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

• Autores: Heuvelink, Gerard B.M.; Angelini, Marcos Esteban; Poggio, Laura; Bai, Zhanguo; Batjes, Niels H.; van den Bosch, Rik; Bossio, Deborah; Estella, Sergio; Lehmann, Johannes; Olmedo, Guillermo Federico; Sanderman, Jonathan
• Año de publicación: 2020
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: European Journal of Soil Science : 1-17 (First published: 20 May 2020)
• Materia: Argentina; Estimación de las Existencias de Carbono; Cambio Climático; Degradación de Tierras; Carbon Stock Assessments; Climate Change; Land Degradation; Quantile Regression Rorest; Space-time Mapping; Bosque de Regresión de Cuantiles; Mapeo Espacio-tiempo
• Institución: Instituto Nacional de Tecnología Agropecuaria

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 data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and static, whereas SOC is dynamic and SOC dynamics are of particular interest to carbon sequestration and land degradation studies. Thus, there is a clear need to extend spatial SOC mapping to space–time SOC mapping. temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.
Fil: Heuvelink, Gerard B.M. ISRIC - World soil information; Holanda. Wageningen University. Soil Geography and Landscape Group; Holanda
Fil: Angelici, Marcos E. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Poggio, Laura ISRIC - World soil information, Wageningen; Holanda
Fil: Bai, Zhanguo ISRIC - World soil information, Wageningen, The Netherlands
Fil: Batjes, Niels H. ISRIC - World soil information, Wageningen, The Netherlands
Fil: an den Bosch, Rik ISRIC - World soil information, Wageningen, The Netherlands
Fil: Bossio, Deborah The Nature Conservancy; Estados Unidos
Fil: Estella, Sergio Vizzuality; España
Fil: Lehmann, Jhoannes. Cornell University. Soil and Crop Sciences; Estados Unidos
Fil: Olmedo, Guillermo F. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina
Fil: Sandermann, Jonathan. Woods Hole Research Center; Estados Unidos