{"type": "FeatureCollection", "features": [{"id": "10.1016/j.geoderma.2020.114237", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:35Z", "type": "Journal Article", "created": "2020-02-06", "title": "Model averaging for mapping topsoil organic carbon in France", "description": "Abstract   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\u00a0km2) 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.", "keywords": ["Soil organic carbon", "[SDV]Life Sciences [q-bio]", "cartographie num\u00e9rique des sols", "04 agricultural and veterinary sciences", "Data-poor countries", "cartographie num\u00e9rique du sol", "15. Life on land", "01 natural sciences", "soil sciences", "sciences du sol", "[SDV] Life Sciences [q-bio]", "Digital soil mapping", "Sample size requirement", "13. Climate action", "Bias-corrected Variance Weighted", "carbone organique du sol", "0401 agriculture", " forestry", " and fisheries", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.science/hal-02473703/file/revised%20accepted%20version%20Chen%20et%20al.pdf"}, {"href": "https://doi.org/10.1016/j.geoderma.2020.114237"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2020.114237", "name": "item", "description": "10.1016/j.geoderma.2020.114237", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2020.114237"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-01T00:00:00Z"}}, {"id": "3005528129", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:35Z", "type": "Journal Article", "created": "2020-02-06", "title": "Model averaging for mapping topsoil organic carbon in France", "description": "Abstract   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\u00a0km2) 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.", "keywords": ["Soil organic carbon", "[SDV]Life Sciences [q-bio]", "cartographie num\u00e9rique des sols", "04 agricultural and veterinary sciences", "cartographie num\u00e9rique du sol", "Data-poor countries", "15. Life on land", "01 natural sciences", "soil sciences", "sciences du sol", "[SDV] Life Sciences [q-bio]", "Digital soil mapping", "Sample size requirement", "13. Climate action", "carbone organique du sol", "Bias-corrected Variance Weighted", "0401 agriculture", " forestry", " and fisheries", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.science/hal-02473703/file/revised%20accepted%20version%20Chen%20et%20al.pdf"}, {"href": "https://doi.org/3005528129"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3005528129", "name": "item", "description": "3005528129", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3005528129"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-01T00:00:00Z"}}, {"id": "9b81642374175d90e0b717deca64ff67", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:28:30Z", "type": "Report", "title": "Satellite time series contribution to organic carbon mapping in cultivated soils at various regional scales", "description": "Open AccessLe carbone organique du sol (COS) dans les zones agricoles joue un r\u00f4le cl\u00e9 dans la s\u00e9curit\u00e9 alimentaire et l'att\u00e9nuation du changement climatique. La quantification du COS est n\u00e9cessaire pour mettre en \u0153uvre des techniques et des pratiques de stockage. Cependant, l'\u00e9chantillonnage du COS dans un monde qui couvre environ 1,5 milliard d'hectares de sols agricoles est un v\u00e9ritable d\u00e9fi. C'est pourquoi l'utilisation de technologies telles que les capteurs satellitaires constitue une alternative prometteuse pour quantifier et cartographier le COS dans diff\u00e9rents types d'agro\u00e9cosyst\u00e8mes \u00e0 travers le monde. L'objectif de cette th\u00e8se est d'\u00e9valuer le potentiel des images satellitaires Sentinel-2 (S2) et Sentinel-1 (S1) pour la cartographie du COS dans les agro-\u00e9cosyst\u00e8mes de la France m\u00e9tropolitaine en utilisant des mod\u00e8les spectraux et spatio-spectraux. 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La quantification du COS est n\u00e9cessaire pour mettre en \u0153uvre des techniques et des pratiques de stockage. Cependant, l'\u00e9chantillonnage du COS dans un monde qui couvre environ 1,5 milliard d'hectares de sols agricoles est un v\u00e9ritable d\u00e9fi. C'est pourquoi l'utilisation de technologies telles que les capteurs satellitaires constitue une alternative prometteuse pour quantifier et cartographier le COS dans diff\u00e9rents types d'agro\u00e9cosyst\u00e8mes \u00e0 travers le monde. L'objectif de cette th\u00e8se est d'\u00e9valuer le potentiel des images satellitaires Sentinel-2 (S2) et Sentinel-1 (S1) pour la cartographie du COS dans les agro-\u00e9cosyst\u00e8mes de la France m\u00e9tropolitaine en utilisant des mod\u00e8les spectraux et spatio-spectraux. Le chapitre 1 aborde l'\u00e9tat d'avancement de la cartographie du COS en France et pr\u00e9sente les principales limitations et m\u00e9thodes actuellement utilis\u00e9es avec les donn\u00e9es d'images satellitaires pour la pr\u00e9diction du COS. Le chapitre 2 pr\u00e9sente les zones d'\u00e9tude situ\u00e9es dans les r\u00e9gions Bretagne, Occitanie et Centre Val de Loire. De plus, les principaux ensembles de donn\u00e9es utilis\u00e9s sont d\u00e9crits et une analyse pr\u00e9liminaire de l'une des zones d'\u00e9tude est pr\u00e9sent\u00e9e. Le troisi\u00e8me chapitre \u00e9value le potentiel des images S2 et des produits d\u00e9riv\u00e9s de S1 et S2 pour pr\u00e9dire le SOC \u00e0 l'aide d'images \u00e0 date unique. Dans ce chapitre comme dans le second, des limitations li\u00e9es principalement aux conditions de surface du sol ont \u00e9t\u00e9 observ\u00e9es ; et les meilleures dates d'image pour d\u00e9tecter le SOC ont \u00e9t\u00e9 identifi\u00e9es. Dans la quatri\u00e8me au lieu d'images \u00e0 date unique, l'utilisation de mosa\u00efques temporelles S2 de sol nu (S2Bsoil) par p\u00e9riodes est abord\u00e9e comme l'utilisation de covariables d\u00e9riv\u00e9es de l'imagerie satellitaire et du terrain. Ce chapitre traite de l'importance de la s\u00e9lection des p\u00e9riodes de production de S2Bsol et de l'utilisation de covariables pertinentes pour comprendre la variabilit\u00e9 spatiale du COS \u00e0 l'\u00e9chelle r\u00e9gionale. 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