Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms.

Rostaminia, Mahmood; Rahmani, Asghar; Mousavi, Sayed Roholla; Taghizadeh-Mehrjardi, Rohullah; Maghsodi, Ziba

Rostaminia, Mahmood; Rahmani, Asghar; Mousavi, Sayed Roholla; Taghizadeh-Mehrjardi, Rohullah; Maghsodi, Ziba.

Afiliación

Rostaminia M; Soil and Water Department, Agriculture Faculty, Ilam University, Ilam, Iran. m.rostaminya@ilam.ac.ir.
Rahmani A; Soil Science and Engineering Department, Faculty of Agricultural Engineering and Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
Mousavi SR; Soil Science and Engineering Department, Faculty of Agricultural Engineering and Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
Taghizadeh-Mehrjardi R; Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran.
Maghsodi Z; Soil Science and Geomorphology, Institute of Geography, Eberhard Karls University Tübingen, 72070, Tübingen, Germany.

Environ Monit Assess ; 193(12): 815, 2021 Nov 17.

Article en En | MEDLINE | ID: mdl-34787728

Asunto(s)

Carbono; Suelo; Algoritmos; Carbono/análisis; Monitoreo del Ambiente; Aprendizaje Automático

Palabras clave

Arid rangelands; Hybrid models; Limited data; Sentinel-2B indices; Soil organic carbon; Uncertainty

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 2_ODS3 Problema de salud: 2_quimicos_contaminacion Asunto principal: Suelo / Carbono Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2021 Tipo del documento: Article País de afiliación: Irán

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