A COMPARISON OF MACHINE LEARNING MODELS FOR SOIL SALINITY ESTIMATION USING MULTI-SPECTRAL EARTH OBSERVATION DATA

26 citations as recorded by crossref.

1. The usage of machine learning approach in predicting oil extraction rate based on yield making, yield taking and mill processing factors on oil palm plantations in East Kalimantan, Indonesia N. Suharyanti et al. https://doi.org/10.1088/1755-1315/1477/1/012055
2. Evaluating soil salinity patterns in Tehran Province: An artificial neural network model incorporating satellite images and geomorphometric parameters M. Sadeghi Pour Marvi & S. Moharami https://doi.org/10.1016/j.asr.2025.07.074
3. Future runoff assessment under climate change and land-cover alteration scenarios: a case study of the Zayandeh-Roud dam upstream watershed A. Ahmadi et al. https://doi.org/10.2166/nh.2022.056
4. Soil security under salt attack: Protection of the soil against the salinization caused by drying up of Lake Urmia M. Faryadi https://doi.org/10.1016/j.soisec.2023.100113
5. Potential evaluation of visible-thermal UAV image fusion for individual tree detection based on convolutional neural network F. Moradi et al. https://doi.org/10.1016/j.jag.2022.103011
6. Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning Y. Ang et al. https://doi.org/10.1007/s12145-022-00882-9
7. Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years G. Yang et al. https://doi.org/10.3390/rs16224294
8. A Two-Stage XGBoost Approach for Mapping Arable and Non-Arable Soils under Salinity Stress in Southern Iraq M. Azeez et al. https://doi.org/10.2478/cee-2026-0017
9. Prediction of Soil Salinity/Sodicity and Salt-Affected Soil Classes from Soluble Salt Ions Using Machine Learning Algorithms D. Andrade Foronda & G. Colinet https://doi.org/10.3390/soilsystems7020047
10. Estimating soil salinity using multiple spectral indexes and machine learning algorithm in Songnen Plain, China H. Ge et al. https://doi.org/10.1109/JSTARS.2023.3274579
11. Assessment of soil salinity using explainable machine learning methods and Landsat 8 images S. Aksoy et al. https://doi.org/10.1016/j.jag.2024.103879
12. A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning H. Shi et al. https://doi.org/10.1109/TGRS.2021.3109819
13. Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran M. Golkar Amoli et al. https://doi.org/10.3390/rs16122149
14. Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean S. Mohammed et al. https://doi.org/10.1016/j.jenvman.2024.122640
15. Eliminating the influence of moisture to enhance the accuracy of soil salinity estimation using hyperspectral remote sensing N. Lin et al. https://doi.org/10.1080/22797254.2026.2671832
16. Machine learning and remote sensing for precision agriculture: Progresses and challenges X. Yao et al. https://doi.org/10.1177/2754124X261435906
17. Mapping invasive alien plant species with very high spatial resolution and multi-date satellite imagery using object-based and machine learning techniques: A comparative study F. Nininahazwe et al. https://doi.org/10.1080/15481603.2023.2190203
18. Estimation of major soil water-soluble salt ions from Sentinel-2 imagery: Integrating spectral and texture features X. Feng et al. https://doi.org/10.1016/j.iswcr.2025.12.003
19. Multispectral data mining: A focus on remote sensing satellite images S. Lim et al. https://doi.org/10.1002/widm.1522
20. Soil salinization prediction through feature selection and machine learning at the irrigation district scale J. Xie et al. https://doi.org/10.3389/feart.2024.1488504
21. Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization B. Chen et al. https://doi.org/10.1080/01431161.2021.2009589
22. Mapping common and glossy buckthorns (Frangula alnusandRhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 F. Nininahazwe et al. https://doi.org/10.1080/17538947.2022.2162136
23. Extracting Plastic Greenhouses from Remote Sensing Images with a Novel U-FDS Net Y. Mo et al. https://doi.org/10.3390/rs15245736
24. Using Pseudo-Color Maps and Machine Learning Methods to Estimate Long-Term Salinity of Soils R. Mukhamediev et al. https://doi.org/10.3390/agronomy14092103
25. Multi-source remote sensing for spatial mapping of salt-affected agricultural soils in Taiwan N. Son et al. https://doi.org/10.1007/s12518-026-00689-z
26. A novel framework for soil salinity risk management based on climate change and socioeconomic scenarios H. Nguyen et al. https://doi.org/10.1080/02723646.2025.2579104