| DOI: | 10.1016/j.geoderma.2020.114237
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| 论文题名: | Model averaging for mapping topsoil organic carbon in France |
| 作者: | Chen S.; Mulder V.L.; Heuvelink G.B.M.; Poggio L.; Caubet M.; Román Dobarco M.; Walter C.; Arrouays D.
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| 刊名: | Geoderma
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| ISSN: | 167061
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| 出版年: | 2020
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| 卷: | 366 | | 语种: | 英语
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| 英文关键词: | Bias-corrected Variance Weighted
; Data-poor countries
; Digital soil mapping
; Sample size requirement
; Soil organic carbon
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| Scopus关键词: | Bayesian networks
; Climate change
; Developing countries
; Land use
; Mapping
; Soil surveys
; Soils
; Uncertainty analysis
; Bias-corrected Variance Weighted
; Data-poor countries
; Digital soil mappings
; Sample sizes
; Soil organic carbon
; Organic carbon
; biogeochemical cycle
; calibration
; digital mapping
; model validation
; numerical model
; organic carbon
; soil organic matter
; topsoil
; France
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| 英文摘要: | The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monitoring of SOC dynamics due to land use change and climate change. However, current estimates of SOC stock and its spatial distribution have large uncertainties. In this study, we test whether we can improve the accuracy of the three existing SOC maps of France obtained at national (IGCS), continental (LUCAS), and global (SoilGrids) scales using statistical model averaging approaches. Soil data from the French Soil Monitoring Network (RMQS) were used to calibrate and evaluate five model averaging approaches, i.e., Granger-Ramanathan, Bias-corrected Variance Weighted (BC-VW), Bayesian Modelling Averaging, Cubist and Residual-based Cubist. Cross-validation showed that with a calibration size larger than 100 observations, the five model averaging approaches performed better than individual SOC maps. The BC-VW approach performed best and is recommended for model averaging. Our results show that 200 calibration observations were an acceptable calibration strategy for model averaging in France, showing that a fairly small number of spatially stratified observations (sampling density of 1 sample per 2500 km2) provides sufficient calibration data. We also tested the use of model averaging in data-poor situations by reproducing national SOC maps using various sized subsets of the IGCS dataset for model calibration. The results show that model averaging always performs better than the national SOC map. However, the Modelling Efficiency dropped substantially when the national SOC map was excluded in model averaging. This indicates the necessity of including a national SOC map for model averaging, even if produced with a small dataset (i.e., 200 samples). This study provides a reference for data-poor countries to improve national SOC maps using existing continental and global SOC maps. © 2020 Elsevier B.V. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/158297
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| Appears in Collections: | 气候变化与战略
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作者单位: | INRAE, Unité InfoSol, Orléans, 45075, France; SAS, INRAE, Agrocampus Ouest, Rennes, 35042, France; Soil Geography and Landscape Group, Wageningen University, PO Box 47, AA Wageningen, 6700, Netherlands; ISRIC–World Soil Information, PO Box 353, AJ Wageningen, 6700, Netherlands
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| Recommended Citation: | |
Chen S.,Mulder V.L.,Heuvelink G.B.M.,et al. Model averaging for mapping topsoil organic carbon in France[J]. Geoderma,2020-01-01,366
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