Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches
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DOI: 10.1371/journal.pone.0153673
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Cited by:
- Johannes Schmidt & Nik Usmar & Leon Westphal & Max Werner & Stephan Roller & Reinhard Rademacher & Peter Kühn & Lukas Werther & Aline Kottmann, 2023. "Erosion Modelling Indicates a Decrease in Erosion Susceptibility of Historic Ridge and Furrow Fields near Albershausen, Southern Germany," Land, MDPI, vol. 12(3), pages 1-11, February.
- Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
- Masoud Zolfaghari Nia & Mostafa Moradi & Gholamhosein Moradi & Ruhollah Taghizadeh-Mehrjardi, 2022. "Machine Learning Models for Prediction of Soil Properties in the Riparian Forests," Land, MDPI, vol. 12(1), pages 1-15, December.
- Kennedy Were & Syphyline Kebeney & Harrison Churu & James Mumo Mutio & Ruth Njoroge & Denis Mugaa & Boniface Alkamoi & Wilson Ng’etich & Bal Ram Singh, 2023. "Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya," Land, MDPI, vol. 12(4), pages 1-19, April.
- Carmen Cianfrani & Aline Buri & Eric Verrecchia & Antoine Guisan, 2018. "Generalizing soil properties in geographic space: Approaches used and ways forward," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
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