Open Access
Issue
BIO Web Conf.
Volume 231, 2026
International Scientific Conference “Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East and Remote Regions: Transforming Agri-Systems through Disruptive Innovation” (AFE-2025)
Article Number 00024
Number of page(s) 16
DOI https://doi.org/10.1051/bioconf/202623100024
Published online 10 April 2026
  • I.Yu. Savin, A.G. Terekhov, E.N. Amirkhaliev, G.N. Sagatdinova, Satellite monitorin g of salinization of irrigated soils in southern Kazakhstan. Soil Science 10, 1259–1268 (2023). https://doi.org/10.31857/S0032180X23600543 [Google Scholar]
  • D. Tola, F. Satgé, R. Pillco Zolá, et al., Soil salinity mapping of plowed agriculture la nds combining radar Sentinel-1 and optical Sentinel-2 with topographic data in machi ne learning models. Remote Sensing 16 (18), 3456 (2024). https://doi.org/10.3390/rs16183456 [Google Scholar]
  • K.O. Prokopyeva, I.V. Sobolev, Digital mapping of soil salinity in the southern steppe zone of Russia based on artificial neural networks and linear regression. Bulletin of M oscow University. Series 17: Soil Science 79 (4), 170–183 (2024). https://doi.org/10.55959/MSU0137-0944-17-2024-79-4-170-183 [Google Scholar]
  • S. Periasamy, K.P. Ravi, K. Tansey, Identification of saline landscapes from an integr ated SVM approach from a novel 3-D classification schema using Sentinel-1 dual-pol arized SAR data. Remote Sensing of Environment 279, 113144 (2022). https://doi.org/10.1016/j.rse.2022.113144 [Google Scholar]
  • G. Ma, J. Ding, L. Han, et al., Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms. Regional Sustainability 2 (2), 177–188 (2021). https://doi.org/10.1016/j.regsus.2021.06.001 [Google Scholar]
  • G. Sahbeni, M. Ngabire, P.K. Musyimi, B. Székely, Challenges and opportunities in r emote sensing for soil salinization mapping and monitoring: A review. Remote Sensin g 15 (10), 2540 (2023). https://doi.org/10.3390/rs15102540 [Google Scholar]
  • Ya. Yan, K. Kayem, Ye. Hao, et al., Mapping the levels of soil salination and alkalizat ion by integrating machine learning methods and soil-forming factors. Remote Sensing 14 (13), 3020 (2022). https://doi.org/10.3390/rs14133020 [Google Scholar]
  • I. Yahiaoui, A. Douaoui, A. Bradaï, M.A. Abdennour, Performance of random forest a nd buffer analysis of Sentinel-2 data for modeling soil salinity in the Lower-Cheliff pl ain (Algeria). International Journal of Remote Sensing 42 (1), 128–151 (2021). https://doi.org/10.1080/01431161.2020.1823515 [Google Scholar]
  • J.W. Sirpa-Poma, F. Satgé, E. Resongles, et al., Towards the improvement of soil salin ity mapping in a data-scarce context using Sentinel-2 images in machine-learning models. Sensors 23 (23), 9328 (2023). https://doi.org/10.3390/s23239328 [Google Scholar]
  • M. Golestani, Z. Mosleh Ghahfarokhi, I. Esfandiarpour-Boroujeni, H. Shirani, Evaluat ing the spatiotemporal variations of soil salinity in Sirjan Playa, Iran using Sentinel-2 A and Landsat-8 OLI imagery. Catena 231, 107375 (2023). https://doi.org/10.1016/j.catena.2023.107375 [Google Scholar]
  • S.A. Mohamed, M.M. Metwaly, M.R. Metwalli, et al., Integrating active and passive r emote sensing data for mapping soil salinity using machine learning and feature select ion approaches in arid regions. Remote Sensing 15 (7), 1751 (2023). https://doi.org/10.3390/rs15071751 [Google Scholar]
  • S. Aksoy, E. Sertel, A. Yildirim, et al., Assessing the performance of machine learnin g algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Advances in Space Research 69 (2), 1072–1086 (2021). h ttps://doi.org/10.1016/j.asr.2021.10.024 [Google Scholar]
  • J. Wang, T. Wang, W. Liu, et al., Soil salinity mapping using machine learning algorit hms with the Sentinel-2 MSI in arid areas, China. Remote Sensing 13 (2), 1–14 (2021). https://doi.org/10.3390/rs13020305 [Google Scholar]
  • E. Scudiero, T.H. Skaggs, D.L. Corwin, Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment 169, 335–343 (20 15). https://doi.org/10.1016/j.rse.2015.08.026 [Google Scholar]
  • H. Zhang, X. Fu, Ya. Zhang, et al., Mapping multi-depth soil salinity using remote sen sing-enabled machine learning in the Yellow River Delta, China. Remote Sensing 15(24), 5640 (2023). https://doi.org/10.3390/rs15245640 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.