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Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation. [ABSTRACT FROM AUTHOR]

Studies on soil organic carbon stocks (SOCS) are increasingly relevant to developing efficient mitigation and adaptation strategies for climate change. Reliable information on soil organic carbon (SOC) content, bulk density (BD), and coarse fragments (CF) are required for precise SOCS calculation. The data quality of SOCS‐related variables is important to represent SOCS realistically across different levels of disturbance in the terrestrial ecosystems of Guatemala. The main objective is to develop Guatemala's first national SOC database to support studies of SOCS magnitudes and spatial trends. We identified and collected national sources of variables related to SOCS (SOC, BD, CF) across the country, mainly distributed in the central zone dominated by disturbed ecosystems and agricultural territories. We integrated 910 observations (soil samples and soil profiles) of SOC content (range 1.45–162 g·kg−1), 704 of BD (range 0.42–1.69 g·cm−3), and 8 of CFs (0%–21% weight). This new database represents the edaphic, climatic, and land use variability of Guatemalan territory. The database contains soil observations collected from 1965 to 2010. The year 2010 has the majority of soil observations (41%). SOCS‐related variables are standardized at 0–30 centimetres of depth using mass conservative splines, which are used to represent SOCS and soil depth relationships. We provide new information on SOCS across various ecological and environmental conditions to enable SOC monitoring systems to report reliable and accurate estimates. The new database is appealing for scientific and commercial purposes, such as representing Guatemalan soils in Earth system models or using soil information in the ecosystem services market (e.g., carbon markets). The new database is accessible to all users through the platform of the Environmental Data Initiative https://doi.org/10.6073/pasta/8dd15238c604c3ac75daf985548bd05c. [ABSTRACT FROM AUTHOR]

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency. [ABSTRACT FROM AUTHOR]

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making. [ABSTRACT FROM AUTHOR]

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, "calcl") from the "late winter–spring" time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view. [ABSTRACT FROM AUTHOR]

To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0–20 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties. [ABSTRACT FROM AUTHOR]

The soil organic carbon (SOC) pool is larger than the biotic and atmospheric carbon pools, and understanding cultivated land SOC is critical for evaluating soil fertility and local agricultural management. Digital soil mapping (DSM) efficiently predicts soil properties by utilizing spatially distributed information. To date, many machine learning techniques have been applied in DSM, but model ensemble methods have rarely been used. In this study, two model ensemble methods, including the Granger-Ramanathan averaging and Bayesian model averaging, and three individual mathematical algorithms, such as the random forest, quantile regression forests, and multivariate adaptive regression splines models, were used to estimate SOC at a local level with limited soil samples. The prediction performances of the five methods were compared using an independent validation dataset composed of field observations of soil samples. The results showed that the prediction accuracy of the ensemble methods was higher than that of the individual methods (ΔR2 = 0.02 to 0.21). Compared to the other methods, the Granger-Ramanathan method had a lower root mean square error (RMSE) and mean absolute error and a higher prediction uncertainty. Therefore, the results of this study show the effectiveness of using ensemble methods in mapping SOC with limited soil samples. [ABSTRACT FROM AUTHOR]

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping. [ABSTRACT FROM AUTHOR]

Accurate mapping of soil organic carbon (SOC) and inorganic carbon (SIC) contents at regional scales can be very important for sustainable agriculture and soil management. Low variation in terrain attributes (classically used for digital soil mapping) at low relief areas calls for additional spatial data to explain soil variability. The main objective of this study was to evaluate the predictive capability of Sentinel-1 (radar) and Sentinel-2 (optical) time series and statistics, summarized as multi-temporal features (MTF) to improve the spatial predictions of SOC and SIC in Ghorveh plain, located in Kurdistan Province, Western Iran. A systematic grid sampling was then employed to collect 150 soil surface samples (0–30 cm) for SOC and SIC measurements. We applied boosted regression trees (BRT) and random forest (RF) to predict SOC and SIC contents by using covariate sets compiled from radar and optical time series and topographic attributes. Model performance, evaluated by 10-fold cross-validation, showed that RF using the covariate set containing time series of Sentinel-1, Sentinel-2 and topographic attributes performed the best in predicting SOC (RMSE = 0.23, ME = 0.005, R2 = 0.29). On the other hand, for SIC, the covariate set containing MTF of Sentinel-1, Sentinel-2 and topographic attributes ranked the best with BRT (RMSE = 0.77, ME= −0.001, R2 = 0.48). The study indicates that using the time series and MTF from multiple dates of remote sensing data with topographic attributes results in improved predictions. However, model performance for SIC and SOC was moderate to poor, respectively. Therefore more substantial studies would be required to verify if the computational effort is likely justified by an increase in accuracy in general. [ABSTRACT FROM AUTHOR]

• Novel approach integrating water erosion and tillage erosion in a Digital Soil Mapping based approach for SOC mapping. • As compared to reference sites "plateau", SOC stocks are half on eroded sites and double on depositional sites. • Tillage erosion affects SOC stocks in the top soil whereas water erosion affects SOC stocks on the entire profile. Although agricultural intensification has generally increased crop yields, it also resulted in a range of environmental issues. These include increased erosion rates and declined soil organic carbon (SOC) stocks. In order to improve our understanding on how erosion impacts the overall SOC storage capacity of croplands, this study analyses the 3D distribution of SOC as a function of water and tillage erosion in a conventionally ploughed field in the Belgian silt loam region. We present a novel methodological framework to integrate the output of an advanced erosion model as a co-variate within a Digital Soil Mapping (DSM) approach with the objective to create detailed SOC maps. More precisely, we combined (i) the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM), simulating spatial patterns of soil erosion and sediment deposition due to water and tillage erosions, with (ii) a SOC sampling campaign, resulting in a SOC spatial distribution model that considers both types of erosion. The results show that, as compared to plateaus, SOC stocks are nearly half as large along eroding slopes (i.e. convex slope positions affected by tillage erosion and steep slopes affected by water erosion). Yet, they are up to twice as large in areas characterized by sediment deposition (i.e. concave positions due to tillage erosion and foot slope positions due to water erosion). Our results further show that tillage erosion has a significant influence on the SOC stocks in the top 0.7 m and in particular on the top 0.4 m. The influence of water erosion is less strong but mostly significant along the entire depth profile. Overall, this work demonstrates the relevance of considering different erosion processes when aiming to predict spatial patterns of SOC. Considering the top 1 m and a WaTEM/SEDEM application at a resolution of 10 m our 3D SOC modelling approach obtained a coefficient of determination (R2) of 0.62, a relative root mean square error (RRMSE) of 30.5 % and a relative mean absolute error (RMAE) of 26.0 %. While future work may likely lead to further improvements e.g. a more detailed SOC sampling network along the foot slopes and thalwegs in order to obtain a more spatially detailed prediction of the area characterized by depositions due to water erosion, we demonstrate the great potential of existing erosion and deposition models in doing so. [ABSTRACT FROM AUTHOR]

• The FOD technique refines soil spectral features, improving SOC mapping. • 3D spectral indices correlate more strongly with SOC compared to 2D indices. • Integrating low-order FOD and 3D indices maximizes SOC prediction accuracy. • ZY1-02D hyperspectral satellite demonstrated advanced soil property prediction. Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China's Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data. [ABSTRACT FROM AUTHOR]

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils' spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale. [ABSTRACT FROM AUTHOR]

Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0–10, 10–20, 20–30 and 30–40 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R2: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg−1 in four depths) performed better than Cubist (R2: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg−1) and XGBoost (R2: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg−1) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation. [ABSTRACT FROM AUTHOR]

Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective. [ABSTRACT FROM AUTHOR]

• National soil organic carbon maps for cropland based on soil reflectance composite. • Local ensemble model that accounts for spatial dependencies in predictions. • Local variable importance of the spectral bands is attributed to soil properties. • New approach to reduce spatial autocorrelation and increase model interpretability. • Enhancing digital soil mapping in regions with heterogeneous soil conditions. National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models. [ABSTRACT FROM AUTHOR]

[Display omitted] • We produced a high resolution (30 m) SOC map in cropland of Southern China. • Our produced SOC map has high accuracy with R2 = 0.73 and RMSE = 2.91 g kg−1. • The map accuracy was enhanced by incorporating soil management information. • Soil properties and soil management measures are most relevant to SOC variation in cropland. An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC. [ABSTRACT FROM AUTHOR]

• This study quantified global soil C research trends and the proportional focus on SOC and SIC pools. • Soil inorganic C is an overlooked pool with only 4% share in global soil C research. • Approximately 40% of the soil C research was dedicated to climate change. • Soil C research is concentrated in few countries with 70% research contributed by 9 countries. • It is imperative to expand research focus on SIC to gain a comprehensive understanding of global C cycle. Soils are a major player in the global carbon (C) cycle and climate change by functioning as a sink or a source of atmospheric carbon dioxide (CO 2). The largest terrestrial C reservoir in soils comprises two main pools: organic (SOC) and inorganic C (SIC), each having distinct fates and functions but with a large disparity in global research attention. This study quantified global soil C research trends and the proportional focus on SOC and SIC pools based on a bibliometric analysis and raise the importance of SIC pools fully underrepresented in research, applications, and modeling. Studies on soil C pools started in 1905 and has produced over 47,000 publications (>1.7 million citations). Although the global C stocks down to 2 m depth are nearly the same for SOC and SIC, the research has dominantly examined SOC (>96 % of publications and citations) with a minimal share on SIC (<4%). Approximately 40 % of the soil C research was related to climate change. Despite poor coverage and publications, the climate change-related research impact (citations per document) of SIC studies was higher than that of SOC. Mineral associated organic carbon, machine learning, soil health, and biochar were the recent top trend topics for SOC research (2020–2023), whereas digital soil mapping, soil properties, soil acidification, and calcite were recent top trend topics for SIC. SOC research was contributed by 151 countries compared to 88 for SIC. As assessed by publications, soil C research was mainly concentrated in a few countries, with only 9 countries accounting for 70 % of the research. China and the USA were the major producers (45 %), collaborators (37 %), and funders of soil C research. SIC is a long-lived soil C pool with a turnover rate (leaching and recrystallization) of more than 1000 years in natural ecosystems, but intensive agricultural practices have accelerated SIC losses, making SIC an important player in global C cycle and climate change. The lack of attention and investment towards SIC research could jeopardize the ongoing efforts to mitigate climate change impacts to meet the 1.5–2.0 °C targets under the Paris Climate Agreement of 2015. This bibliographic study calls to expand the research focus on SIC and including SIC fluxes in C budgets and models, without which the representation of the global C cycle is incomplete. [ABSTRACT FROM AUTHOR]

• SOC inversion models were established for various land-use in an arid mining area. • SOC spatiotemporal variability and its effect factors were analyzed. • Changes in vegetation and soil erosion dominate the spatial distribution of SOC. • Coal mining affects plant, terrain and soil erosion, then weakens SOC accumulation. Soil organic carbon (SOC) undergoes rapid changes due to human production activities, which have an impact on the land carbon cycle and ultimately global change. As one of the main human production activities, coal mining significantly impacts the soil carbon cycle. However, due to the lack of remote sensing modeling of soil carbon in mining areas, the spatio-temporal changes and driving mechanisms of SOC in mining areas remain unclear. Therefore, this study investigated and determined SOC data from 300 sampling points (depth of 0–20 cm) located in an arid mining area of China. Remote sensing images were then used to established a soil organic carbon density (SOCD) prediction model within the Random Forest (RF) model to achieve digital mapping of soil organic carbon stocks (SOCS). The spatiotemporal changes of SOCS were analyzed using SOCS digital mapping, and the influencing mechanism of SOCS was revealed using path analysis. The results showed that the constructed SOCD predictive model meets the demand for SOCD prediction (R2 ≥ 0.74, p < 0.01, RMSE ≤ 1.72 kg/m2). Under the combined influence of coal mining and land reclamation, the total amount of surface SOCS in the mining area exhibited a fluctuating upward trend from 1990 (6.34 Tg) to 2021 (7.73 Tg), with an annual growth rate of 0.038 Tg/a. The spatial distribution of SOCS generally increased from southeast to northwest. Precipitation, Normalized Difference Vegetation Index (NDVI), and land use were positively correlated to SOCS spatial distribution, while temperature, elevation, soil erosion, and mining intensity were negatively correlated to SOCS. The impact degree of factors on SOCS was as follows: NDVI > soil erosion > mining intensity > precipitation > elevation > land use > temperature. The negative impact of coal mining on SOCS was mainly indirect, through disturbance to elevation, vegetation, and soil erosion. The uneven ground subsidence and stretching caused by coal mining contribute to intensified soil erosion and vegetation degradation in the affected area, leading to a reduction in SOCS. However, SOCS did not decrease under high intensity mining, which was related to the increase in vegetation and the reduction in soil erosion in the mining area. In this study, a soil carbon prediction model was established based on remote sensing modeling to evaluate the temporal and spatial distribution of soil carbon in an arid mining area. The results can serve as valuable references for the scientific improvement of the ecological environment in mining areas, the rational planning of mining area construction, as well as low-carbon land reclamation and ecological compensation assessments. [ABSTRACT FROM AUTHOR]

• SOC content in multiple soil horizons of the Heihe River Basin was mapped. • EL gave satisfactory estimation result with high accuracy and low uncertainty. • MAP showed the highest contribution to the estimation of SOC content. • SOC content of farmland in the A-horizon was higher than that in the O-horizon. Estimating the spatial distribution of soil organic carbon (SOC) content is essential for evaluating ecosystem functions and establishing global climate change models. Most of the previous SOC mapping studies have focused on estimating SOC content at fixed depths. However, soils at the same depth are not always belonging to the same soil horizon. Therefore, the characteristics of soil horizons should be considered. In this study, the SOC content of multiple soil horizons in the middle and upper reaches of the Heihe River Basin was estimated using multivariate factors and ensemble learning (EL). The results show that EL obtains the best SOC content estimation performance, where the R2 values are 0.58, 0.57 and 0.69 for the O-horizon, A-horizon, and B-horizon, respectively. Environmental factors show a high degree of contribution to estimate SOC content, and mean annual precipitation (MAP) presents the highest contribution amongst all the factors. For the middle and upper reaches of the Heihe River Basin, the SOC content is high in the southwest and low in the northeast. Amongst the multiple soil horizons, the O-horizon usually showed higher SOC content than the A-horizon and the B-horizon. But the SOC content of the A-horizon is higher than that of the O-horizon for farmland. From 2012 to 2013, the SOC content of forestland and farmland increased in the O-horizon and A-horizon. The application of EL improves the mapping performance of SOC content in different soil horizons, and the involvement of multivariable factors reveals the distribution of SOC content in the Heihe River Basin. This study creates maps of SOC content in different soil horizons and analyses the effect of land covers on SOC content, which provides insights for ecological conservation and regional sustainable development. [ABSTRACT FROM AUTHOR]

• DSM of SOC using different remote sensing data was reviewed. • Majority of studies were conducted on mixed land-use types followed by cropland. • Machine learning techniques were used more than (geo)statistical ones. • Terrain variables as predictors had the largest contribution to map SOC. • Data acquired by satellite sensors was employed more than the other types. Soil organic carbon (SOC) has attracted a lot of attention in the soil science community. Freely available remote sensing data combined with advanced digital soil mapping (DSM) techniques has led to a better understanding and management of SOC. This paper has considered the published literature with a focus on digital mapping of SOC using remote sensing data within 2010 to 2023 intervals. The objective was to consider all the important aspects of SOC prediction and mapping, including different land-use types, DSM algorithms, environmental variables, and remote sensing data sources. According to this review conducted on the 217 papers, cropland was the most popular type of land use. Regarding the DSM algorithms, random forest (RF) appeared in the largest number of studies. The terrain and spectral variables derived from the digital elevation model (DEM) and remote sensing images, were the highest demanding among all those used as input predictors. In addition, satellite platforms provided the largest portion of the remote sensing data used for the calibration of DSM models. This review provides quantitative insight into recent trends of SOC digital mapping using remote sensing technology while suggesting some directions for future development of the topic. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important indicator to evaluate agricultural soil quality. Precise mapping SOC can help to facilitate soil and environmental management decisions. This study applied multiple stepwise regression (MSR), boosted regression trees (BRT) model, and boosted regression trees hybrid residuals kriging (BRTRK) to map SOC of agricultural lands in Wafangdian City, northeastern China. A 10-fold cross-validation procedure was used to evaluate the performance of the three models. The BRTRK method exhibited the best predictive performance and explained 78% of the total SOC variability. The distribution of SOC was mainly explained by elevation, followed by soil-adjusted vegetation index (SAVI), and topographic wetness index (TWI). We conclude that the BRTRK was the most accurate method in predicting spatial distribution of SOC. In addition, our study indicated that topographic variables as key factors to affect SOC should be considered in future SOC mapping. [ABSTRACT FROM AUTHOR]

This article reviews the current research and applications of various digital soil mapping (DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks following a systematic mapping approach from 2013 until present (18 February 2019). It is intended that this review of relevant literature will assist prospective researchers by identifying knowledge clusters and gaps in relation to the digital mapping of SOC. Of 120 studies, most were clustered in some specific countries such as China, Australia and the USA. The highest number publications were in 2016 and 2017. Regarding the predictive models, there was a progression from Linear Models towards Machine Learning (ML) techniques, and hybrid models in Regression Kriging (RK) framework performed better than individual models. Multiple Linear Regression (MLR) was the most frequently used method for predicting SOC, although it was outperformed by other ML techniques in most studies. Random Forest (RF) was found to perform better than MLR and other ML techniques in most comparative studies. Other common and competitive techniques were Cubist, Neural Network (NN), Boosted Regression Tree (BRT), Support Vector Machine (SVM) and Geographically Weighted Regression (GWR). Due to the inconsistency in various comparative studies, it would be advisable to calibrate the competitive algorithms using specific experimental datasets. This review also reveals the environmental covariates that have been identified as the most important by RF technique in recent years in regard to digital mapping of SOC, which may assist in selecting optimum sets of environmental covariates for mapping SOC. Covariates representing organism/organic activities were among the most frequent among top five covariates, followed by the variables representing climate and topography. Climate was reported to be influential in determining the variation in SOC level at regional scales, followed by parent materials, topography and land use. However, for mapping at a resolution that represents smaller areas such as a farm- or plot-scale, land use and vegetation indices were stated to be more influential in predicting SOC. Furthermore, unlike a previous review work, all recent studies in this review incorporated validation and 41% of them estimated spatially explicit prediction of uncertainty. Only 9.16% studies performed external validation, whereas most studies used data-splitting and cross-validation techniques which may not be the best options for datasets obtained through non-probability sampling. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a key factor in soil fertility and structure and plays an important role in the global carbon cycle. However, SOC causes a large uncertainty in Earth System Models for predicting future climate change. The GlobalSoilMap (GSM) project aims to provide global digital soil maps of primary functional soil properties at six standard depth intervals (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) with a grid resolution of 90 × 90 m. Currently, few SOC national products that meet the GSM specifications are available. This study describes the three-dimensional spatial modeling of SOC maps according to GSM specifications. We used 5982 soil profiles collected during the Second National Soil Survey of China, along with 16 environmental covariates related to soil formation. The results were obtained by parallel computing over tiles of 100 × 100 km, and the predictions for the tiles were subsequently merged into a single SOC map for the whole of China per standard GSM depth interval. For each standard GSM depth interval, SOC contents and their uncertainties were predicted and mapped at a spatial resolution of approximately 90 m using bootstrapping. Southwestern and northeastern China had higher SOC contents than the rest of China did, whereas northwestern China had a lower SOC content. The range of the coefficient of determination for the six depth intervals ranged from 0.35 to 0.02, and the mean SOC content was 17.86–8.67 g kg−1. Both these values decreased strongly with increasing soil depth. Cropland SOC content was lower than that of forest and grassland. The results of variable importance show that SoilGrids data were the best predictors for defining the soil-landscape relationship during regression modeling for SOC. These SOC maps can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor SOC dynamics, and a guide for the design of future soil surveys. Unlabelled Image • National soil organic carbon maps were produced at six GSM depth intervals. • The XGBoost model had reasonable accuracy, which decreased with depth. • SoilGrids products were helpful in national modeling. • The proposed framework was efficient and flexible for large-scale modeling. • Our results assist policymaking in terms of land management and food production. [ABSTRACT FROM AUTHOR]

• The SOC in two depths were mapped at a resolution of 1 km and five-year interval during 1980–2100. • Three climate scenarios of SSP 1–1.9, SSP2-4.5 and SSP5-8.5 in CMIP6 were considered. • The top 20 cm and 100 cm SOC of China both acted as carbon sinks during 1980–2020. • The SOC will fluctuate significantly in the future under different climate scenarios. • Under the SSP2-4.5 scenario, SOC presented a larger likelihood of being a carbon source in most region. Revealing historical changes in soil organic carbon (SOC) and exploring its future status are important for safeguarding soil health and food security, giving full play to the service function of soil ecosystems, and coping with climate change. However, there is still a gap in our understanding of the SOC stocks in China and their spatial patterns in response to future climate change. Therefore, we attempted to fill this knowledge gap using a large amount of soil observation data, digital soil mapping technology, and global circulation models from Coupled Model Inter-comparison Project phase 6 (CMIP6). In this study, a random forest model was selected to construct the spatial relationship between SOC in the top 0–20 cm (SOC020) and 0–100 cm (SOC0100) of the soil and 21 environmental factors. Spatiotemporal changes in SOC from 1980 to 2100 were revealed at a resolution of 1 km and five-year interval for three climate scenarios in CMIP6. The cross-validation results indicated acceptable predictions for both depths of SOC; however, relatively large prediction uncertainties were observed for SOC0100 in the Tibetan Plateau and northeastern China. The mean values of SOC020 and SOC0100 over the last four decades were 35.77 and 84.62 Tg, respectively, and showed carbon sinks at the national scale, with accumulation rates of 0.05 Pg yr-1 and 0.036 Pg y-1 for the two depths. Compared with historical values (1980–2020), SOC will fluctuate significantly in the future under different climate scenarios. Among them, SOC showed a slow increasing trend at both depths under the SSP1-1.9 low emission scenario, while SOC presented a decreasing trend under the SSP2-4.5 and SSP5-8.5 medium–high emission scenarios. In particular, in most of China under the SSP2-4.5 scenario, SOC0100 presented a larger likelihood of being a carbon source. This study provides a reference for exploring the response of soil carbon pools to climate change and evaluating the effectiveness of land management and ecological protection. [ABSTRACT FROM AUTHOR]

Abstract As an important indicator of soil quality, soil organic matter (SOM) significantly contributes to land productivity and ecosystem health. Accurately mapping SOM at regional scales is of critical importance for sustainable agriculture and soil utilization management and remains a grand challenge. Many studies used soil sampling data and machine learning algorithms to predict SOM at regional scales for a given year, while few studies mapped SOM for multiple years and examined its temporal dynamics. We compared the performance of four machine learning algorithms: decision tree (DT), bagging decision tree (BDT), random forest (RF), and gradient boosting regression trees (GBRT) in mapping SOM in Hubei province, China over the 18-year period from 2000 to 2017. Our results showed that RF and DT had the highest coefficient of determination (R2) (0.61) and the lowest potential bias (9.48 g/kg), respectively, while GBRT had the lowest mean error (ME) (1.26 g/kg), root mean squared error (RMSE) (5.41 g/kg) and Lin's concordance correlation coefficient (LCCC) (0.72). The SOM map based on GBRT better captured the distribution of the soil sample data than that based on RF. The trained GBRT model and the spatially explicitly data on explanatory variables (e.g., climate, terrain, remote sensing) were used to predict SOM for each 500 m × 500 m grid cell in Hubei for the period from 2000 to 2017. Our results showed that the SOM content of cropland was relatively high in the southeast and relatively low in the north. The SOM content in the topsoil varied from 0.89 to 58.86 g/kg and was averaged at 20.52 g/kg. The mean cropland SOM content of the province exhibited an increasing trend from 2000 to 2017 with an increase of 0. 26 g/kg and a growth rate of 1.28%. Spatially, the SOM content increased in southern Hubei and decreased in central and northern parts of the province. A large portion of the areas with decreasing SOM content in northern Hubei was reclaimed cropland, while a large part of the high-quality cropland with rising SOM content in the east (~0.45 × 104 ha) was lost due to land use change (e.g., urbanization). Graphical abstract Unlabelled Image Highlights • GBRT was a better algorithm for spatially predicting and mapping SOM content in Hubei, China than DT, BDT, and RF. • Remote sensing reflectance and vegetation indices were proved to be key factors for predicting SOM content. • The SOM content in the topsoil in 2017 varied from 0.89 to 58.86 g/kg, with a mean value of 20.52 g/kg. • The mean cropland SOM content of Hubei exhibited a slight increasing trend from 2000 to 2017. [ABSTRACT FROM AUTHOR]

Abstract Estimation of soil organic carbon (SOC) in arable land can help with the better understanding of the impact of human activities on soils and the environment. Knowledge of cultivation history can assist with studies on soil carbon balance and CO 2 emissions. This study used historical cultivation data for the last 300 years and quantified the influence on the spatial distribution of SOC in arable land from northeastern China. More than 230 SOC observations were used to train the prediction model derived from boosted regression trees (BRT) where topography, temperature, precipitation, and length of cultivation were employed as SOC predictors. Two BRT models, one with all predictors except length of cultivation (MA) and the other with all predictors (MB) were generated and the models were compared for SOC prediction performance. The MB model accounted for 76% of SOC variability in the study area and showed a better prediction performance compared to the MA model which exhibited lower values for Lin's concordance correlation coefficient (LCCC) and coefficient of determination (R2) and the higher mean absolute prediction error (MAE) and root mean square error (RMSE) indices. The model assigned the highest variable importance to length of cultivation indicating that this variable was key to determining the status of SOC spatial distribution. Our analysis showed that the study area lost about 45% SOC during the past 300 years of cultivation in Northeastern China. The predicted maps from both models suggested a decreasing trend in SOC content from north to south in the study area. We conclude that the historical data in cultivation history were a key variable in SOC predictions on the arable lands and it should be considered as a major variable in future SOC mapping studies, especially in agricultural areas with a long history of cultivation. Highlights • Quantify the role of length of cultivation in SOC distribution in arable lands • Mapping spatial distribution of SOC and comparing the performance of models • Evaluating the potential applications of BRT model [ABSTRACT FROM AUTHOR]

Much of the carbon (C) stored in the soil of the high Qinghai–Tibet Plateau could be lost as a result of global warming. To provide a baseline against which to assess the loss we have made a new map at 90-m resolution from sample data of 1148 soil profiles augmented by information on climate, vegetation, physiography and digital elevation. We used the program Cubist, which works as a form of regression tree, to predict the concentration at the nodes of the 90-m grid. The uncertainty of the predictions was computed by bootstrapping 50 times at each node. Soil type, evapotranspiration (ET), precipitation, radiation and vegetation type contributed most to the variation in C at the coarse scale; temperature, net primary productivity, normalized difference vegetation index (NDVI), ET and elevation contributed most at finer scales. We mapped the predicted concentration of C and converted the predictions to stocks of C for the main kinds of land: 1.93 Pg for the alpine steppe, 1.57 Pg for the meadow, 0.66 Pg in the coniferous forest, 0.63 Pg in the broadleaf forest, 1.06 Pg under shrub, < 0.4 Pg for each of the alpine desert and cropland. We estimate the uppermost 30 cm of soil to contain 6.81 Pg of C with 95% (3.80 to 10.27 Pg). This estimate differs substantially from the two previous coarser estimates based on global modelling which far exceed our 95% upper confidence limit. Our new estimate can now serve as a base against which to judge any change of soil C as a response to global warming. [ABSTRACT FROM AUTHOR]

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. • Our results revealed that the composite mode, radar data utilization strategies and sensor selection influenced outputs. • The mixed information of different orbital directions and polarization modes improved the mapping accuracy. • The predictive performance of Sentinel-1 and Sentinel-2 was comparable. • The accuracy rankings based on satellite observations were as follows: Sentinel-3 > Sentinel-2 > Sentinel-1 > ALOS-2. • The synergistic utilization of the Sentinel series gave better prediction models. [ABSTRACT FROM AUTHOR]

• It was assumed that soils covered by forest can be considered as saturated in SOC. • Cubist-based PTF was developed for predicting and mapping saturated SOC content. • Environmental conditions are also important factors in predicting SOC saturation. • Significant SOC deficit was revealed on large part of Hungary with high variability. • Highest SOC deficits occur in areas with medium to high SOC content. Stakeholders and policymakers have been becoming more and more interested not just in the potential organic carbon (SOC) saturation level of soils but also in spatially explicit information on the degree of SOC deficit, which can support future policy and sustainable management strategies, and carbon sequestration-associated spatial planning. Thus the objective of our study was to develop a cubist-based pedotransfer function (PTF) for predicting and mapping the saturated SOC content of the topsoils (0–30 cm) in Hungary and then compare the resulting map with the actual SOC map to determine and assess the degree of SOC deficit. It was assumed that topsoils covered by permanent forests can be practically considered as saturated in SOC. Using the monitoring points of the Hungarian Soil Information and Monitoring System located in forests as reference soil profiles, we developed a cubist-based PTF. The transparent model structure provided by cubist allowed to show that not just the physicochemical properties of soils (e.g., texture, and pH) but also environmental conditions, such as topography (e.g., slope, altitude, and topographical position) and climate (e.g., long-term mean annual temperature, and evaporation), characterizing landscape are important factors in predicting the level of SOC saturation. Our results also pointed out that there is SOC deficit on large part of the country (∼80%) showing high spatial variability. It was also revealed that the most considerable potential for additional SOC sequestration can be found related to soils with medium to high actual SOC content. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and total nitrogen (TN) influence physical, chemical and biological properties and process in soils that determine soil fertility and enhance or maintain agricultural productivity and food security. Knowledge on the spatial distribution of SOC and TN is necessary for sustainable soil resource management. In this study, we applied boosted regression trees (BRT) model to predict and map the spatial distribution of SOC and TN in the northeastern coastal areas of China, and C:N ratio map was generated from the predicted maps of SOC and TN. A total of 149 topsoil (0–20 cm) samples and 12 environmental variables that included topography, landuse and remote sensing indices, and climatic variables were selected. The performance of the models was evaluated based on cross-validation using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ). The BRT model was run for 100 iterations where the average of 100 SOC and TN maps was considered as final predicted maps and its standard deviation as prediction uncertainty. The BRT model showed a good predictive performance due to its higher R 2 and lower MAE, and RMSE indices. The model explained approximately 64% and 56% of the total SOC and TN variability. Topographic variables showed a maximum influence in the prediction of SOC and TN, followed by vegetation and climate. The SOC and TN contents were reduced from the northwest towards the southeast of the study area; average predicted SOC and TN contents were 15.4 g kg −1 and 1.01 g kg −1 , respectively. The spatial distribution of C:N ratio was closely related to landuse types in a decreasing order: woodland > orchard > cultivated land > grassland. We found topographic variables as main environmental indicators of SOC and TN distribution, and recommend to include them in future SOC and TN mapping studies in the coastal agroecosystems in China. [ABSTRACT FROM AUTHOR]

Estimation of carbon and nitrogen stocks is important for quantifying carbon and nitrogen sequestration as well as greenhouse gas emissions and inventorying national carbon and nitrogen balances. For Liaoning province of China, we estimated the vertical distribution of soil organic carbon (SOC), soil total nitrogen (STN), bulk density (BD), and mapped their spatial distribution at five standard soil depth intervals (0–5, 5–15, 15–30, 30–60 and 60–100 cm) using nine environmental variables as predictors including precipitation, temperature, land use, elevation, system for automated geoscientific analyses (SAGA) wetness index, and Normalized Difference Vegetation Index (NDVI). The highest average contents of SOC and STN were 15.2 g kg − 1 and 1.6 g kg − 1 in the 0–5 cm soil layer, and 1.5 g kg − 1 SOC and 0.4 g kg − 1 STN in the 60–100 cm soil layer, respectively. The prediction precision for SOC, STN and BD all decreased with soil depth. Average SOC and STN stocks for 0–30 cm were 3.1 kg m − 2 and 0.5 kg m − 2 , respectively. For the top 1 m, SOC and STN were 4.5 kg m − 2 and 0.9 kg m − 2 , respectively. In total, the soils stored approximately 588 Tg SOC and 128 Tg STN within the top 1 m. The soils under forest had the highest amount of carbon (356 Tg) and nitrogen (58 Tg) followed by agriculture and wetland that contributed 34% and 48% of the total stock, respectively. > 91% of the total SOC and STN stocks were in Argosols and Cambosols. We adopted a digital soil mapping method to map the spatial distribution of SOC and STN stocks and predict their uncertainties. The estimation was validated with a 10-fold cross-validation procedure. The data and high-resolution maps from this study can be used for future soil carbon and nitrogen assessment and inventorying. [ABSTRACT FROM AUTHOR]

There is an increasing concern on how human activities could affect the ecosystems of the Galapagos Islands, a hot-spot for biodiversity recognized worldwide. Despite the high number of studies related to the ecological relationships between species in these islands, almost no research has been conducted on the description of their soils, a basic component of the ecosystems of Galapagos. In 1962, Belgian researchers from Ghent University organized a geo-pedological mission in order to collect detailed information on soil diversity on the Santa Cruz Island. Here we use this legacy data in a Digital Soil Mapping framework in order to quantify the spatial distribution of Soil Organic Carbon (SOC) stocks in this area. Our results indicate that the soils of Santa Cruz store about 706 Gg SOC in the upper 10 cm. SOC accumulation is mainly driven by climatic factors, which are highly influenced by both altitude and the direction of predominant winds. An increase in the amount of rainfall, as predicted by climate change scenarios, will result in an overall increase of the SOC stocks and likely modify the vegetation species composition within the different bioclimatic strata of the islands. A SOC monitoring program, based on spectroscopic analyses could be used to determine temporal variations in SOC stocks in a quick and cost-effective manner while minimizing human disturbances in this area. [ABSTRACT FROM AUTHOR]

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. • Including agricultural management practices factors improved cropland SOC content mapping. • Carbon input was the dominant SOC predictor in the intensively cultivated area of the Tuojiang River Basin. • The necessity of considering cropland management practices alongside environmental predictors for cropland SOC mapping. [ABSTRACT FROM AUTHOR]

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens current mitigation approaches and can jeopardise carbon stock assimilation. To effectively manage and protect SOC stocks, large-scale projections that accurately model both current and future SOC pools are necessary. Hence, this study modelled the effects of simulated climate and land-cover change on SOC inventories across South Africa up to the year 2050. A digital soil mapping strategy in concert with a deep neural network (DNN) was used to model current SOC stocks distribution. Subsequently, WorldClim general circulation models and a space-for-time substitution (SFTS) method were used to derive future SOC stocks under four shared socio-economic emission pathways. Results show a relatively high accuracy with RMSE of 7.44 t/h for current stocks, while future stocks ranged from 11.37 to 13.56 t/h. Depending on emission rates, results showed a reduction in SOC inventories, with overall SOC stocks declining from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. Meanwhile, forests, which account for approximately 1.2 Pg of total SOC in South Africa, were found to have lost more than 1% of their total coverage by 2050. These findings provide a glimpse into the state of South Africa's current and future SOC stock inventories and the influence of climate and land-use change. These findings are valuable to among others policymakers, land use managers and climate change experts in assessing the long-term feasibility of South Africa's existing SOC management protocols and land-use planning agenda. However, to adequately protect future SOC stocks, current land-use planning frameworks need to be re-adjusted to prioritize pressing environmental concerns. • First national scale projection (2050) of SOC stocks in South Africa. • South Africa SOC stocks inventory declined from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. • The extent of SOC decrease varies across different shared socio-economic emission pathways. • SOC stocks are proportional to land cover size, but forests have the highest sequestration rate. [ABSTRACT FROM AUTHOR]

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. • Multivariate RF accounts for the correlation structure between soil properties. • Simulations produced with multivariate RF are superior compared to those of RF. • In digital soil assessment it is vital to account for correlations in soil properties. • Multivariate RF C:N ratio simulations outperformed those of regression co-kriging. • Prediction accuracy of multivariate RF is comparable with that of RF. [ABSTRACT FROM AUTHOR]

• Estimation of nutrient transport from both water and wind erosion. • Assessment of spatial and temporal variations of nutrient transport. • Impact analysis of land uses and management on nutrient transport. • Valuation of economic loss of soil nutrient transport from erosion. Nutrient transported from soils to water bodies not only threatens agricultural productivity and food security but also causes the degradation of water quality and the environment in many parts of the world. However, nutrient transport through soil erosion is often ignored in nutrient cycle studies; there is little understanding of how much nutrient is lost through water and wind erosion. In this study, we attempted to assess soil nutrient transport due to both water and wind erosion and its spatial and temporal variability across New South Wales (NSW), Australia. We estimated the mass fraction (%) of total nitrogen (N), total phosphorus (P) and soil organic carbon (SOC) in the eroded soil, and the total nutrient stock of the soil layer down to 200 cm using water and wind erosion models and digital soil mapping. The estimated average N, P and SOC stocks in NSW topsoils (0–5 cm) are 160, 43, and 2970 kg ha−1 respectively. There are great variations in the transport of nutrients by erosion in space and time, ranging from near zero in the Western region to 395 kg ha−1 yr−1 in the North Coast region. The average total nutrient transport rate is about 2.4 % of the surface soil (0–5 cm) total stock in NSW due to both water and wind erosion. The total cost of nutrient transport is estimated at 4.2 billion Australian dollars for the entire state of NSW and 0.2 billion dollars from the cropping lands per year. The areas with the highest nutrient transport rates are the North Coast and Hunter regions due to the relatively high water erosion and nutrient content of the soils. On average, water erosion contributes up to 98 % of the total nutrient transport across the state as a whole, but wind erosion can contribute up to 12 % of the total transport in Spring (i.e., September). This study is the first attempt to investigate nutrient transport from both water and wind erosion in Australia. The methodology and findings contribute to the knowledge of nutrient transport due to erosion in broad nutrient cycle studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Predicting SOC and SIC from two sampling periods (1990 vs 2015); • Quantifying the impact of environmental variables on SOC and SIC changes; • Mapping soil carbon stocks change during 25 years (1990–2015). Soil organic carbon (SOC) and soil inorganic carbon (SIC) has important effects on soil physical, chemical and biological properties. They play an important role in coordinating the relationship between soil water and air, increasing soil water holding capacity and improving plant productivity. In this study, a boosted regression trees (BRT) model was developed to map the spatial distribution carbon stocks in the semi-arid region of Northeast China in 1990 and 2015. During the two periods, 10-fold cross-validation technology was used to test the performance and uncertainty of BRT model. In order to construct the model, 9 environmental variables (derived from climate, topography and biology) and 173 (1990) and 223 (2015) topsoil (0–30 cm) samples were used. The comparison between estimated and observed data shows that the RMSE of SOC and SIC stocks were 0.53 kgm−2 and 0.19 kgm−2 in 1990, and 0.65 kgm−2 and 0.20 kgm−2 in 2015, respectively. Elevation, normalized difference vegetation index, mean annual precipitation and Landsat band 3 were identifies as critical environmental factors for simulating the spatial distribution of SOC, accounting for 76.6 % and 70.3 % of the total relative importance in 1990 and 2015, respectively. Mean annual precipitation, mean annual temperature and topographic wetness index were the critical environmental factors for simulating the spatial variation of SIC during the two periods. Land use change also played an important role in the spatial variability of SOC and SIC stocks. In the past 25 years, soil carbon stocks decreased from 6.2 kg m−2 in 1990 to 5.9 kg m−2 in 2015. The spatial distribution pattern of SOC was high in northeastern area and low in southwestern area during the two periods, while the spatial distribution pattern of SIC was opposite to that of SOC stocks. The mapped soil carbon stock distribution is fundamental to future study of soil carbon cycle and regional carbon balance in semi-arid regions. [ABSTRACT FROM AUTHOR]

This study focus on modeling and mapping soil organic carbon (SOC) at high spatial resolution and at four standard depths in an arid and semi-arid region of Iran. The SOC data includes 850 soil samples collected from 278 observation profiles. In parallel, a wide range of environmental covariates (n = 62) were obtained from multiple sources. Six individual machine learning (ML) algorithms were compared to modeling and predicting SOC. Two scenarios were investigated. The first one accounts for soil and environmental covariates (S1) while the second one only accounts for environmental covariates (S2). Our results show that accounting for soil variables in the prediction (S1) leads to a twofold increase of R 2 for all ML algorithms, while random forest (RF) outperformed the other ML approaches at all depths. Whenever possible, using additionally the soil variables that are at hand in a study area is thus beneficial for improving SOC predictions. • Machine learning algorithms were used for mapping SOC content at four soil depths in an arid and semi-arid region of Iran. • Among the six machine learning algorithms that were compared, Random Forest was identified as the best algorithm at all soil depths. • A twofold performance increase was obtained when using measurements of soil properties (BD, Clay, and CCE) in addition to environmental covariates from DEM, remote sensing and climate data. • Hybridization based on residual kriging led to a minor performance increase for our case study. [ABSTRACT FROM AUTHOR]

This study aimed to quantify SOC stocks and potential C sequestration for Nigeria using legacy soil data. Mass preserving splines were fitted to legacy SOC and bulk density (BD) pedon data based on GlobalSoilmap soil depths. SOC concentrations (g kg −1 ) were predicted using Random Forest model (RFM), Cubist and Boosted regression tree (BRT). Thereafter, the soil carbon density (Mg C ha − 1 ) was calculated from the SOC concentration and BD (Mg m − 3 ). The information was combined with land use/land cover (LULC) map and agro-ecological zone (AEZ) digital maps to estimate SOC sequestration. The mean SOC concentration ranged between 4.2 and 23.7 g kg − 1 in the top 30 cm and between 2.6 and 9.2 g kg − 1 at the lower soil depth. Total SOC stock in the top 1 m was 6.5 Pg with an average density of 71.60 Mg C ha − 1 . Almost half of the SOC stock was found in the 0–30 cm layer. SOC stocks decreased from the southwest to the northeast of Nigeria, and increased from Sahel to Humid forest agro-ecological zones. Restoration of the various land use types has the potential to sequester about 0.2 to 30.8 Mg C ha − 1 depending on the AEZ. The Derived Guinea Savannah presents a potential hotspot for targeted carbon sequestration projects in Nigeria. Knowledge of SOC stock and sequestration is vital for framing appropriate management regimes to increase soil carbon stocks, for C accounting and environmental monitoring purposes. [ABSTRACT FROM AUTHOR]

This work presents the first high-resolution map of soil organic carbon (SOC) in mainland France, including soils below 30 cm. The research was performed within the framework of GlobalSoilMap (GSM). SOC predictions for different depth layers (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and > 100 cm) were made at 90 and 500 m resolution for mainland France, along with their upper and lower confidence intervals. The maps were developed using data mining and an elaborate cross-validation scheme. The 90 m maps were compared to 500 m resolution GlobalSoilMap maps and the SoilGrids1km (SG1km) product. The latter is a global model for predicting soil properties for the same depth layers, at 1 km resolution. At 90 and 500 m resolution, the predicted SOC content was unbiased and showed good agreement with the measured SOC, despite the poor model diagnostics and decrease of performance with depth. It was found that the subsoil (> 30 cm) carbon pool for France contributes 49% to the total soil carbon stock. The use of coarser resolution prediction grids resulted in smoother spatial patterns and wider confidence intervals; however, it did not bias the estimated carbon stocks. Applying GlobalSoilMap specifications to France, using a large soil dataset and all the exhaustive spatially available data outperformed SG1km predictions. The overall spatial patterns of the SG1km SOC content were found to be very similar to the GlobalSoilMap maps. However, the SG1km overestimated the SOC content and carbon stocks (> 75% for the total carbon stock, and 100% for the stocks below 30 cm) and showed a similar spatial distribution over the different soil depth layers. The main reason for the overestimation was that the local data used in SG1km was rather small (56 samples) and not representative in terms of SOC content or represented soil types; the profiles had far higher SOC content and this may have propagated in the modelled vertical profile and the kriging part of the residuals. Improvements for SG1km may entail the use of a representative national subsample from large national soil databases. Furthermore, a bottom-up approach such as GlobalSoilMap may be more favourable when considering prediction accuracies, data privacy policies and local acceptance of generated products. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg −1 and 26.6 g kg −1 , respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments. [ABSTRACT FROM AUTHOR]

Peatlands store large amounts of soil organic carbon (SOC). Depending on their present condition, they act as a source or sink of carbon dioxide. Therefore, peatlands are highly relevant for climate change investigations and there is considerable interest to assess spatial heterogeneity of peat soil properties in order to assess the total amount of stored carbon. However, reliable information about peat properties remains difficult to obtain at the field scale. A potential way to acquire this information is the indirect mapping of easily recordable physical variables that correlate with peat properties, such as the apparent electrical conductivity ( EC a ). In this study, we aim to explore the potential of multi-coil offset electromagnetic induction (EMI) measurements to provide spatial estimates of SOC content, bulk density, and SOC stock for a highly variable and disturbed peatland relict (~ 35 ha) with a remaining peat layer thickness of less than 1 m. EMI measurements comprised six integral depths that varied from 0–0.25 to 0–1.80 m. In combination with ancillary laser-scanning elevation data, a multiple linear regression model was calibrated to reference data from 34 soil cores that were used to calculate integral properties of the upper 0.25, 0.5, and 1 m layer, as well as for the total peat layer. Leave-one-out cross-validation for the different depth ranges resulted in a root mean square error of prediction (RMSEP) between 1.36 and 5.16% for SOC content, between 0.108 and 0.183 g cm − 3 for bulk density, and between 3.56 and 9.73 kg m − 2 for SOC stocks, which corresponds to roughly 15%, 10%, and 20% of the total field variability, respectively. The selection of explanatory variables in the regression models showed that the EMI data were important for accurate model predictions, while the topography-based variables mainly acted as noise suppressors. The accuracy of the SOC content estimates roughly equalled the quality of SOC content predictions obtained in previous field applications of the visible-near infrared technique (vis-NIR). The spatial variation of the predicted peat layer properties showed similarities to the former land use distribution. Overall, it was concluded that EMI measurements offer a useful alternative to the established vis-NIR method for SOC content mapping in carbon-rich soils. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha −1 had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha −1 had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R 2 values (14.9 Mg C ha −1 and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R 2 values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts. [ABSTRACT FROM AUTHOR]