Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates
Author:
Funder
University of Tehran
Publisher
Springer Science and Business Media LLC
Subject
Soil Science,Agronomy and Crop Science
Link
https://link.springer.com/content/pdf/10.1007/s10705-023-10303-y.pdf
Reference68 articles.
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2. Alloway BJ (2013) Heavy metals and metalloids as micronutrients for plants and animals. In: Alloway B (eds) Heavy Metals in Soils. Environmental Pollution, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4470-7_7
3. ALOS PALSAR (2021) Dataset:© JAXA/METI ALOS PALSAR L1.0 2007. Accessed through ASF DAAC 05 September 2021
4. Azizi K, Ayoubi S, Nabiollahi K, Garosi Y, Gislum R (2022) Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran. J Geochem Explor 233:106921. https://doi.org/10.1016/j.gexplo.2021.106921
5. Bagherzadeh A, Ghadiri E, Souhani Darban AR, Gholizadeh A (2016) Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region. Model Earth Syst Environ 2:1–11. https://doi.org/10.1007/s40808-016-0152-4
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