Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms
Author:
Funder
Shiraz University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11368-024-03825-7.pdf
Reference98 articles.
1. Asadzadeh F, Maleki-Kakelar M, Shabani F (2019) Predicting cationic exchange capacity in calcareous soils of East-Azerbaijan province, northwest Iran. Commun Soil Sci Plant Anal 50:1106–1116. https://doi.org/10.1080/00103624.2019.1604728
2. Asgari N, Ayoubi S, Demattê JAM, Dotto AC (2020) Carbonates and organic matter in soils characterized by reflected energy from 350–25000 nm wavelength. J Mt Sci 17:1636–1651. https://doi.org/10.1007/s11629-019-5789-9
3. Babaeian E, Homaee M, Vereecken H, Montzka C, Norouzi AA, van Genuchten MT (2015) A comparative study of multiple approaches for predicting the soil–water retention curve: Hyperspectral information vs. basic soil properties. Soil Sci Soc Am J 79:1043–1058. https://doi.org/10.2136/sssaj2014.09.0355
4. Baumann P, Helfenstein A, Gubler A, Keller A, Meuli RG, Wächter D, Lee J, Viscarra Rossel R, Six J (2021) Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring. Soil 7:525–546. https://doi.org/10.5194/soil-7-525-2021
5. Bayat H, Davatgar N, Jalali M (2014) Prediction of CEC using fractal parameters by artificial neural networks. Int Agrophys 28:143–152. https://doi.org/10.2478/intag-2014-0002
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