Remote Sensing and GIS-Based Approaches for Soil Salinization Assessment: A Comprehensive Review

Year : 2024 | Volume : 15 | Issue : 03 | Page : 29 38
    By

    Y.R. Krupavathi,

  • B.N. Anusha,

  • K. Raghu Babu,

  • P. Padma Sree,

  1. Research Scholar, Department of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, India.
  2. Research Scholar, Department of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, India
  3. Professor, Department of Geology, Yogi Vemana University, Kadapa, Andhra Pradesh, India
  4. Lecturer, Department of Geology, Government College of (Autonomous ), Anantpur,, Andhra Pradesh, India

Abstract

Soil salinization, a critical environmental challenge, significantly impacts land productivity, agricultural yields, and contributes to desertification, particularly in arid and semi-arid regions. Early detection and effective management of soil salinity are essential for sustainable agriculture and land management. Remote sensing (RS) and geographic information systems (GIS) have emerged as indispensable tools for mapping, monitoring, and analyzing soil salinity over vast areas. RS provides multi-temporal and multi-spectral data that helps identify salinity-affected regions, while GIS enables the integration and spatial analysis of this data to detect patterns and trends. This comprehensive review discusses the latest advancements in RS- and GIS-based techniques for soil salinization assessment, highlighting key spectral indices, mapping approaches, and spatial modeling techniques. Additionally, the paper explores the challenges in integrating RS and GIS, such as data limitations and the need for ground validation, while also suggesting future directions, including the use of machine learning and unmanned aerial vehicles for more precise salinity monitoring.

Keywords: Soil salinization, degradation, normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), remote sensing (RS), geographic information system (GIS)

[This article belongs to Journal of Remote Sensing & GIS ]

How to cite this article:
Y.R. Krupavathi, B.N. Anusha, K. Raghu Babu, P. Padma Sree. Remote Sensing and GIS-Based Approaches for Soil Salinization Assessment: A Comprehensive Review. Journal of Remote Sensing & GIS. 2024; 15(03):29-38.
How to cite this URL:
Y.R. Krupavathi, B.N. Anusha, K. Raghu Babu, P. Padma Sree. Remote Sensing and GIS-Based Approaches for Soil Salinization Assessment: A Comprehensive Review. Journal of Remote Sensing & GIS. 2024; 15(03):29-38. Available from: https://journals.stmjournals.com/jorsg/article=2024/view=176819


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References

  1. Food and Agricultural Organization (FAO). Global Soil Partnership: Soil Salinization and Soil Health. Rome, Italy: Food and Agriculture Organization of the United Nations; 2021. [Online]. Available at https://www.fao.org/global-soil-partnership/areas-of-work/soil-salinity/en/
  2. Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, Buchanan G, Dech S, Dwyer J, Herold M, Koh LP, Leimgruber P, Taubenboeck H, Wegmann M, Wikelski M, Woodcock Free and open-access satellite data for the assessment of land degradation and salinization. Biol Conservation. 2015; 182: 173–176. doi: 10.1016/j.biocon.2014.11.048.
  3. Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W. Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol. 2014; 51 (4): 839– doi: 10.1111/1365-2664.12261.
  4. Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, Anderson MC, Helder D, Irons JR, Johnson DM, Kennedy R, Scambos TA, Schaaf CB, Schott JR, Sheng Y, Vermote EF, Belward AS, Bindschadler R, Cohen WB, Gao F, Hipple JD, Zhu Z. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing Environ. 2014; 185: 149– doi: 10.1016/j.rse.2016.05.013.
  5. Salisu N, Ejeikwu EO, Ejaro S NDVI-based assessment and monitoring of vegetation degradation trends in Kebbi State, Northwestern Nigeria, a semiarid region. Bull Nat Appl Sci. 2020; 1 (1): 32–42. doi: 10.5281/zenodo.10911925.
  6. Salem OH, Jia Z. Evaluation of different soil salinity indices using remote sensing techniques in Siwa Oasis, Egypt. Agronomy. 2024; 14 (4):723. doi: 3390/agronomy14040723.
  7. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Environ. 2002; 83 (1–2): 195–
  8. Becker F, Li Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sensing Rev. 1995; 12 (3–4): 225–253. doi: 10.1080/02757259509532286.
  9. Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, et al. Monitoring global croplands with coarse resolution earth observations: the Global Agriculture Monitoring (GLAM) project. Remote Sensing. 2010; 2 (6): 1589– doi: 10.3390/rs2061589.
  10. Goodchild MF, Li L. Assuring the quality of volunteered geographic information. Spatial Stat. 2012; 1: 110–
  11. Cressie Statistics for Spatial Data. Wiley Series in Probability and Statistics. New York, NY, USA: Wiley; 1993.
  12. Duan Z, Wang X, Sun L. Monitoring and mapping of soil salinity on the exposed seabed of the Aral Sea, Central Asia. Water. 2022; 14 (9): doi: 10.3390/w14091438.
  13. Pricope NG, Minei A, Halls JN, Chen C, Wang Y. UAS hyperspatial LiDAR data performance in delineation and classification across a gradient of wetland types. Drones. 2022; 6 (10): doi: 10.3390/drones6100268.
  14. Huang Y, Zhang Challenges in remote sensing-based salinity assessment: a review. Remote Sensing. 2017; 9 (4): 332. doi: 10.3390/rs9040332.
  15. Metternicht GI, Zinck JA. Remote sensing of soil salinity: potentials and constraints. Remote Sensing Environ. 2003; 85 (1): 1–
  16. Goetz AFH. Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sensing Environ. 2009; 113 (Suppl 1): S5–S16.
  17. Anderson K, Gaston K Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 2013; 11 (3): 138–146. doi: 10.1890/120150.
  18. Chen Y-N, Fan K-C, Chang Y-L, Moriyama T. Special Issue Review: Artificial intelligence and machine learning applications in remote sensing. Remote Sensing. 2023; 15 (3): doi: 10.3390/rs15030569.
  19. Google Earth Engine. Google Earth Engine: Cloud-Based Geospatial Analysis. [Online]. Available at https://www.google.com/earth/education/tools/google-earth-engine/#:~:text=Google
    %20Earth%20Engine%20is%20a,natural%20resource%20management%2C%20and%20more
  20. Badapalli PK, Nakkala AB, Gugulothu S, et al. Dynamic land degradation assessment: integrating machine learning with Landsat 8 OLI/TIRS for enhanced spectral, terrain, and land cover indices. Earth Syst Environ. 2024. Epub ahead of print. doi: 1007/s41748-024-00442-9.
  21. Badapalli PK, Nakkala AB, Gugulothu S, et al. Geospatial insights into urban growth and land cover transformation in Anantapur city, India. Environ Dev Sustain. 2024. Epub ahead of print. doi: 1007/s10668-024-05180-6.
  22. Badapalli PK, Kottala RB, Pujari P Aeolian Desertification: Disaster with Visual Impact in Semi-arid Regions of Andhra Pradesh, South India. Singapore: Springer; 2023.
  23. Kumar BP, Anusha BN, Babu KR, Sree P Identification of climate change impact and thermal comfort zones in semi-arid regions of AP, India using LST and NDBI techniques. J Cleaner Prod. 2023; 407: 137175.
  24. Pasham H, Gugulothu S, Badapalli PK, et al. Geospatial approaches of TGSI and morphometric analysis in the Mahi River basin using Landsat 8 OLI/TIRS and SRTM-DEM. Environ Sci Pollut Res. 2024; 31: 54129–54146. doi: 1007/s11356-022-24863-z.
  25. Kumar BP, Babu KR, Anusha BN, Rajasekhar Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI/TIRS, LST, and NDVI approach. Environ Challenges. 2022; 8: 100578.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 15/09/2024
Accepted 19/09/2024
Published 04/10/2024
615

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