Machine learning in space and time for modelling soil organic carbon change

Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a dat...

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Veröffentlicht in:	European journal of soil science 2021-07, Vol.72 (4), p.1607-1623
Hauptverfasser:	Heuvelink, Gerard B. M., Angelini, Marcos E., Poggio, Laura, Bai, Zhanguo, Batjes, Niels H., Bosch, Rik, Bossio, Deborah, Estella, Sergio, Lehmann, Johannes, Olmedo, Guillermo F., Sanderman, Jonathan
Format:	Artikel
Sprache:	eng
Schlagworte:	Argentina Biodegradation Calibration Carbon carbon stock Climate change Climate change mitigation Desertification Information management Intergovernmental Panel on Climate Change International organizations International policies International policy Land degradation Land management Land use Learning algorithms Machine learning Mapping Mitigation Normalized difference vegetative index Organic carbon Organic soils Predictions quantile regression forest Soil Soil sampling space–time mapping Statistical analysis Stocks Temporal distribution Temporal variations Topsoil Uncertainty
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