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

Year : 2024 | Volume :15 | Issue : 03 | Page : –
By

P. Padma Sree,

Y. R. Krupavathi,

B. N. Anusha,

K. Raghu Babu,

  1. Lecturer,, Yogi Vemana University, Kadapa,, Andhra Pradesh,, India.
  2. Research Scholar,, Yogi Vemana University, Kadapa,, Andhra Pradesh,, India
  3. Research Scholar,, Yogi Vemana University, Kadapa,, Andhra Pradesh,, India
  4. Professor,, Yogi Vemana University, Kadapa,, 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 analysing 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 ML and UAVs for more precise salinity monitoring.

Keywords: Soil salinization, Degradation, NDVI, SAVI, RS, GIS.

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

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

References

  • (2021). Global Soil Partnership: Soil Salinization and Soil Health. Food and Agriculture Organization of the United Nations. [Online]. Available at: FAO Website. https://www.fao.org/global-soil-partnership/areas-of-work/soil-salinity/en/
  • Turner, W., C. Rondinini, N. Pettorelli, B. Mora, A.K. Leidner, Z. Szantoi, G. Buchanan, S. Dech, J. Dwyer, M. Herold, L.P. Koh, P. Leimgruber, H. Taubenboeck, M. Wegmann, M. Wikelski, C. Woodcock (2015). Free and open-access satellite data for the assessment of land degradation and salinization. Erschienen in: Biological Conservation; 182 (2015). – S. 173-176. https://dx.doi.org/10.1016/j.biocon.2014.11.048
  • Pettorelli, N., William F. Laurance, Timothy G. O’Brien, Martin Wegmann, Harini Nagendra, Woody Turner. Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology. 2014; 51(4): 839-848. doi:10.1111/1365-2664.12261
  • P. Roy , M. A. Wulder, T. R. Loveland, C. E. Woodcock, R. G. Allen, M. C. Anderson, D. Helder, J. R. Irons, D. M. Johnson, R. Kennedy, T. A. Scambos, C. B. Schaaf, J. R. Schott, Y. Sheng, E. F. Vermote, A. S. Belward, R. Bindschadler, W. B. Cohen, F. Gao, J. D. Hipple, Z. Zhu. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 2014; 185: 149-172. doi: 10.1016/j.rse.2016.05.013
  • Salisu, N., Ejeikwu, E. O., Ejaro, S. P. (2020). NDVI-Based Assessment and Monitoring of Vegetation Degradation Trends in Kebbi State, Northwestern Nigeria, a Semiarid Region. Bulletins of Natural and Applied Sciences, 1(1), 32–42. https://doi.org/10.5281/zenodo.10911925
  • Salem OH, Jia Z. Evaluation of Different Soil Salinity Indices Using Remote Sensing Techniques in Siwa Oasis, Egypt. Agronomy. 2024; 14(4):723. https://doi.org/10.3390/agronomy14040723
  • Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao, L. G. Ferreira. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. November 2002; 83 (1–2): 195-213.
  • Becker, F., & Li, Z. Surface temperature and emissivity at various scales: Definition, measurement and related problems. Remote Sensing Reviews. 1995; 12(3–4): 225–253. https://doi.org/10.1080/02757259509532286
  • 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):1589-1609. https://doi.org/10.3390/rs2061589
  • Michael F. Goodchild, Linna Li. Assuring the quality of volunteered geographic information. Spatial Statistics. Volume 1, May 2012, Pages 110-120.
  • Cressie, N. (1993). Statistics for Spatial Data. Wiley Series in Probability and Statistics. Wiley. Subsequent edition
  • 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):1438. https://doi.org/10.3390/w14091438
  • 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):268. https://doi.org/10.3390/drones6100268
  • Huang, Y., & Zhang, L. (2017). Challenges in remote sensing-based salinity assessment: A review. Remote Sensing, 9(4), 332. doi:10.3390/rs9040332
  • I Metternicht, J. A Zinck. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment. Volume 85, Issue 1, 25 April 2003, Pages 1-20.
  • Alexander F.H. Goetz. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment. Volume 113, Supplement 1, September 2009, Pages S5-S16
  • Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138-146. doi:10.1890/120150
  • 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):569. https://doi.org/10.3390/rs15030569
  • Google Earth Engine. (2024). 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.
  • Badapalli, P.K., Nakkala, A.B., 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). https://doi.org/10.1007/s41748-024-00442-9
  • Badapalli, P.K., Nakkala, A.B., Gugulothu, S. et al.Geospatial insights into urban growth and land cover transformation in Anantapur city, India. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05180-6
  • Badapalli, P. K., Kottala, R. B., & Pujari, P. S. (2023). Aeolian Desertification: Disaster with Visual Impact in Semi-arid Regions of Andhra Pradesh, South India. Springer.
  • Kumar, B. P., Anusha, B. N., Babu, K. R., & Sree, P. P. (2023). Identification of climate change impact and thermal comfort zones in semi-arid regions of AP, India using LST and NDBI techniques. Journal of Cleaner Production, 407, 137175.
  • Pasham, H., Gugulothu, S., Badapalli, P.K. 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 31, 54129–54146 (2024). https://doi.org/10.1007/s11356-022-24863-z
  • Kumar, B. P., Babu, K. R., Anusha, B. N., & Rajasekhar, M. (2022). Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI/TIRS, LST, and NDVI approach. Environmental Challenges, 8, 100578.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 16/09/2024
Accepted 20/09/2024
Published 04/10/2024
Views: 19

Check Our other Platform for Workshops in the field of AI, Biotechnology & Nanotechnology.
Learn more
Check Out Platform for Webinars in the field of AI, Biotech. & Nanotech.
Learn more