Using Remote Sensing in Soil Salinization Mapping: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Ghada Sahbeni
,
Maurice Ngabire
,
Peter K. Musyimi
,
Balázs Székely

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing.

  • monitoring
  • salinization mapping
  • soil
  • remote sensing

1. Introduction

Soil salinization is a major environmental hazard affecting agricultural productivity and food security worldwide [1]. It adversely influences soil structure, nutrient availability, and plant growth, leading to reduced crop yields and, in extreme scenarios, desertification [2][3]. The increasing levels are caused by diverse natural and anthropogenic factors, such as inadequate irrigation practices, fertilizer overuse, and land use changes [4]. In addition, climate change impacts on soil salinization are a significant concern, with weather patterns playing a fundamental role in increasing salt content around the rhizosphere [5][6]. This is particularly noticeable in areas with shallow water tables and degraded groundwater quality [7]. Therefore, real-time monitoring of soil salinity levels is essential for effective soil management and sustainable agriculture [8][9].
Subsequently, remote sensing has proven to be an attractive alternative for mapping and monitoring salinization in large-scale and heterogeneous landscapes, especially under different land use, land cover types, and areas where socio-culturally different farming cultivation techniques are maintained [10][11]. Remote sensing data from satellite imagery and aerial photography offer valuable information on various environmental parameters, including vegetation cover, soil composition, and moisture content, which are interconnected to salt content [12]. By analyzing and interpreting such data, researchers, and practitioners can generate detailed maps and spatial models of salinity distribution, which inform land management decisions and support the development of effective strategies for risk mitigation [13][14]. Over the past few decades, remote sensing has undergone significant advancements, enabling the collection of high-resolution data on various scales [15][16]. As research progresses, various tools have emerged [17][18][19], including multispectral imaging sensors which capture information at different wavelengths, leading to more accurate results with higher spatial resolution [20]. This enables the extraction of auxiliary data on soil properties such as moisture content, organic matter, and salt content by analyzing the reflected radiation from the surface [21]. To map soil salinization, many researchers have used the concept of spectral index, a combination of pixel values from two or more spectral bands [22][23][24]. As they rely on the variance in reflectance between the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) bands, they can be useful in detecting changes in salt content [25].
In addition to multispectral imaging, synthetic aperture radar (SAR) has recently become one of the most efficient remote sensing tools for soil salinity detection due to its insensitivity to weather conditions, unlike optical remote sensing [26]. SAR uses microwave signals to penetrate the soil and retrieve information on soil moisture [27] and structure [28]. Therefore, it can generate relevant information on soil’s electrical conductivity (EC), which is closely related to its salt content [29][30]. The backscatter coefficient (sigma-0), as a measure of the microwave energy reflected back to the sensor, is a commonly used parameter for mapping soil salinity by integrating it into empirical models [31].
The accuracy of salinity mapping has been significantly improved by the fusion of multiple data sources, such as optical and SAR data [32]. As optical sensors can capture surface reflectance and vegetation cover, SAR sensors penetrate the vegetation and retrieve information on soil properties [33]. Moreover, integrating remote sensing data with other data types, such as those on land cover, land use, and topographic features, provides even more accurate estimations [4][34]. By combining data from different sensors and platforms, researchers can take advantage of the complementary strengths of each data source and overcome their limitations.
As the field of salinization mapping continues to progress, it has become increasingly evident that integrating remote sensing data with machine learning offers a more robust framework for effectively processing large datasets and generating more accurate products [35]. Machine learning algorithms, such as random forest [36] and support vector machines [37], have been remarkably efficient at data processing and analysis, enabling prediction models to learn from the spectral and spatial patterns and produce estimations based on input features [38][39].

2. Remote Sensing for Mapping Soil Salinization

Not only do environmental factors, such as soil type, land use, topography, and climate, play a leading role in salinization expansion, but anthropogenic actions, such as the inadequacy of drainage systems and ineffective irrigation activities over an extended period, also have a direct impact on this dynamic process [4][40][41]. In light of this, the availability of spaceborne and airborne platforms has significantly facilitated the monitoring of environmental hazards by providing vast amounts of data that can be applied to diverse fields, from sustainable agriculture, land surveys, and climate change to risk mitigation [42]. Enhanced data in terms of spatiotemporal and spectral resolutions offered by these systems have enabled researchers to monitor changes inland and identify salinity patterns at various spatial scales. In addition, when combined with geospatial data, ground-based systems such as the electromagnetic induction instrument (EMI) give valuable insights into the salinization status at both local and canopy scales, allowing policy-makers to gain a more comprehensive understanding of the complex dynamics of salinization at the field level [43].
Integrating remote sensing data with robust analytical techniques has shown great promise in salinization mapping, as suggested by many researchers. A study conducted in Qom Valley in Iraq demonstrated that combining Landsat 8 OLI’s spectral indices and topographic features can accurately predict and map soil salinity [44]. Further, jointly using Sentinel-2 Multispectral Imager (MSI) data with laboratory measurements to build a machine learning model for soil salinity estimation in the northern margin of the Tarim Basin (China) provided a timeless scientific reference for futuristic scenarios related to salinization expansion in arid areas [45]. Field observations, Landsat 5 TM, and radar data retrieved from ALOS (Advanced Land Observing Satellite) and PALSAR (Phased Array L-Band Synthetic Aperture Radar) have provided a promising solution for salinity monitoring in central Iraq with lower costs, as suggested by the authors of [46]. In the Great Hungarian Plain, the authors of [47] employed spectral indices and principal components derived from Landsat 8 OLI data coupled with multiple linear regression analysis to map salt content distribution in the area. The study proved the potential of multispectral data, with the outperformance of ridge regression yielding an overall accuracy of 75%. Thus, linear regression modeling using remote sensing-based variables can be significantly effective for locally assessing soil salinity.
As pattern changes in land use and land cover supposedly vary with salinization magnitude, relevant data can be effectively employed to predict soil salinity levels [48][49]. In Europe, among several land cover inventories, the CORINE system has solely provided this information for over two decades, which fortunately could be used to map salinization by many researchers [4][50][51]. In addition, a study conducted in Dakhla Oasis, located in the western desert of Egypt, showed a discrepancy in soil salinity estimations based on the linear spectral unmixing (LSU) related to land surface temperature over different land cover types and altitudes [52]. These findings are consistent with another study conducted in Korat province (Thailand), emphasizing the importance of vegetation cover, soil characteristics, and seasonal fluctuations in mapping soil salinization via remote sensing [53].
Over the past decade, research focus has shifted from traditional, labor-intensive methods of measuring salt content through field surveys and laboratory analysis towards a greater reliance on remotely sensed data often used with limited reference datasets for calibration purposes [54]. Based on a qualitative analysis of the Scopus database, researchers have run an advanced search query to find available peer-reviewed research papers with the following terms: soil salinization, monitoring, and remote sensing. Significant progress was made in spaceborne and airborne remote sensing systems between 2014 and 2023, which was fundamentally driven by the launch of Landsat 8 in 2013 and the subsequent launches of Sentinel 1 and Sentinel 2 in 2014 and 2015. Figure 1 demonstrates a positive trend in research studies that employed remote sensing for salinization assessment in the same timeframe. However, the sudden drop in 2021 is attributed to a shift in research focus toward other areas or technologies, funding limitations, and reference data unavailability due to the geographic inaccessibility caused by extreme global events such as COVID-19. Given the rise in technology availability and professional knowledge worldwide, this increasing trend is expected to continue in 2023.
Remotesensing 15 02540 g001 550
Figure 1. Progress of remote sensing application in salinization mapping based on available studies in Scopus between 2014 and 2023. The yearly number of studies using various sensors has significantly increased from 14 in 2014 to 81 in 2022. The availability of satellite data and the development of sophisticated instruments with higher spectral and spatial resolutions have contributed to this growth.
Extensive data have been used to assess salinization by measuring the changes in reflectance along the spectrum from visible to infrared (IR) for different salinity levels accompanied by vegetated and sparsely vegetated profiles to distinguish the disparities. Usually, the interaction between soil and spectral energy differs based on the emitted radiation and the surface properties. In contrast, salt-affected soils often exhibit whitish or grayish crust on the topsoil [55][56]. In this regard, the authors of [57] found an increase in reflectance in the visible range, particularly in the blue (450 nm) range with excessively saline soils. Moreover, higher reflectance occurs in the SWIR region (1100–3000 nm), revealing more sensitivity to salt content, as discovered by the authors of [58] and [59]. According to the authors of [60], alterations in surface roughness caused by salinity induce shifts in spectral reflectance. Many studies have used SWIR and thermal infrared (TIR) spectroscopy to quantify salt content [61][62]. This means various properties influence salt-affected land identification, including soil color, texture, and moisture content.
Consequently, hyperspectral remote sensing has facilitated salt characterization by establishing predictive models to estimate salt content more accurately [63] or through creating spectral libraries for various salt types based on their narrow continuous bands [64], which cover the entire spectrum from visible, near-infrared to SWIR and TIR [64]. The situation is quite different in vegetated areas since reduced crop growth induced by saline stress causes chlorophyll and other pigment alterations and increases heat emissions. This presents a potential avenue to use thermal remote sensing for qualifying vulnerable areas based on vegetation status, except for halophytes [65].

This entry is adapted from the peer-reviewed paper 10.3390/rs15102540

References

  1. FAO. Soil Salinization as a Global Major Challenge|ITPS Soil Letter #3. Available online: https://www.fao.org/global-soil-partnership/resources/highlights/detail/en/c/1412475/ (accessed on 7 March 2023).
  2. Shahid, S.A.; Zaman, M.; Heng, L. Introduction to Soil Salinity, Sodicity and Diagnostics Techniques. In Guideline for Salinity Assessment, Mitigation, and Adaptation Using Nuclear and Related Techniques; Springer: Cham, Switzerland, 2018; pp. 1–25.
  3. Daba, A.W.; Qureshi, A.S. Review of Soil Salinity and Sodicity Challenges to Crop Production in the Lowland Irrigated Areas of Ethiopia and Its Management Strategies. Land 2021, 10, 1377.
  4. Kaya, F.; Schillaci, C.; Keshavarzi, A.; Başayiğit, L. Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain. Land 2022, 11, 2148.
  5. Shrivastava, P.; Kumar, R. Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi J. Biol. Sci. 2015, 22, 123–131.
  6. Okur, B.; Orcen, N. Soil Salinization and Climate Change. In Climate Change and Soil Interactions; Prasad, M.N.V., Pietrzykowski, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2020; pp. 331–350.
  7. Corwin, D.L. Climate Change Impacts on Soil Salinity in Agricultural Areas. Eur. J. Soil Sci. 2020, 72, 842–862.
  8. Hussain, S.; Shaukat, M.; Ashraf, M.; Zhu, C.; Jin, Q.; Zhang, J. Salinity Stress in Arid and Semi-Arid Climates: Effects and Management in Field Crops. In Climate Change and Agriculture; IntechOpen: London, UK, 2019.
  9. Hassani, A.; Azapagic, A.; Shokri, N. Global Predictions of Primary Soil Salinization under Changing Climate in the 21st Century. Nat. Commun. 2021, 12, 6663.
  10. Wu, W. A Brief Review on Soil Salinity Mapping by Optical and Radar Remote Sensing. In Research Developments in Saline Agriculture; Dagar, J.C., Yadav, R.S., Sharma, P.C., Eds.; Springer: Singapore, 2019; pp. 23–40.
  11. Corwin, D.L.; Scudiero, E. Review of Soil Salinity Assessment for Agriculture across Multiple Scales Using Proximal and/or Remote Sensors. Adv. Agron. 2019, 158, 1–130.
  12. Metternicht, G.I.; Zinck, J.A. Remote sensing of soil salinity: Potentials and constraints. Remote Sens. Environ. 2003, 85, 1–20.
  13. Xu, S.; Xu, Y.; Fu, Y.; Wang, Q. Soil Salinization and Mitigation Measures in Land Reclamation Regions. In Soil Contamination—Current Consequences and Further Solutions; Larramendy, M.L., Soloneski, S., Eds.; IntechOpen: London, UK, 2016.
  14. Singh, A. Soil salinity: A global threat to sustainable development. Soil Use Manag. 2022, 38, 39–67.
  15. Taghadosi, M.M.; Hasanlou, M.; Eftekhari, K. Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. Int. J. Remote Sens. 2018, 40, 237–252.
  16. Kasim, N.; Maihemuti, B.; Sawut, R.; Abliz, A.; Dong, C.; Abdumutallip, M. Quantitative Estimation of Soil Salinization in an Arid Region of the Keriya Oasis Based on Multidimensional Modeling. Water 2020, 12, 880.
  17. Azabdaftari, A.; Sunar, F. Soil Salinity Mapping Using Multitemporal Landsat Data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016, XLI-B7, 3–9.
  18. Abuelgasim, A.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.
  19. Günal, E.; Wang, X.; Kılıc, O.M.; Budak, M.; Al Obaid, S.; Ansari, M.J.; Brestic, M. Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan. PLoS ONE 2021, 16, e0259695.
  20. Avdan, U.; Kaplan, G.; Küçük Matcı, D.; Yigit Avdan, Z.; Erdem, F.; Tuğba Mizik, E.; lknur Demirtas, I. Soil salinity prediction models constructed by different remote sensors. Phys. Chem. Earth 2022, 128, 103230.
  21. Sahbeni, G. A PLSR model to predict soil salinity using Sentinel-2 MSI data. Open Geosci. 2021, 13, 977–987.
  22. Aceves, E.Á.; Guevara, H.J.P.; Enríquez, A.C.; Sánchez, M.F.; Mora, E.M. Determining Salinity and Ion Soil Using Satellite Image Processing. Pol. J. Environ. Stud. 2019, 28, 1549–1560.
  23. Merembayev, T.; Amirgaliyev, Y.; Saurov, S.; Wójcik, W. Soil Salinity Classification Using Machine Learning Algorithms and Radar Data in the Case from the South of Kazakhstan. J. Ecol. Eng. 2022, 23, 61–67.
  24. Smanov, Z.M.; Laiskhanov, S.U.; Poshanov, M.N.; Abikbayev, Y.R.; Duisekov, S.N.; Tulegenov, Y.A. Mapping of Cornfield Soil Salinity in Arid and Semi-Arid Regions. J. Ecol. Eng. 2023, 24, 146–158.
  25. Khan, N.M.; Rastoskuev, V.V.; Sato, Y.; Shiozawa, S. Assessment of Hydro Saline Land Degradation by Using a Simple Approach of Remote Sensing Indicators. Agric. Water Manag. 2005, 77, 96–109.
  26. Ouchi, K. Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics. Remote Sens. 2013, 5, 716–807.
  27. Barrett, B.; Dwyer, E.; Whelan, P. Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques. Remote Sens. 2009, 1, 210–242.
  28. Beale, J.; Snapir, B.; Waine, T.; Evans, J.; Corstanje, R. The Significance of Soil Properties to the Estimation of Soil Moisture from C-Band Synthetic Aperture Radar. Hydrol. Earth Syst. Sci. Discuss. 2019, 1–28.
  29. Muhetaer, N.; Nurmemet, I.; Abulaiti, A.; Xiao, S.; Zhao, J. A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data. Remote Sens. 2022, 14, 363.
  30. Wu, K.; Lambot, S. Analysis of Low-Frequency Drone-Borne GPR for Root-Zone Soil Electrical Conductivity Characterization. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–13.
  31. Hoa, P.; Giang, N.; Binh, N.; Hai, L.; Pham, T.-D.; Hasanlou, M.; Bui, D.T. Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sens. 2019, 11, 128.
  32. Dehni, A.; Lounis, M. Remote Sensing Techniques for Salt-Affected Soil Mapping: Application to the Oran Region of Algeria. Procedia Eng. 2012, 33, 188–198.
  33. Sahbeni, G.; Székely, B. Salinity Levels Discrimination Using ERS-1/2 and Sentinel-1 SAR Time Series Data in Hortobágyi National Park, Hungary. In Proceedings of the 2022 IEEE Mediterranean and Middle East Geoscience and Remote Sensing Symposium (M2GARSS), Virtual, 7–9 March 2022; pp. 194–197.
  34. Shahrayini, E.; Noroozi, A.A. Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: A Case Study in Iran. Environ. Model. Assess 2022, 27, 901–913.
  35. Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput. Sci. 2021, 2, 160.
  36. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32.
  37. Vapnik, V.; Golowich, S.; Smola, A. Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9; Mozer, M.C., Jordan, M.I., Petsche, T., Eds.; MIT Press: Cambridge, MA, USA, 1997; pp. 281–287.
  38. Vermeulen, D.; Van Niekerk, A. Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates. Geoderma 2017, 299, 1–12.
  39. Wang, F.; Yang, S.; Yang, W.; Yang, X.; Ding, J. Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China. Eur. J. Remote Sens. 2019, 52, 256–276.
  40. Hillel, D.; Braimoh, A.K.; Vlek, P.L.G. Soil Degradation Under Irrigation. In Land Use and Soil Resources; Braimoh, A.K., Vlek, P.L.G., Eds.; Springer: Dordrecht, The Netherlands, 2008; pp. 97–120.
  41. Yuvaraj, M.; Subash Chandra Bose, K.; Elavarasi, P.; Tawfik, E. Soil Salinity and Its Management. In Soil Moisture Importance; Meena, R.S., Datta, R., Eds.; IntechOpen: London, UK, 2021; Volume 1, Chapter 5.
  42. Zhao, Q.; Yu, L.; Du, Z.; Peng, D.; Hao, P.; Zhang, Y.; Gong, P. An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sens. 2022, 14, 1863.
  43. Scudiero, E.; Corwin, D.L.; Anderson, R.G.; Skaggs, T.H. Moving Forward on Remote Sensing of Soil Salinity at Regional Scale. Front. Environ. Sci. 2016, 4, 65.
  44. Habibi, V.; Ahmadi, H.; Jafari, M.; Moeini, A. Mapping Soil Salinity Using a Combined Spectral and Topographical Index with Artificial Neural Network. PLoS ONE 2021, 16, e0228494.
  45. Wang, J.; Peng, J.; Li, H.; Yin, C.; Liu, W.; Wang, T.; Zhang, H. Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sens. 2021, 13, 305.
  46. Wu, W.; Muhaimeed, A.S.; Al-Shafie, W.M.; Al-Quraishi, A.M.F. Using Radar and Optical Data for Soil Salinity Modeling and Mapping in Central Iraq. In Environmental Remote Sensing and GIS in Iraq; Al-Quraishi, A., Negm, A., Eds.; Springer Water: Cham, Switzerland, 2020; pp. 19–32.
  47. Sahbeni, G. Soil Salinity Mapping Using Landsat 8 OLI Data and Regression Modeling in the Great Hungarian Plain. SN Appl. Sci. 2021, 3, 587.
  48. Saad, K.; Kallel, A.; Rebah, Z.B.; Solaiman, B. Spatio-temporal monitoring of soil salinity and land cover changes using remote sensing techniques: Zaghouan case study (Tunisia). In Proceedings of the 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sfax, Tunisia, 24–27 May 2022; pp. 1–5.
  49. Biro Turk, K.; Aljughaiman, A. Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment. Open Geosci. 2020, 12, 220–231.
  50. Lekka, C.; Petropoulos, G.P.; Triantakonstantis, D.; Detsikas, S.E.; Chalkias, C. Exploring the spatial patterns of soil salinity and organic carbon in agricultural areas of Lesvos Island, Greece, using Geoinformation Technologies. Environ. Monit. Assess. 2023, 195, 3.
  51. Bakacsi, Z.; Tóth, T.; Makó, A.; Barna, G.; Laborczi, A.; Szabó, J.; Szatmári, G.; Pásztor, L. National level assessment of soil salinization and structural degradation risks under irrigation. Hung. Geogr. Bull. 2019, 68, 141–156.
  52. Masoud, A.A.; Koike, K.; Atwia, M.G.; El-Horiny, M.M.; Gemail, K.S. Mapping Soil Salinity Using Spectral Mixture Analysis of Landsat 8 OLI Images to Identify Factors Influencing Salinization in an Arid Region. Int. J. Appl. Earth Obs. Geoinform. 2019, 83, 101944.
  53. Shrestha, R.P.; Qasim, S.; Bachri, S. Investigating Remote Sensing Properties for Soil Salinity Mapping: A Case Study in Korat Province of Thailand. Environ. Chall. 2021, 5, 100290.
  54. El-SayedGad, M.M.; Mohamed, M.H.A.; Mohamed, M.R. Soil salinity mapping using remote sensing and GIS. Geomatica 2022, 75, 295–309.
  55. Howari, F.M.; Goodell, P.C.; Miyamoto, S. Spectral Properties of Salt Crusts Formed on Saline Soils. J. Environ. Qual. 2002, 31, 1453–1461.
  56. Li, S.; Li, C.; Fu, X. Characteristics of Soil Salt Crust Formed by Mixing Calcium Chloride with Sodium Sulfate and the Possibility of Inhibiting Wind-Sand Flow. Sci. Rep. 2021, 11, 9746.
  57. Metternicht, G.I.; Zinck, J.A. Spatial Discrimination of Salt- and Sodium-Affected Soil Surfaces. Int. J. Remote Sens. 1997, 18, 2571–2586.
  58. 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.
  59. Sahbeni, G. Comparative Study of Machine-Learning-Based Classifiers for Soil Salinization Prediction using Sentinel-1 SAR and Sentinel-2 MSI Data. In Proceedings of the 2022 10th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Valencia, Spain, 7–9 July 2022; pp. 1–4.
  60. Goldshleger, N.; Chudnovsky, A.; Ben-Binyamin, R. Predicting Salinity in Tomato Using Soil Reflectance Spectral. Int. J. Remote Sens. 2013, 34, 6079–6093.
  61. Wu, S.; Wang, C.; Liu, Y.; Li, Y.; Liu, J.; Xu, A.; Pan, K.; Li, Y.; Pan, X. Mapping the Salt Content in Soil Profiles Using Vis-NIR Hyperspectral Imaging. Soil Sci. Soc. Am. J. 2018, 82, 1259–1269.
  62. Rajakumari, S.; Mahesh, R.; Sarunjith, K.J.; Ramesh, R. Building spectral catalogue for salt marsh vegetation, hyperspectral and multispectral remote sensing. Reg. Stud. Mar. Sci. 2022, 53, 102435.
  63. Nguyen, K.A.; Liou, Y.A.; Tran, H.P.; Hoang, P.P.; Nguyen, T.H. Soil Salinity Assessment by Using Near-Infrared Channel and Vegetation Soil Salinity Index Derived from Landsat 8 OLI Data: A Case Study in the Tra Vinh Province, Mekong Delta, Vietnam. Prog. Earth Planet. Sci. 2020, 7, 2–16.
  64. Ramos, T.B.; Castanheira, N.; Oliveira, A.R.; Paz, A.M.; Darouich, H.; Simionesei, L.; Farzamian, M.; Gonçalves, M.C. Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. Application to Lezíria Grande, Portugal. Agric. Water Manag. 2020, 241, 106387.
  65. Metternicht, G.I.; Zinck, J.A. Remote Sensing of Soil Salinization: Impact on Land Management; CRC Press: Boca Raton, FL, USA, 2008.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback