; Li, X.; Liang, J.; Lizaga, I.; et al. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma 2019, 353, 172–187. [Google Scholar] [CrossRef]
- Corwin, D.L.; Scudiero, E. Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. In Advances in Agronomy; Elsevier: Amsterdam, The Netherlands, 2019; Volume 158, pp. 1–130. [Google Scholar]
- Bannari, A.; Musa, N.H.M.; Abuelgasim, A.; El-Battay, A.; Hameid, N. Sentinel-MSI and Landsat-OLI Data Quality Characterization for High Temporal Frequency Monitoring of Soil Salinity Dynamic in an Arid Landscape. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 2434–2450. [Google Scholar] [CrossRef]
- Alexakis, D.D.; Daliakopoulos, I.N.; Panagea, I.S.; Tsanis, I.K. Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece. Geocarto Int. 2016, 33, 321–338. [Google Scholar] [CrossRef]
- Kovács, F.; Gulácsi, A. Spectral Index-Based Monitoring (2000–2017) in Lowland Forests to Evaluate the Effects of Climate Change. Geosciences 2019, 9, 411. [Google Scholar] [CrossRef] [Green Version]
- Whitney, K.; Scudiero, E.; El-Askary, H.M.; Skaggs, T.H.; Allali, M.; Corwin, D.L. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecol. Indic. 2018, 93, 889–898. [Google Scholar] [CrossRef]
- Shamsi, S.R.F.; Zare, S.; Abtahi, S.A. Soil salinity characteristics using moderate resolution imaging spectroradiometer (MODIS) images and statistical analysis. Arch. Agron. Soil Sci. 2013, 59, 471–489. [Google Scholar] [CrossRef]
- Loveland, T.R.; Dwyer, J. Landsat: Building a strong future. Remote Sens. Environ. 2012, 122, 22–29. [Google Scholar] [CrossRef]
- Banskota, A.; Kayastha, N.; Falkowski, M.J.; Wulder, M.A.; Froese, R.E.; White, J.C. Forest Monitoring Using Landsat Time Series Data: A Review. Can. J. Remote Sens. 2014, 40, 362–384. [Google Scholar] [CrossRef]
- NASA. Landsat Benefiting Society for Fifty Years. 2018. Available online: https://landsat.gsfc.nasa.gov/wp-content/uploads/2019/02/Case_Studies_Book2018_Landsat_Final_12x9web.pdf (accessed on 18 March 2019).
- Buitre, M.J.; Zhang, H.; Lin, H. The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery. Remote Sens. 2019, 11, 688. [Google Scholar] [CrossRef] [Green Version]
- Xu, X.; Liu, H.; Lin, Z.; Jiao, F.; Gong, H. Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China. Remote Sens. 2019, 11, 1564. [Google Scholar] [CrossRef] [Green Version]
- Wulder, M.A.; Loveland, T.R.; Roy, D.P.; Crawford, C.J.; Masek, J.G.; Woodcock, C.E.; Allen, R.G.; Anderson, M.C.; Belward, A.S.; Cohen, W.B.; et al. Current status of Landsat program, science, and applications. Remote Sens. Environ. 2019, 225, 127–147. [Google Scholar] [CrossRef]
- Tran, T.V.; Tran, D.X.; Myint, S.W.; Huang, C.-Y.; Hoa, P.V.; Luu, T.H.; Vo, T.M. Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach. Sci. Total Environ. 2019, 687, 1087–1097. [Google Scholar] [CrossRef] [PubMed]
- Varallyay, G. Climate Change, Soil Salinity and Alkalinity. In Soil Responses to Climate Change; Springer Science and Business Media LLC: Berlin, Germany, 1994; Volume 23, pp. 39–54. [Google Scholar]
- De Forges, A.C.R.; Arrouays, D.; Bardy, M.; Bispo, A.; Lagacherie, P.; Laroche, B.; Lemercier, B.; Sauter, J.; Voltz, M. Mapping of Soils and Land-Related Environmental Attributes in France: Analysis of End-Users’ Needs. Sustainability 2019, 11, 2940. [Google Scholar] [CrossRef] [Green Version]
- El-Battay, A.; Bannari, A.; Hameid, N.A.; Abahussain, A.A. Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data. Adv. Remote Sens. 2017, 6, 23–39. [Google Scholar] [CrossRef] [Green Version]
- Teillet, P.; Santer, R. Terrain Elevation and Sensor Altitude Dependence in a Semi-Analytical Atmospheric Code. Can. J. Remote Sens. 1991, 17, 36–44. [Google Scholar]
- Moran, M.; Bryant, R.; Thome, K.; Ni, W.; Nouvellon, Y.; González-Dugo, M.; Qi, J.; Clarke, T. A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+. Remote Sens. Environ. 2001, 78, 71–82. [Google Scholar] [CrossRef]
- Al-Sarawi, M.; El-Baz, F.; Koch, M. Geomorphologic Controls on Surface Deposits of Kuwait as Depicted in Satellite Images. Kuwait J. Sci. Eng. 2006, 33, 123–154. [Google Scholar]
- Al-Sarawi, M.A. Surface geomorphology of Kuwait. GeoJournal 1995, 35, 493–503. [Google Scholar] [CrossRef]
- Al-Sarawi, M. Introduction of Geomorphologic Provinces in Kuwait’s Desert Using Multi-Source and Multi-Data Satellite Data. In Proceedings of the Eleventh Thematic Conference and Workshops on Applied Geological Remote Sensing, Las Vegas, NV, USA, 27–29 February 1996; pp. 536–545. [Google Scholar]
- Al-Hurban, A.E.; El-Gamily, H.I. Geo-Historical and Geomorphological Evolution of the Sabkhas and Ridges at the Al-Khiran Area, State of Kuwait. J. Geogr. Inf. Syst. 2013, 5, 208–221. [Google Scholar] [CrossRef] [Green Version]
- Milton, D. Geology of the Arabian Peninsula, Kuwait, Geological Survey Professional Paper; United State Government Printing Office: Washington, DC, USA, 1967.
- Omar, S.; Shahid, S. Reconnaissance Soil Survey for the State of Kuwait. In Developments in Soil Classification, Land Use Planning and Policy Implications: Innovative Thinking of Soil Inventory for Land Use Planning and Management of Land Resources; Shahid, S., Taha, F.K., Abdelfattah, M.A., Eds.; Springer Science and Business Media: Dordrecht, The Netherlands, 2013; Chapter 3; pp. 85–107. [Google Scholar]
- USDA. Soil Taxonomy: A basic System of Soil Classification for Making and Interpreting Soil Surveys; United States Department of Agriculture: Washington, DC, USA, 1999.
- Bannari, A.; Teillet, P.M.; Landry, R. Comparaison des réflectances des surfaces naturelles dans les bandes spectrales homologues des capteurs TM de Landsat-5 et TME+ de Landsat-7. Revue Télédétec. 2004, 4, 263–275. [Google Scholar]
- Devries, B.; Decuyper, M.; Verbesselt, J.; Zeileis, A.; Herold, M.; Joseph, S. Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sens. Environ. 2015, 169, 320–334. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.; Barsi, J.A. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef] [Green Version]
- Chander, G.; Markham, B.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893–903. [Google Scholar] [CrossRef]
- Roy, D.; Wulder, M.A.; Loveland, T.; Woodcock, C.E.; Allen, R.; Anderson, M.C.; Helder, D.; Irons, J.; Johnson, D.; Kennedy, R.; et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 2014, 145, 154–172. [Google Scholar] [CrossRef] [Green Version]
- Roy, D.P.; Zhang, H.; Ju, J.; Gomez-Dans, J.L.; Lewis, P.; Schaaf, C.; Sun, Q.; Li, J.; Huang, H.; Kovalskyy, V. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 2016, 176, 255–271. [Google Scholar] [CrossRef] [Green Version]
- Wulder, M.A.; Hilker, T.; White, J.C.; Coops, N.C.; Masek, J.G.; Pflugmacher, D.; Crevier, Y. Virtual constellations for global terrestrial monitoring. Remote Sens. Environ. 2015, 170, 62–76. [Google Scholar] [CrossRef] [Green Version]
- NASA. Landsat 5 Sets Guinness World Record for Longest Operating Earth Observation Satellite. 2013. Available online: https://www.nasa.gov/mission_pages/landsat/news/landsat5-guinness.html (accessed on 18 March 2019).
- Flood, N. Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape. Remote Sens. 2014, 6, 7952–7970. [Google Scholar] [CrossRef] [Green Version]
- NASA. Landsat-7 Science Data Users Handbook; NASA/GSFC: Greenbelt, MD, USA, 2011. Available online: http://landsathandbook.gsfc.nasa.gov (accessed on 10 September 2019).
- Mishra, N.; Helder, D.; Barsi, J.A.; Markham, B. Continuous calibration improvement in solar reflective bands: Landsat 5 through Landsat 8. Remote Sens. Environ. 2016, 185, 7–15. [Google Scholar] [CrossRef] [Green Version]
- NASA. Landsat-8 Instruments. 2014. Available online: http://www.nasa.gov/mission_pages/landsat/spacecraft/index.html (accessed on 10 September 2019).
- Czapla-Myers, J.S.; McCorkel, J.; Anderson, N.; Thome, K.; Biggar, S.; Helder, D.; Aaron, D.; Leigh, L.; Mishra, N. The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI. Remote Sens. 2015, 7, 600–626. [Google Scholar] [CrossRef] [Green Version]
- Mishra, N.; Helder, D.; Angal, A.; Choi, J.; Xiong, X. (Jack) Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site. Remote Sens. 2014, 6, 1327–1346. [Google Scholar] [CrossRef] [Green Version]
- Knight, E.J.; Kvaran, G. Landsat-8 Operational Land Imager Design, Characterization and Performance. Remote Sens. 2014, 6, 10286–10305. [Google Scholar] [CrossRef] [Green Version]
- Bannari, A.; Teillet, P.M.; Richardson, G. Nécessité de l’étalonnage radiométrique et standardisation des données de télédétection. Can. J. Remote Sens. 1999, 25, 45–59. [Google Scholar] [CrossRef]
- Bannari, A.; Omari, K.; Fedosejev, G.; Teillet, P.M. Using Getis Statistic for the Uniformity Characterization of Land Test Sites Used for Radiometric Calibration of Earth Observation Sensors. IEEE TGRS 2005, 43, 2918–2926. [Google Scholar]
- Roy, D.P.; Borak, J.S.; Devadiga, S.; Wolfe, R.E.; Zheng, M.; Descloitres, J. The MODIS Land product quality assessment approach. Remote Sens. Environ. 2002, 83, 62–76. [Google Scholar] [CrossRef]
- Price, J.C. Calibration of satellite radiometers and the comparison of vegetation indices. Remote Sens. Environ. 1987, 21, 15–27. [Google Scholar] [CrossRef]
- Teillet, P.; Staenz, K.; William, D. Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions. Remote Sens. Environ. 1997, 61, 139–149. [Google Scholar] [CrossRef]
- Myneni, R.B.; Asrar, G. Atmospheric effects and spectral vegetation indices. Remote Sens. Environ. 1994, 47, 390–402. [Google Scholar] [CrossRef]
- Schmidt, G.L.; Jenkerson, C.B.; Masek, J.; Vermote, E.; Gao, F. Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Algorithm Description; Open-File Report 2013-1057; U.S. Department of the Interior: Washington, DC, USA; U.S. Geological Survey: Reston, VA, USA, 2013.
- Pahlevan, N.; Lee, Z.; Wei, J.; Schaaf, C.; Schott, J.R.; Berk, A. On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing. Remote Sens. Environ. 2014, 154, 272–284. [Google Scholar] [CrossRef]
- Karpouzli, E.; Malthus, T.J. The empirical line method for the atmospheric correction of IKONOS imagery. Int. J. Remote Sens. 2003, 24, 1143–1150. [Google Scholar] [CrossRef]
- Bannari, A.; Al-Ali, Z.M. Ground Reflectance Factor Retrieval from Landsat (MSS, TM, ETM+, and OLI) Time Series Data based on Semi-empirical Line Approach and Pseudo-invariant Targets in Arid Landscape. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS-2020), Waikoloa, HI, USA, 19–24 July 2020. [Google Scholar]
- Themistocleous, K.; Hadjimitsis, D.G.; Retalis, A.; Chrysoulakis, N. The identification of pseudo-invariant targets using ground field spectroscopy measurements intended for the removal of atmospheric effects from satellite imagery: A case study of the Limassol area in Cyprus. Int. J. Remote Sens. 2012, 33, 7240–7256. [Google Scholar] [CrossRef]
- PCI-Geomatics. Using PCI Software; PCI-Geomatics: Richmond Hill, ON, Canada, 2018; 540p. [Google Scholar]
- Teillet, P. An algorithm for the radiometric and atmospheric correction of AVHRR data in the solar reflective channels. Remote Sens. Environ. 1992, 41, 185–195. [Google Scholar] [CrossRef]
- Smith, G.M.; Milton, E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653–2662. [Google Scholar] [CrossRef]
- Schroeder, T.A.; Cohen, W.B.; Song, C.; Canty, M.J.; Yang, Z. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens. Environ. 2006, 103, 16–26. [Google Scholar] [CrossRef]
- Hadjimitsis, D.G.; Clayton, C.R.I.; Retalis, A. The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 192–200. [Google Scholar] [CrossRef]
- Bannari, A.; Guedon, A.M.; El-Harti, A.; Cherkaoui, F.Z.; El-Ghmari, A. 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. 2008, 39, 2795–2811. [Google Scholar] [CrossRef]
- Fan, X.; Liu, Y.; Tao, J.; Weng, Y. Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression. Remote Sens. 2015, 7, 488–511. [Google Scholar] [CrossRef] [Green Version]
- Zhang, T.T.; Zeng, S.L.; Gao, Y.; Ouyang, Z.T.; Li, B.; Fang, C.M.; Zhao, B. Using hyperspectral vegetation indices as a roxy to monitor soil salinity. Ecol. Indic. 2011, 11, 1552–1562. [Google Scholar] [CrossRef]
- 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. Ecol. Indic. 2016, 70, 276–284. [Google Scholar] [CrossRef] [Green Version]
- DeHaan, R.; Taylor, G. Image-derived spectral endmembers as indicators of salinisation. Int. J. Remote Sens. 2003, 24, 775–794. [Google Scholar] [CrossRef]
- Lobell, D.B.; Lesch, S.M.; Corwin, D.; Ulmer, M.G.; Anderson, K.A.; Potts, D.J.; Doolittle, J.A.; Matos, M.R.; Baltes, M.J. Regional-scale Assessment of Soil Salinity in the Red River Valley Using Multi-year MODIS EVI and NDVI. J. Environ. Qual. 2010, 39, 35–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fan, X.; Pedroli, B.; Liu, G.; Liu, Q.; Liu, H.; Shu, L. Soil salinity development in the Yellow River Deltain relation to ground water dynamics. Land Degrad. Dev. 2012, 23, 175–189. [Google Scholar] [CrossRef]
- Sidike, A.; Zhao, S.; Wen, Y. Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 156–175. [Google Scholar] [CrossRef]
- Nawar, S.; Buddenbaum, H.; Hill, J. Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region. Remote Sens. 2015, 7, 1181–1205. [Google Scholar] [CrossRef] [Green Version]
- Farifteh, J.; Van Der Meer, F.; Van Der Meijde, M.; Atzberger, C. Spectral characteristics of salt-affected soils: A laboratory experiment. Geoderma 2008, 145, 196–206. [Google Scholar] [CrossRef]
- Ghosh, G.; Kumar, S.; Saha, S.K. Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis. J. Indian Soc. Remote Sens. 2011, 40, 129–136. [Google Scholar] [CrossRef]
- Wu, W.; Zucca, C.; Muhaimeed, A.S.; Al-Shafie, W.M.; Al-Quraishi, A.; Nangia, V.; Zhu, M.; Liu, G. Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq. Land Degrad. Dev. 2018, 29, 4005–4014. [Google Scholar] [CrossRef]
- Fernandez-Buces, N.; Siebe, C.; Cram, S.; Palacio, J. Mapping Soil Salinity Using a Combined Spectral Res- ponse Index for Bare Soil and Vegetation: A Case Study in the Former Lake Texcoco, Mexico. J. Arid Environ. 2006, 65, 644–667. [Google Scholar] [CrossRef]
- El Harti, A.; Lhissou, R.; Chokmani, K.; Ouzemou, J.-E.; Hassouna, M.; Bachaoui, E.M.; El Ghmari, A. Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. Int. J. Appl. Earth Obs. Geoinf. 2016, 50, 64–73. [Google Scholar] [CrossRef]
- Ben-Dor, E. Quantitative remote sensing of soil properties. Adv. Agron. 2002, 75, 173–243. [Google Scholar] [CrossRef]
- Ben-Dor, E.; Banin, A. Visible and near-infrared (0.4–1.1 μm) analysis of arid and semi-arid soils. Remote Sens. Environ. 1994, 48, 261–274. [Google Scholar] [CrossRef]
- Metternicht, G.; Zinck, J.A. Spatial discrimination of salt- and sodium-affected soil surfaces. Int. J. Remote Sens. 1997, 18, 2571–2586. [Google Scholar] [CrossRef]
- Metternicht, G.; Zinck, J. Remote sensing of soil salinity: Potentials and constraints. Remote Sens. Environ. 2003, 85, 1–20. [Google Scholar] [CrossRef]
- Verma, K.S.; Saxena, R.K.; Barthwal, A.K.; Deshmukh, S.N. Remote sensing technique for mapping salt affected soils. Int. J. Remote Sens. 1994, 15, 1901–1914. [Google Scholar] [CrossRef]
- Hashem, M.; El-Khattib, N.; El-Mowelhi, M.; Abd El-Salam, A. Desertification and land degradation using high resolution satellite data in the Nile Delta, Egypt. In Proceedings of the IGARSS-1997, Singapore, 3–8 August 1997; pp. 197–199. [Google Scholar]
- Bannari, A.; Huete, A.R.; Morin, D.; Zagolski, F. Effets de la couleur et de la brillance du sol sur les indices de végétation. Int. J. Remote Sens. 1996, 17, 1885–1906. [Google Scholar] [CrossRef]
- Goldshleger, N.; Benyamini, Y.; Agassi, M.; Ben-Dor, E.; Blumberg, D.G. Characterization of soil’s structural crust by spectral reflectance in the SWIR region (1.2-2.5 mum). Terra Nova 2001, 13, 12–17. [Google Scholar] [CrossRef]
- Shrestha, R.P. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degrad. Dev. 2006, 17, 677–689. [Google Scholar] [CrossRef]
- Leone, A.; Menenti, M.; Buondonno, A.; Letizia, A.; Maffei, C.; Sorrentino, G. A field experiment on spectrometry of crop response to soil salinity. Agric. Water Manag. 2007, 89, 39–48. [Google Scholar] [CrossRef]
- Odeh, I.O.A.; Onus, A. Spatial Analysis of Soil Salinity and Soil Structural Stability in a Semiarid Region of New South Wales, Australia. Environ. Manag. 2008, 42, 265–278. [Google Scholar] [CrossRef]
- Chapman, J.E.; Rothery, D.A.; Francis, P.W.; Pontual, A. Remote sensing of evaporite mineral zonation in salt flats (salars). Int. J. Remote Sens. 1989, 10, 245–255. [Google Scholar] [CrossRef]
- Drake, N.A. Reflectance spectra of evaporite minerals (400–2500 nm): Applications for remote sensing. Int. J. Remote Sens. 1995, 16, 55–71. [Google Scholar] [CrossRef]
- Hawari, F. Spectroscopy of evaporates. Per. Mineral 2002, 71, 191–200. [Google Scholar]
- Rahmati, M.; Hamzehpour, N. Quantitative remote sensing of soil electrical conductivity using ETM+ and ground measured data. Int. J. Remote Sens. 2016, 38, 123–140. [Google Scholar] [CrossRef]
- Wu, H.; Li, Z.-L. Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling. Sensors 2009, 9, 1768–1793. [Google Scholar] [CrossRef]
- Willmott, C.J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 1982, 63, 1309–1313. [Google Scholar] [CrossRef] [Green Version]
- Markham, B.; Barsi, J.; Kvaran, G.; Ong, L.; Kaita, E.; Biggar, S.; Czapla-Myers, J.S.; Mishra, N.; Helder, D. Landsat-8 Operational Land Imager Radiometric Calibration and Stability. Remote Sens. 2014, 6, 12275–12308. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Ganguly, S.; Dungan, J.L.; Wang, W.; Nemani, R.R. Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI. Adv. Remote Sens. 2017, 6, 147–159. [Google Scholar] [CrossRef] [Green Version]
- Gascon, F.; Bouzinac, C.; Thépaut, O.; Jung, M.; Francesconi, B.; Louis, J.; Lonjou, V.; Lafrance, B.; Massera, S.; Gaudel-Vacaresse, A.; et al. Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens. 2017, 9, 584. [Google Scholar] [CrossRef] [Green Version]
- Chander, G.; Markham, B.L. Revised Landsat-5 TM Radiometric Calibration Procedures and Post-calibration Dynamic Ranges. IEEE TGRS 2003, 41, 2674–2677. [Google Scholar]
- Thome, K. Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sens. Environ. 2001, 78, 27–38. [Google Scholar] [CrossRef]
- Thome, K.; Helder, D.; Aaron, D.; Dewald, J. Landsat-5 TM and Landsat-7 ETM+ absolute radiometric calibration using the reflectance-based method. IEEE Trans. Geosci. Remote Sens. 2004, 42, 2777–2785. [Google Scholar] [CrossRef]
- Shahid, S.A.; Rahman, K. Soil salinity development, classification, assessment and management in irrigated agriculture. In Handbook of Plant and Crop Stress, 3rd ed.; Pessarakli, M., Ed.; Taylor and Francis Group: Abingdon, UK, 2011; Chapter 2; pp. 23–39. [Google Scholar]
- Al-Gharabally, D.; Al-Barood, A. Kuwait Environmental Remediation Program (KERP): Remediation Demonstration Strategy. Biol. Chem. Res. 2015, 2015, 289–296. [Google Scholar]
- Leifer, I.; Lehr, W.J.; Simecek-Beatty, D.; Bradley, E.; Clark, R.; Dennison, P.E.; Hu, Y.; Matheson, S.; Jones, C.E.; Holt, B.; et al. State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill. Remote Sens. Environ. 2012, 124, 185–209. [Google Scholar] [CrossRef] [Green Version]
- Koch, M.; El-Baz, F. Identifying the Effects of the Gulf War on the Geomorphic Features of Kuwait by Remote Sensing and GIS. Photogr. Eng. Remote Sens. 1998, 64, 739–747. [Google Scholar]
- Mostagab, H.D.; Senosy, M.; Al Rashed, A.; Salem, M.E. The Impact of Hydrocarbon Pollution on Soil Degradation Using GIS Techniques and Soil Characterization in Burgan Oil field, South Kuwait. J. Environ. Prot. 2018, 9, 699–719. [Google Scholar] [CrossRef] [Green Version]
- Misak, R. Sources of Sand and Dust Storms in Kuwait. Am. J. Biomed. Sci. Res. 2019, 4, 1–3. [Google Scholar] [CrossRef]
- Abuduwaili, J.; Liu, D.; Wu, G. Saline dust storms and their ecological impacts in arid regions. J. Arid. Land 2010, 2, 144–150. [Google Scholar] [CrossRef] [Green Version]
- Zinck, J.A.; Metternicht, G. Soil Salinity and Salinization Hazard. In Remote Sensing of Soil Salinization: Impact on Land Management; Metternicht, G., Zinck, J.A., Eds.; CRC Press Taylor and Francis Group: Boca Raton, FL, USA, 2009; Chapter 1; pp. 3–20. [Google Scholar]
- Wang, X.; Hua, T.; Zhang, C.; Lang, L.; Wang, H. Aeolian salts in Gobi deserts of the western region of Inner Mongolia: Gone with the dust aerosols. Atmos. Res. 2012, 118, 1–9. [Google Scholar] [CrossRef]
- Zhu, B.; Yang, X. The Origion and Distribution of Soluble Salts in the Sand Seas of Nortern China. Geomorphology 2010, 123, 232–242. [Google Scholar] [CrossRef]
- Trenberth, K.E.; Dai, A. Effects of Mount Pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett. 2007, 34, L15702. [Google Scholar] [CrossRef] [Green Version]
- Dai, A.; Trenberth, K.E.; Qian, T. A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming. J. Hydrometeorol. 2004, 5, 1117–1130. [Google Scholar] [CrossRef]
- Stottlemyer, R.; Toczydlowski, D. Effect of Reduced Winter Precipitation and Increased Temperature on Watershed Solute Flux, 1988–2002, Northern Michigan. Biogeochem. 2006, 77, 409–440. [Google Scholar] [CrossRef]
- Hoegh-Guldberg, O.; Hoegh-Guldberg, O.; Jacob, D.; Taylor, M.; Bindi, M.; Brown, S.; Camilloni, I.; Diedhiou, A.; Djalante, R.; Ebi, K.; et al. Impacts of 1.5 °C of Global Warming on Natural and Human Systems. Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-Industrial Levels and related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. Masson-Delmotte, V., Zhai, P., Eds.; 2018. Available online: https://www.ipcc.ch/site/assets/uploads/sites/2/2019/02/SR15_Chapter3_Low_Res.pdf (accessed on 18 August 2019).
- NASA. World of Change: Global Temperatures. 2019. Available online: https://earthobservatory.nasa.gov/world-of-change/DecadalTemp (accessed on 10 March 2018).
- Greve, P.; Seneviratne, S.I. Assessment of future changes in water availability and aridity. Geophys. Res. Lett. 2015, 42, 5493–5499. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alsahli, M.M.M.; AlHasem, A.M. Vulnerability of Kuwait coast to sea level rise. Geogr. Tidsskr. J. Geogr. 2016, 116, 56–70. [Google Scholar] [CrossRef]
- Alothman, A.; Bos, M.S.; Fernandes, R.M.S.; Ayhan, M. Sea level rise in the north-western part of the Arabian Gulf. J. Geodyn. 2014, 81, 105–110. [Google Scholar] [CrossRef]
- Church, J.A.; White, N.J. Sea-Level Rise from the Late 19th to the Early 21st Century. Surv. Geophys. 2011, 32, 585–602. [Google Scholar] [CrossRef] [Green Version]
- Szabolcs, I. Impact of Climate Change on Soil Attributes: Influence on Salinization and Alkalization. In Soils on a Warmer Earth: Effects of Expected Climate Change on Soil Processes, with Emphasis on the Tropics and Sub-Tropics; Scharpenseel, H.W., Schomaker, M., Ayoub, A., Eds.; Elsevier: Amsterdam, The Netherlands, 1990; pp. 61–69. [Google Scholar]
- Pankova, E.I.; Konyushkova, M.V. Effect of global warming on soil salinity of the arid regions. Russ. Agric. Sci. 2013, 39, 464–467. [Google Scholar] [CrossRef]
- Sterling, S.M.; Ducharne, A.; Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Chang. 2012, 3, 385–390. [Google Scholar] [CrossRef]
- Vautard, R.; Gobiet, A.; Sobolowski, S.; Kjellström, E.; Stegehuis, A.; Watkiss, P.; Mendlik, T.; Landgren, O.; Nikulin, G.; Teichmann, C.; et al. The European climate under a 2 °C global warming. Environ. Res. Lett. 2014, 9, 034006. [Google Scholar] [CrossRef]
- Hinkel, J.; Lincke, D.; Vafeidis, A.T.; Perrette, M.; Nicholls, R.J.; Tol, R.S.J.; Marzeion, B.; Fettweis, X.; Ionescu, C.; Levermann, A. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl. Acad. Sci. USA 2014, 111, 3292–3297. [Google Scholar] [CrossRef] [Green Version]
- Le Dang, H.; Li, E.; Bruwer, J.; Nuberg, I. Farmers’ perceptions of climate variability and barriers to adaptation: Lessons learned from an exploratory study in Vietnam. Mitig. Adapt. Strat. Glob. Chang. 2013, 19, 531–548. [Google Scholar] [CrossRef]
- Smajgl, A.; Toan, T.Q.; Nhan, D.K.; Ward, J.; Trung, N.H.; Tri, L.Q.; Tri, V.P.D.; Vu, P.T. Responding to rising sea levels in the Mekong Delta. Nat. Clim. Chang. 2015, 5, 167–174. [Google Scholar] [CrossRef]
- Bhadwal, S.; Sharma, G.; Gorti, G.; Sen, S.M. Livelihoods, gender and climate change in the Eastern himalayas. Environ. Dev. 2019, 31, 68–77. [Google Scholar] [CrossRef]
- Hoegh-Guldberg, O.; Jacob, D.; Taylor, M.; Bolaños, T.G.; Bindi, M.; Brown, S.; Camilloni, I.A.; Diedhiou, A.; Djalante, R.; Ebi, K.L.; et al. The human imperative of stabilizing global climate change at 1.5 °C. Science 2019, 365, eaaw6974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Satellite | Sensor | Acquisition Date | Acquisition Time |
---|---|---|---|
Landsat-5 | TM | 14/07/1987 | 09:41:25 |
Landsat-5 | TM | 28/08/1992 | 09:38:38 |
Landsat-5 | TM | 29/08/1998 | 09:54:54 |
Landsat-7 | ETM+ | 11/09/2000 | 10:06:57 |
Landsat-7 | ETM+ | 17/09/2002 | 10:04:03 |
Landsat-7 | ETM+ | 20/03/2006 | 10:06:22 |
Landsat-7 | ETM+ | 03/12/2009 | 10:06:24 |
Landsat-8 | OLI | 05/07/2013 | 10:18:15 |
Landsat-8 | OLI | 14/08/2016 | 10:16:19 |
Landsat-8 | OLI | 13/05/2017 | 10:15:41 |
Class Number | Class Name | Thresholding Values | EC-Lab Limits (dS/m−1) |
---|---|---|---|
1 | Non-Saline | 0–10 | 0–4 |
2 | Low salinity | 11–25 | 4.5–10 |
3 | Moderate | 26–51 | 10.5–20 |
4 | High salinity | 52–102 | 20.5–40 |
5 | Very high salinity | 103–152 | 40.5–60 |
6 | Extreme salinity | >152 | ≥61 |
Salinity Classes | Average EC-Lab | pH | Ca2+ | K+ | Mg2+ | Na+ | Cl− | HCO3 | SAR |
---|---|---|---|---|---|---|---|---|---|
dS/m−1 | meq/L | (mmoles/L)0.5 | |||||||
Non-Saline | 2.6 | 7.6 | 39 | 1.8 | 7.8 | 32 | 9.6 | 6.6 | 3.4 |
Low | 6.7 | 7.7 | 67 | 2.3 | 12 | 23 | 38 | 9.1 | 3.5 |
Moderate | 11.8 | 7.7 | 45 | 7 | 14 | 49 | 70 | 10 | 9.1 |
High | 38.4 | 7.3 | 146 | 310 | 100 | 258 | 350 | 6 | 23.3 |
Very high | 48.8 | 7.4 | 78 | 15 | 19 | 325 | 590 | 4 | 46.8 |
Extreme | 300 | 7.0 | 230.5 | 97 | 1118 | 3615 | 3932 | 6.6 | 166.5 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bannari, A.; Al-Ali, Z.M. Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data. Remote Sens. 2020, 12, 2794. https://doi.org/10.3390/rs12172794
Bannari A, Al-Ali ZM. Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data. Remote Sensing. 2020; 12(17):2794. https://doi.org/10.3390/rs12172794
Chicago/Turabian StyleBannari, Abderrazak, and Zahra M. Al-Ali. 2020. "Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data" Remote Sensing 12, no. 17: 2794. https://doi.org/10.3390/rs12172794
APA StyleBannari, A., & Al-Ali, Z. M. (2020). Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data. Remote Sensing, 12(17), 2794. https://doi.org/10.3390/rs12172794