Mapping Soil Organic Carbon Research Articles

Exploring the inter-decadal variability of soil organic carbon in China

Spatiotemporal variability in soil organic carbon (SOC) plays key roles in ecosystem stability and terrestrial carbon cycling. However, the inter-decadal characteristics of SOC variability and its climate drivers in China from the 1980s to 2010s remain unclear. In this study, a modeling framework for combining spatiotemporal prediction models was proposed. Within this model, soil samples from each of the two periods were combined and calibrated to obtain three models, and their prediction outputs were selectively combined to obtain SOC maps for the different inter-decadal periods. A total of 8473 soil samples from three periods (1980s, 2000s and 2010s) were used for model validation. The results suggested that the SOC prediction accuracy in the top 0–20 cm of soil (SOC020) was better than that of the first meter of soil (SOC0100), and the predicted spatial patterns shared characteristics with findings from previous studies. Predictions for the 1990s exhibited relatively high uncertainty, especially in the Tibetan Plateau, Tien Shan, and Northeast China. Both individual and coupled climatic factors exerted scale- and location-specific effects on the SOC. Coupled climatic factors accounted for more than 65% of the SOC variability in both soil layers. The regional SOC in North and Northeast China exhibited inter-decadal decline. Soil in China functioned as carbon sinks from the 1980s to 2010s at a national scale, with SOC020 increasing from 30.40 Pg to 33.97 Pg and SOC0100 from 79.29 Pg to 82.26 Pg. Overall, our results provide reliable baseline data for optimizing carbon cycle models of terrestrial ecosystems, and to guide future carbon sequestration policies.

Global and regional soil organic carbon estimates: Magnitudes and uncertainties

Globally, soil is the largest terrestrial carbon (C) reservoir. Robust quantification of soil organic C (SOC) stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends. We investigated the magnitudes and distributions of global and regional SOC estimates (i.e., density and stocks) based on five widely used global gridded SOC datasets, a regional permafrost dataset developed in 2021 (UM2021), and a global-scale soil profile database (World Soil Information Service) reporting measurements of a series of physical and chemical edaphic attributes. The five global gridded SOC datasets were the Harmonized World Soil Database (HWSD), World Inventory of Soil Emission Potentials at 30 arc-second resolution (WISE30sec), Global Soil Dataset for Earth System Models (GSDE), Global Gridded Soil Information at 250-m resolution (SoilGrids250m), and Global Soil Organic Carbon Map (GSOCmap). Our analyses showed that the magnitude and distribution of SOC varied widely among datasets, with certain datasets showing region-specific robustness. At the global scale, SOC stocks at the top 30 and 100 cm were estimated to be 828 (range: 577–1 171) and 1 873 (range: 1 086–2 678) Pg C, respectively. The estimates from GSDE, GSOCmap, and WISE30sec were comparable, and those of SoilGrids250m and HWSD were at the upper and lower ends. The spatial SOC distribution varied greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. Regionally, UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions, and GSDE performed well in China. The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes. The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil. The datasets demonstrated relatively higher agreement in grasslands, croplands, and shrublands/savannas than in other biomes (e.g., wetlands). The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses. Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density, different sources, and various mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates.

Assessing the carbon sequestration potential and identifying influential factors of cultivated soils in Northeast China

Cultivated lands play a crucial role in terrestrial carbon cycle, and enhancing soil organic carbon (SOC) sequestration in these areas can help effectively mitigate the rise of atmospheric CO2 concentration. In this study, topsoil (0–20 cm) saturated SOC and its density of cultivated soil in Northeast China were mapped using a boosted regression trees (BRT) model. The distribution of the SOC sequestration potential was also calculated based on the difference between saturated SOC and SOC density in ArcGIS. Nine environmental factors including climate, topography and lengths of cultivation data (LCD) and 197 soil samples are used. A 10-fold cross-validation technique is applied to derive four statistical indices - mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2) and Lin's consistent correlation coefficient (LCCC) to verify the model performance. The model explains 81% and 85% of the spatial variation of saturated SOC and SOC density, respectively. Mean annual temperature and mean annual precipitation are key factors controlling SOC density and saturated SOC distribution. In addition, LCD showed a similar spatial pattern to SOC sequestration potential, influencing the distribution of SOC density and saturated SOC in the study area. We recommend LCD as an important factor to consider in saturated SOC and SOC density predictions, especially in the farmland ecosystem with a long reclamation history. Accurate mapping SOC sequestration potential and identifying environmental factors will help manage land use and promote soil quality evaluation and improve soil carbon sequestration in this region.

National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine

Modeling accurate and detailed soil spatial information is essential for environmental modeling, precision soil management and decision-making. In this study, we integrated long-term optical (Sentinel-2) and radar (Sentinel-1) satellite observations via the Google Earth Engine (GEE) platform for high-resolution national-scale digital mapping of soil organic carbon (SOC) and total soil nitrogen (TSN) in Austria. Our soil predictive models based on boosted regression tree (BRT) and regression kriging (RK) methods were constructed from 449 soil samples (0–20 cm) covering the study area in the LUCAS soil database and Sentinel observations synthesized with different time intervals. The different input predictors of these soil predictive models resulted in seven modeling scenarios, and their prediction performance was evaluated by a cross-validation technique. Comparative analysis indicated that satellite sensors, modeling techniques, and SAR data acquisition configurations greatly affected the model outputs. Cross-polarization and co-polarization had similar performance in TSN and SOC predictions, and their combination improved the prediction accuracy. Predictive models based on Sentinel-1 with the “ASCENDING” orbits outperformed the models involving the “DESCENDING” orbits; the prediction accuracy of the former was comparable to models involving two orbital data. The models built by Sentinel-1 and Sentinel-2 performed similarly in predicting SOC (R2 = 0.51 vs. R2 = 0.52, respectively) and TSN (their R2 were both 0.42); their synergistic utilization improved the prediction results. Models involving more years of Sentinel observations on the GEE platform provided more accurate modeling results. The best soil predictive models explained 55% and 45% of soil variability for SOC and TSN, respectively, both constructed from long-term Sentinel-1/2 observations using the RK method. The overall trends of the mapping results of the models constructed by Sentinel-1 and Sentinel-2 and their combinations were consistent. The predicted digital soil maps displayed high spatial heterogeneity: SOC and TSN—shared similar spatial patterns—were greater in high-altitude central and western regions than other regions. This study provides valuable information for revealing the effects of satellite sensors, modeling techniques and SAR configurations on mapping SOC and TSN.

Mapping soil organic carbon stocks in Nepal’s forests

Comprehensive forest carbon accounting requires reliable estimation of soil organic carbon (SOC) stocks. Despite being an important carbon pool, limited information is available on SOC stocks in global forests, particularly for forests in mountainous regions, such as the Central Himalayas. The availability of consistently measured new field data enabled us to accurately estimate forest soil organic carbon (SOC) stocks in Nepal, addressing a previously existing knowledge gap. Our method involved modelling plot-based estimates of forest SOC using covariates related to climate, soil, and topographic position. Our quantile random forest model resulted in the high spatial resolution prediction of Nepal’s national forest SOC stock together with prediction uncertainties. Our spatially explicit forest SOC map showed the high SOC levels in high-elevation forests and a significant underrepresentation of these stocks in global-scale assessments. Our results offer an improved baseline on the distribution of total carbon in the forests of the Central Himalayas. The benchmark maps of predicted forest SOC and associated errors, along with our estimate of 494 million tonnes (SE = 16) of total SOC in the topsoil (0–30 cm) of forested areas in Nepal, carry important implications for understanding the spatial variability of forest SOC in mountainous regions with complex terrains.

Perimeter-Area Soil Carbon Index (PASCI): modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment

ABSTRACT Soil Organic Carbon (SOC) is the most important indicator of soil health and determines long-term crop productivity. Here, we applied the Random Forest regression model to soil hyperspectral data to determine the important spectral bands and regions for SOC retrieval. Multiple existing studies already identified specific wavelength bands that could be good indicators of SOC. However, there is no hyperspectral-based method that is currently available to simultaneously investigate these identified specific wavelength regions for SOC. To help fill this gap, we developed the Perimeter-Area Soil Carbon Index (PASCI) that utilized optimal SOC spectral bands and then evaluated its robustness for SOC prediction and retrieval against other existing indices. The results of regression analysis between SOC and PASCI values showed a significant relationship (r2 = 0.76; p < 0.05). A significant statistical relationship (r2 = 0.73) was also observed between SOC and the sum indices. The results from this study have advanced our understanding of the optimal spectral bands for SOC. Finally, the PASCI could be applied to hyperspectral and multispectral images to remotely quantify, predict, and map SOC.

Mapping soil organic carbon fractions for Australia, their stocks, and uncertainty

Abstract. Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuous set of compounds with different chemical compositions, origins, and susceptibilities to decomposition that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric-log-ratio (ilr) transformation to account for the closed compositional nature of SOC fractions. The resulting back-transformed ilr components were mapped across Australia. SOC fraction stocks for 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments, and soil thickness. Mapping was done by a quantile regression forest fitted with the ilr-transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17 %, whereas 28 % ± 17 % was PyOC and 13 % ± 11 % was POC. The allocation of total organic carbon (TOC) to the MAOC fractions increased with depth. SOC vulnerability (i.e. POC/[MAOC+PyOC]) was greater in areas with Mediterranean and temperate climates. TOC and the distribution among fractions were the most influential variables in SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates' importance metric. We estimated the total SOC stocks (0–30 cm) to be 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as a response to land use change, management, and climate change.

Baseline high-resolution maps of organic carbon content in Australian soils

We introduce a new dataset of high-resolution gridded total soil organic carbon content data produced at 30 m × 30 m and 90 m × 90 m resolutions across Australia. For each product resolution, the dataset consists of six maps of soil organic carbon content along with an estimate of the uncertainty represented by the 90% prediction interval. Soil organic carbon maps were produced up to a depth of 200 cm, for six intervals: 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The maps were obtained through interpolation of 90,025 depth-harmonized organic carbon measurements using quantile regression forest and a large set of environmental covariates. Validation with 10-fold cross-validation showed that all six maps had relatively small errors and that prediction uncertainty was adequately estimated. The soil carbon maps provide a new baseline from which change in future carbon stocks can be monitored and the influence of climate change, land management, and greenhouse gas offset can be assessed.

Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China

The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the R2 (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies.

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and “ASCENDING” orbits outperformed those involving copolarization, a single time period and “DESCENDING” orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

AbstractUnderstanding the vertical and lateral distribution of soil organic carbon (SOC) and soil organic carbon density (SOCD) is indispensable for soil use and environmental management because of their vital role in soil quality assessments. Primarily, they are needed in calculating soil organic carbon storage (SOCS). The objective of this research was to provide digital maps of SOC and SOCD variation as well as their uncertainties at multiple standardized depths (H1: 0–5, H2: 5–15, H3: 15–30, H4: 30–60 and H5: 60–100 cm) using a parsimonious model with optimized terrain‐related attributes and satellite‐derived data. SOCS were evaluated at soil subgroup levels. An area of about 808 km2 with varying elevation, plant cover and lithology from the Miandoab region, West Azerbaijan Province, Iran was selected as a case study area. A total of 386 soil samples were collected from 104 profiles comprising various soil genetic horizons. A continuous spline function was then fitted to the target properties in advance of creating a dataset at five standard depth intervals (following the GlobalSoilMap project). These were then grouped into three classes including top (H1), middle (H2, H3 and H4) and bottom (H5) depths to ease interpretation. Static and dynamic covariates (30‐m resolution) were derived from a digital elevation model (DEM) and a suite of Landsat‐8 spectral imageries, respectively. Four candidate models including stepwise multiple linear regression (SMLR), random forest (RF), cubist (CU) and extreme gradient boosting (XGBoost) Tree were tested in this study. Finally, the digital maps at 30‐m resolution of SOC and SOCD and their uncertainties were prepared using the best‐fit model and the bootstrapping method, respectively. Four soil subgroups (Gypsic Haploxerepts, Typic Calcixerepts, Typic Haploxerepts and Xeric Haplocalcids) were identified across the study area. The covariates had variable contributions on the evaluated models. The XGBoost Tree model generally outperformed other models for prediction of SOC and SOCD (R2 = 0.60, on average). Regardless of soil subgroups, the uncertainty analysis showed that the SOCD map had a low prediction interval range value indicating high accuracy. Additionally, the highest SOCS and SOCD was observed at the top followed by middle and bottom depths in the study area. All subgroups exhibited a decreasing trend of SOCD with increasing depth. A similar trend was also observed for SOCS. The highest SOCD (on average) was observed in Gypsic Haploxerepts (4.71 kg C/m2) followed by Typic Calcixerepts (4.46 kg C/m2), Typic Haploxerepts (4.45 kg C/m2) and Xeric Haplocalcids (4.40 kg C/m2). Overall, the SOCS normalized by area within soil order boundaries was greater in Inceptisols than Aridisols across the study area. The findings of this study provide critical information for sustainable management of soil resources in the area for agricultural production and environmental health in the Miandoab region of Iran.

Digital Mapping of Soil Organic Carbon Using UAV Images and Soil Properties in a Thermo-Erosion Gully on the Tibetan Plateau

Thermo-erosion gullies (TGs) are typical thermokarst features in upland permafrost; the soil organic carbon (SOC) of TGs has an important influence on soil quality in cold regions. The objectives of this study were to estimate the spatial distribution of SOC content in a typical TG on the northeastern Tibetan Plateau in China by using soil properties from seven different TGs and covariates from unmanned aerial vehicle (UAV) images, and to characterize the SOC content changes in four representative landscape regions (NO-Slumping, Slumping1, Slumping2, and Slumped) within this typical TG. The support vector machine (SVM) was the optimal machine learning algorithm for SOC content prediction, which explained 53.06% (R2) of the SOC content variation. Silt content was the most influential factor which demonstrated a positive relationship with SOC content in different TGs. In addition, the SOC content in the TGs was related to the landscapes. Severe Slumping (Slumping2: 150.79 g·kg−1) had a lower SOC content than NO-Slumped (163.29 g·kg−1) and the initial slumping stage (Slumping1: 169.08 g·kg−1). The results suggested that SVM was an effective algorithm to obtain a profound understanding of the SOC content over space, while future research needs to pay more attention to the SOC content distribution in the different TGs.

Multivariate random forest for digital soil mapping

In digital soil mapping (DSM), soil maps are usually produced in a univariate manner, that is, each soil map is produced independently and therefore, when multiple soil properties are mapped the underlying dependence structure between these soil properties is ignored. This may lead to inconsistent predictions and simulations. For example, soil organic carbon (SOC) and total nitrogen (TN) maps produced independently may show unrealistic carbon–nitrogen (C:N) ratios. In the last decade the production of soil maps with machine learning models has become increasingly popular as these models are able to capture complex non-linear relationships between soil properties and environmental covariates. However, producing soil maps with multivariate machine learning models is still lacking and requires much investigation in DSM. In this paper we present the combined modelling of multiple soil properties with a multivariate random forest (MRF) model. We applied this model to mapping SOC and TN, and we compared it with results of two separate univariate random forest (RF) models. The comparison was done by means of stochastic simulations determined by sampling from the conditional distributions of the soil properties, given the covariates, as estimated by quantile regression forest. The results show that the MRF model is superior in terms of maintaining the dependence structure between SOC and TN, and consequently, is also able to produce more realistic C:N ratios. The models were also compared on the basis of prediction accuracy using commonly used accuracy metrics such as the root mean square error (RMSE). We found that the accuracy of the MRF model (RMSE-SOC=40.04, RMSE-TN=2.26, RMSE-CN=3.58) is comparable to that of the univariate RF models (RMSE-SOC=39.76, RMSE-TN=2.26, RMSE-CN=3.65). We performed the same comparisons between a regression co-kriging model and two separate regression kriging models, and made similar conclusions.

Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field

Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (R2val = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (R2val = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.

First soil organic carbon report of Paraguay

Assessing soil organic carbon (SOC) stocks at a national scale provides relevant information on soil fertility and soil functions, such as soil water retention capacity, nutrient availability, and soil structure. Our overall goal is to map the SOC stock at the topsoil layer (0–30 cm) in Paraguay, using spatial information of key environmental factors (Environmental Factors - EF; 119 variables) related to SOC and by performing a Regression Kriging (RK) model. We fitted a RK model with 954 sample points, evaluating and computing the model uncertainty of SOC predictions with 10% of independent sample points. Our results show that on average, the SOC stock across Paraguay was 4,46 ± 2,66 kg m−2 where the highest SOC is found in humid regions covered by broad-leaf forests (8.66 × 10−6); while the lowest SOC is found within water-limited ecosystems (1.97 × 10−6). We determined that soil type, climate, and vegetation land cover were the strongest SOC predictors. Since this study represents the first Paraguayan effort to develop a SOC monitoring framework, it also provides the first national SOC map with its associated uncertainty. Enabling a unique opportunity to fulfill relevant information gaps and the extended opportunity to be used to validate global and regional SOC products.

Shapley values reveal the drivers of soil organic carbon stock prediction

Abstract. Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.

Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin

Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R2), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.

Soil fertility monitoring and mapping with a minimal set of data: A case study for rice paddies

Odisha is an agrarian state where 73% population dependent on farming and most of them are small and marginal farmer. Farmers of this state also vulnerable to different natural extreme events like flood, drought and cyclone. For sustainable crop production it is essential to develop a village level the soil fertility index (SFI) map at a higher resolution. A total of 192 gridded soil samples with a resolution of 0.5 km × 0.5 km were collected in Tangi Choudwar. The Soil organic carbon (SOC), available Nitrogen (N), Phosphorous (P), potassium (K), and micronutrient (Fe, Cu, Mn, Zn and B) maps were developed using spatial variability analysis. Then a complete soil fertility index map was developed using the Arc GIS software. In spatial variability analysis SOC and micronutrients (Fe, Cu, Mn, Zn and B) content were higher in NE site of Tangi Choudwar block. The SFI value was range between 0.21 to 0.62 with higher SFI in reported at NE site. Person’s correlation of rice grain yield showed a positively correlation with SFI (0.44, P&lt;0.05). These results showed that soil fertility index can be used in precision agriculture during crop planning and restoration of soil fertility of a region.

Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks

The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping. In this regard, this study mapped SOC distribution across South Africa's major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep neural networks (CAE-DNN). From the different deep neural frameworks tested, the CAE-DNN model (developed from 26 selected covariates) achieved the best accuracy with an RMSE value of 7.91 t/ha (about 20 % of the mean). Results further showed that SOC stock correlated with general biome coverage, as the Grassland and Savanna biomes contributed the most (32.38 % and 31.28 %) to the overall SOC pool in South Africa. However, despite their smaller footprint, Forests (44.12 t/h) and the Indian Ocean Coastal Belt (43.05 t/h) biomes demonstrated the highest SOC sequestration capacity. The restoration of degraded biomes is advocated for, in order to boost SOC storage; but a balance between carbon sequestration capacity, biodiversity health, and the adequate provision of ecosystem services must be maintained. To this end, these findings provide a guideline to facilitate sustainable SOC stock management within South Africa's major biomes and indeed other regions of the world.

Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai–Tibetan Plateau

Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai–Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m × 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R2 of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g·kg−1 in the southeast to 1.47 g·kg−1 in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region.