Current Estimates of Soil Organic Carbon Stocks Are Not Four to Six Times Underestimated. Comment on "Non-Flat Earth Recalibrated for Terrain and Topsoil. Soil Syst. 2018, 2, 64"

In the interesting paper "Non-Flat Earth Recalibrated for Terrain and Topsoil" published in Soil Systems [...

Vertical distribution and influencing factors of deep soil organic carbon in a typical subtropical agricultural watershed

Acknowledgement This study was financially supported by the National Key Research and Development Program of China (2018YFE0107000), the National Natural Science Foundation of China (42107334), the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0306), and the China Postdoctoral Science Foundation (2021TQ0337, 2021M703305). Special thanks go to our colleagues of Soils in Time and Space team for their help during field survey and laboratory analysis.Peer reviewe

Digital mapping of GlobalSoilMap soil properties at a broad scale: a review

Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources

High resolution soil property maps over mainland France : application to soil organic carbon and its additional sequestration and storage potentials

Cette thèse est une contribution à la Cartographie Numérique des Sols (CNS) sur de vastes espaces. Dans le Chapitre 1, je discute des principaux facteurs à l’origine de l’émergence et du développement de la CNS et j’en retrace brièvement l’historique. Dans le Chapitre 2, je réalise une revue générale de la CNS sur de vastes espaces en me fondant sur 160 articles publiés de 2003 à mi-2019. J’y synthétise et discute les principales avancées et défis pour la communauté de la CNS. Je me focalise ensuite sur le carbone organique des sols (COS) en raison de son importance fondamentale pour les services écosystémiques et le cycle global du carbone. Dans le Chapitre 3, je montre comment améliorer une carte nationale du COS en agrégeant différentes cartes de COS et je donne des des pistes sur la façon de tirer parti deprédictions globales pour les pays disposant de peu de données, en utilisant une stratégie d’échantillonnage peu coûteuse et efficace.Ensuite, dans les Chapitres 4 et 5, je me concentre sur le domaine de validité de fonctions de pédotranfert utilisées pour la prédiction de la densité apparente et je développe une approche nouvelle pour traiter l’épaisseur des sols en France. J’y propose également des stratégies efficientes pour améliorer la précision de leurs prédictions. Je passe ensuite de la CNS à la cartographie de fonctions des sols en prenant comme exemple la cartographie du potentiel de séquestation (Chapitre 6) et de stockage (Chapitre 7) en COS. Ces chapitres contribuent à améliorer de nombreux aspects concernant la CNS et son application au programme GlobThis thesis is a contribution to Digital Soil Mapping (DSM) at broad scale, with applications on the French mainland territory. In Chapter 1, I discussed the main drivers for the rise and development of DSM and gave a brief history about DSM. In Chapter 2, I made a general review about broad-scale DSM by reviewing 160 selected articles from 2003 to mid-2019. I synthetized and discussed the main achievements and challenges for the DSM community. Then I decided to focus on soil organic carbon (SOC) because of its main importance for ecosystem services and global carbon cycle. In Chapter 3, I showed how to improve a national SOC map by merging various SOC maps and provided inputs on how to take advantage of global predictions in ‘data-poor’ countries using a low cost and efficient sampling strategy. Then in Chapters 4 and 5, I focused onthe validity domain of pedotransfer functions used for bulk density predictions and on developing a novel approach to deal with soil thickness prediction over France. I also proposed efficient sampling strategies to improve the accuracy of their predictions. I moved from DSM to Digital Soil Assessment (DSA), exemplified by SOC sequestration potential in Chapter 6 and SOC storage potentials in Chapter 7. They contribute to improving some aspects related to DSM and GlobalSoilMap. In Chapter 8, I finished this thesis by discussing the most important findings of my work and relating them to main challenges of Pedometrics. I outlined the inputs that my work provided to reaching these challenges and highlighted the remaining issues to be solved i

Cartographie à haute résolution de propriétés du sol à l’échelle de la France métropolitaine : application au carbone organique du sol et à ses potentiels additionnels de séquestration et de stockage

This thesis is a contribution to Digital Soil Mapping (DSM) at broad scale, with applications on the French mainland territory. In Chapter 1, I discussed the main drivers for the rise and development of DSM and gave a brief history about DSM. In Chapter 2, I made a general review about broad-scale DSM by reviewing 160 selected articles from 2003 to mid-2019. I synthetized and discussed the main achievements and challenges for the DSM community. Then I decided to focus on soil organic carbon (SOC) because of its main importance for ecosystem services and global carbon cycle. In Chapter 3, I showed how to improve a national SOC map by merging various SOC maps and provided inputs on how to take advantage of global predictions in ‘data-poor’ countries using a low cost and efficient sampling strategy. Then in Chapters 4 and 5, I focused onthe validity domain of pedotransfer functions used for bulk density predictions and on developing a novel approach to deal with soil thickness prediction over France. I also proposed efficient sampling strategies to improve the accuracy of their predictions. I moved from DSM to Digital Soil Assessment (DSA), exemplified by SOC sequestration potential in Chapter 6 and SOC storage potentials in Chapter 7. They contribute to improving some aspects related to DSM and GlobalSoilMap. In Chapter 8, I finished this thesis by discussing the most important findings of my work and relating them to main challenges of Pedometrics. I outlined the inputs that my work provided to reaching these challenges and highlighted the remaining issues to be solved inCette thèse est une contribution à la Cartographie Numérique des Sols (CNS) sur de vastes espaces. Dans le Chapitre 1, je discute des principaux facteurs à l’origine de l’émergence et du développement de la CNS et j’en retrace brièvement l’historique. Dans le Chapitre 2, je réalise une revue générale de la CNS sur de vastes espaces en me fondant sur 160 articles publiés de 2003 à mi-2019. J’y synthétise et discute les principales avancées et défis pour la communauté de la CNS. Je me focalise ensuite sur le carbone organique des sols (COS) en raison de son importance fondamentale pour les services écosystémiques et le cycle global du carbone. Dans le Chapitre 3, je montre comment améliorer une carte nationale du COS en agrégeant différentes cartes de COS et je donne des des pistes sur la façon de tirer parti deprédictions globales pour les pays disposant de peu de données, en utilisant une stratégie d’échantillonnage peu coûteuse et efficace.Ensuite, dans les Chapitres 4 et 5, je me concentre sur le domaine de validité de fonctions de pédotranfert utilisées pour la prédiction de la densité apparente et je développe une approche nouvelle pour traiter l’épaisseur des sols en France. J’y propose également des stratégies efficientes pour améliorer la précision de leurs prédictions. Je passe ensuite de la CNS à la cartographie de fonctions des sols en prenant comme exemple la cartographie du potentiel de séquestation (Chapitre 6) et de stockage (Chapitre 7) en COS. Ces chapitres contribuent à améliorer de nombreux aspects concernant la CNS et son application au programme Glo

Soil carbon stocks are underestimated in mountainous regions

International audienceCurrent estimates of soil organic carbon (SOC) stocks are calculated by multiplying the SOC density times the planimetric area of an individual cell. In the reality, the land surface is not always in horizontal planes, but sometimes in tilted planes, especially in mountainous regions. The differences between a horizontal plane and a tilted plane are controlled by slope, so we investigated the effect of inclination on the SOC stocks calculation using HWSD dataset in mountainous regions including the Alps, the Andes, the Plateau of Tibet and the Rocky Mountains. Our results showed that inclination effect strongly influenced SOC stocks calculation in mountainous regions and previous SOC stocks estimates were underestimated. SOC stocks increased between 4.04% and 15.00% when 90 m resolution elevation data was used for accounting the inclination effect, which was much higher than that of using 1 km resolution elevation (0.90% to 5.00%). Therefore, we suggest that it is necessary to consider the inclination effect in the calculation of SOC stocks in mountainous regions

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g&middot;kg&minus;1, 1.15 to 1.51 g&middot;kg&minus;1, and 0.80 to 0.90 g&middot;kg&minus;1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0&ndash;20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20&ndash;40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability

Role of Environment Variables in Spatial Distribution of Soil C, N, P Ecological Stoichiometry in the Typical Black Soil Region of Northeast China

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g·kg−1, 1.15 to 1.51 g·kg−1, and 0.80 to 0.90 g·kg−1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0–20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20–40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Visible and near-infrared (Vis&ndash;NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC