
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Machine learning in space and time for modelling soil organic carbon change

doi: 10.1111/ejss.12998
handle: 20.500.12123/8054
AbstractSpatially 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 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.Highlights We tested the use of machine learning for space–time mapping of soil organic carbon (SOC) stock. Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time. The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach. Accurate machine learning SOC stock prediction requires dense soil sampling in space and time.
- Woodwell Climate Research Center United States
- The Nature Conservancy United States
- Cornell University United States
- Woods Hole Research Center United States
- National Agricultural Technology Institute Argentina
Climate Change, Argentina, Bosque de Regresión de Cuantiles, Estimación de las Existencias de Carbono, quantile regression forest, land degradation, carbon stock, Carbon Stock Assessments, Space-time Mapping, space–time mapping, climate change, Cambio Climático, Land Degradation, Quantile Regression Rorest, Mapeo Espacio-tiempo, Degradación de Tierras
Climate Change, Argentina, Bosque de Regresión de Cuantiles, Estimación de las Existencias de Carbono, quantile regression forest, land degradation, carbon stock, Carbon Stock Assessments, Space-time Mapping, space–time mapping, climate change, Cambio Climático, Land Degradation, Quantile Regression Rorest, Mapeo Espacio-tiempo, Degradación de Tierras
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).109 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
