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[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]

• 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]

• 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]

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]

[Display omitted] • Quantifying the potential human factors on SOC content in topsoil of agroecosystems. • Mapping the spatial distribution of SOC content in agroecosystems of Northeast China. • Evaluating the predictive performance and application potential of the predictive model. Both natural and anthropogenic variables affect soil C distribution and its pool, however studies about anthropogenic influence on soil C distribution are very limited in the literature. This study investigated anthropogenic effects on soil organic carbon (SOC) changes in the cultivated lands of Northeast China. A total of 196 topsoil samples (0–30 cm) were collected, and analyzed for SOC content, and 12 environmental variables (natural and anthropogenic) were selected as SOC predictors. Natural factors included elevation, slope gradient, slope aspect (SA), topographic wetness index (TWI), mean annual temperature, mean annual precipitation, and normalized difference vegetation index, while population (POP), gross domestic product (GDP), distance to the socioeconomic center, distance to roads, and reclamation period (PER) represented anthropogenic variables. Three different boosted-regression trees models with different combination of SOC predictors were constructed, and the model performance was evaluated with 10-fold cross-validation. We found that the model that included all predictors had the best performance, followed by the model with topography and climate variables, and the model with only anthropogenic variables. However, adding the anthropogenic variables in the model greatly improved its performance. Results showed that PER, POP and GDP were the key environmental variables affecting SOC content in the topsoil agroecosystems in Northeast China. This study suggests that anthropogenic variables should be selected as the main environmental variable in predicting of SOC content in agroecosystem with a higher human influence. We believe that the accurate prediction and mapping of SOC content in the topsoil agroecosystem will help formulate farmland soil management policies and promote soil carbon sequestration. [ABSTRACT FROM AUTHOR]

[Display omitted] • A progressive simulation method was proposed to further improve the accuracy of estuarine DSM. • The method adequately utilized the close correlations among soil parameters in estuarine areas. • The progressive simulation increased accuracies for SOC and TN by 54.4% and 57.2%, respectively. • Island exploitations and reclamation activities determined the spatial pattern of SOC and TN stocks. • The method was proven to be applicable in estuarine DSM and showed potential for 3D and 4D mapping. Simulating the soil organic carbon (SOC) and total nitrogen (TN) stocks in estuarine areas is important for the development of digital soil mapping and the regional inventory of carbon and nitrogen, and simulation accuracy is the key issue. In this study, a progressive simulation method was proposed to further improve accuracy through the adequate utilization of the correlations among soil parameters and their relations with the comprehensive land surface factor system. Three soil parameters, namely, SOC, TN, and bulk density (BD), were simulated using the method for estimating SOC and TN stocks. The simulations were progressively conducted in three levels (Levels 1–3) by using partial least squares regression (PLSR) and cokriging. Each of the parameters was simulated separately in Level 1, one parameter was simulated based on each of the two other parameters in Level 2, and one parameter was simulated based on the two other parameters in Level 3. A typical estuarine archipelago in southern China was selected to demonstrate the performance of the method in surface soil (0–20 cm). Results showed the effectiveness of the progressive simulation in improving accuracy when the PLSR was used. The simulation results in Level 3 had the highest accuracy among the results in all levels, increasing the accuracies for SOC, TN, and BD by 54.35%, 57.24%, and 5.61%, respectively, compared with those in Level 1 and achieving high accuracies compared with those in similar studies. The SOC and TN stocks amounted to 270.66 and 26.96 Gg, respectively, and the mean values of SOC and TN densities were 2.65 and 0.26 kg/m2, respectively, which are lower than global values and those for China. The SOC and TN densities exhibited distinct spatial heterogeneities, which were fundamentally driven by different types of island exploitations and large-scale sea reclamation activities. [ABSTRACT FROM AUTHOR]

[Display omitted] • A progressive simulation method was proposed to further improve the accuracy of estuarine DSM. • The method adequately utilized the close correlations among soil parameters in estuarine areas. • The progressive simulation increased accuracies for SOC and TN by 54.4% and 57.2%, respectively. • Island exploitations and reclamation activities determined the spatial pattern of SOC and TN stocks. • The method was proven to be applicable in estuarine DSM and showed potential for 3D and 4D mapping. Simulating the soil organic carbon (SOC) and total nitrogen (TN) stocks in estuarine areas is important for the development of digital soil mapping and the regional inventory of carbon and nitrogen, and simulation accuracy is the key issue. In this study, a progressive simulation method was proposed to further improve accuracy through the adequate utilization of the correlations among soil parameters and their relations with the comprehensive land surface factor system. Three soil parameters, namely, SOC, TN, and bulk density (BD), were simulated using the method for estimating SOC and TN stocks. The simulations were progressively conducted in three levels (Levels 1–3) by using partial least squares regression (PLSR) and cokriging. Each of the parameters was simulated separately in Level 1, one parameter was simulated based on each of the two other parameters in Level 2, and one parameter was simulated based on the two other parameters in Level 3. A typical estuarine archipelago in southern China was selected to demonstrate the performance of the method in surface soil (0–20 cm). Results showed the effectiveness of the progressive simulation in improving accuracy when the PLSR was used. The simulation results in Level 3 had the highest accuracy among the results in all levels, increasing the accuracies for SOC, TN, and BD by 54.35%, 57.24%, and 5.61%, respectively, compared with those in Level 1 and achieving high accuracies compared with those in similar studies. The SOC and TN stocks amounted to 270.66 and 26.96 Gg, respectively, and the mean values of SOC and TN densities were 2.65 and 0.26 kg/m2, respectively, which are lower than global values and those for China. The SOC and TN densities exhibited distinct spatial heterogeneities, which were fundamentally driven by different types of island exploitations and large-scale sea reclamation activities. [ABSTRACT FROM AUTHOR]

[Display omitted] • The potential of several environmental covariates was assessed in the digital soil mapping of SOC. • Most commonly used and time-series covariates were used for the prediction of SOC. • Soil properties and time-series NDVI explained most of SOC variability. • Remote sensing time-series did not increase the modeling accuracy but decrease the mapping uncertainty. • The Cubist model with the combined datasets (MCC + RST) produced an accurate SOC map. In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content. [ABSTRACT FROM AUTHOR]

• Near infrared band (sentinel-2) and SATVI are of great importance in predicting SOC. • Adding phenological parameters improved SOC prediction accuracy. • Crop phenological parameters were generated based on sentinel-2 images. • Contrast the effects of moisture index, bright-related index and vegetation index in SOC mapping. It is important to predict the spatial distribution of SOC accurately for migrating carbon emission and sustainable soil management. Environmental variables influence the accuracy of SOC prediction with digital soil mapping (DSM) approaches. In addition to the commonly-used natural predictors, remote sensing variables have been recently used in DSM. However, it is still challenging which variables are effective to predict SOC in farmland. Although phenological parameters have been recently used to indicate human activities that affect SOC in farmland, there are few studies that employ the phenological parameters in SOC prediction. Therefore, this study investigates the feasibility of SOC prediction with the phenological parameters and numerous remote sensing variables extracted from Sentinel-2 at high temporal and spatial resolutions. From 34 Sentinel-2 time series images from 2018 to 2019, 17 phenological parameters were extracted for Xuanzhou, Anhui Province using a dynamic threshold method. Furthermore, fifteen remote sensing predictors comprised of vegetation indices, bright-related indices, and moisture indices were generated from the Sentinel-2 images. The phenological parameters and remote sensing variables were combined with natural variables to predict SOC contents at the surface soil layer using random forest. The results showed that the auxiliary parameters, i.e., the phenological parameters and remote sensing predictors, enhanced the predictability of SOC with an increase in R2 by 171% and a decrease in RMSE by 14%. This study also identified relatively more important auxiliary parameters for the SOC prediction: the largest data value for the fitted function during the season (a6), rate of increase at the beginning of the season (a8), large seasonal integral (a10), SATVI, and Band8. Therefore, this study verified that the phenological parameters and remote sensing predictors extracted from the Sentinel-2 EVI time series are effective for DSM in farmland. [ABSTRACT FROM AUTHOR]

• Topsoil SOC (0–20 cm) was mapped in an alluvial plain region at 30 m resolution. • Random Forests performed better than the Stepwise MLR-Kriging. • Silt content and river proximity were most important predictors of SOC. • Uncertainty was quantified using Quantile Regression Forests. • Highest SOC content was found in Calcaric Phaeozems under shrubs and forest. Topsoil (0–20 cm) soil organic carbon content (SOC, g kg−1) was predicted and mapped at 30 m resolution on an intensively cultivated alluvial plain in the Sarlahi district of Nepal. It has been reported that SOC content in this region has decreased to alarmingly low levels; however, no documented work on the mapping of SOC at high spatial resolutions has been found. We compared the performance of the Stepwise-Multiple-Linear-Regression-Kriging (SMLRK) and Random Forest (RF) techniques for mapping SOC content. Environmental covariates of SOC were selected following the SCORPAN framework of digital soil mapping. Prediction uncertainty was quantified using Quantile Regression Forest technique. Predicted SOC was further calculated for different soil and land cover unit combinations. It was found that RF performed better than SMLRK as evidenced by more favourable error statistics. Silt content, distance to major river systems, precipitation during coldest quarter of the year, and NDVI for January-February were found to be the most important variables affecting SOC content in this region. Mean SOC of the study area was predicted as 10.98 ± 0.01 g kg−1. Calcaric Phaeozems under Shrubs (14.85 ± 0.09 g kg−1) and forests (14.54 ± 0.02 g kg−1) had the highest mean SOC contents. Cultivated lands within irrigation command areas had less SOC content (9.41 ± 0.01 g kg−1) than those outside the areas (11.84 ± 0.01 g kg−1). The SOC spatial distribution map could guide efforts to prioritize SOC content enhancement activities in this area. It was concluded that in such low-relief alluvial regions, the processes of silt deposition due to the proximity to river systems are key predictors than other variables, and that the RF technique could predict SOC content better than SMLRK. The success of activities to raise SOC contents might thus be subject to the control of silt deposition from the upstream hills. [ABSTRACT FROM AUTHOR]

• Production of a digital map of topsoil organic carbon. • Individual and hybrid machine learning models were compared. • The most appropriate covariates were selected by PCA. • The geomorphometric and remote sensing (GR) scenario was better than the remote sensing (R). • The hybrid model (RF-OK) predicted the most accurate of SOC map. Digital mapping of soil organic carbon (SOC) is crucial to evaluate its spatial variability and also to assess environmental factors controlling it at field scale. The current study was conducted to compare one statistical method include partial least squares regression (PLSR), four individual machine learning(ML) algorithms including random forest (RF), quantile regression forest (QRF), cubist (CB), fuzzy logic (SoLIM) along with two hybrid methods including the random forest-ordinary kriging (RF-OK) and quantile random regression forest-ordinary kriging (QRF-OK) to map SOC. A number of 146 soil samples (0–30 cm) were collected in Khorramabad plain (680 ha). For the quantitative evaluation of SOC spatial variability, two scenarios were considered to use remote sensing data (R) and a combination of geo-morphometric and remote sensing data (GR). The data randomly were split into 80% (117 points) for training and 20% (29 points) for validation. The model performances were evaluated by the statistical indices as the coefficient of determination (R2), root mean square error (RMSE). According to principal component analysis (PCA), nine covariates including transformed soil adjusted vegetation index (TSAVI), relative vegetation index (RVI), Band 10, Band 11,Digital Elevation Model (DEM), Standard height(Standard_he), Valley_Dep, terrain surface texture (texture), and terrain surface convexity (Convexity) were selected as the environmental predictors. Results showed that the hybrid model RF-OK (RMSE = 0.05and R2 = 0.93) and SoLIM model (RMSE = 0.47and R2 = 0.41) with scenario GR had the highest and lowest accurate respectively. TSAVI, DEM, and Band 10 were the most important predictors and explanation more than 50% of SOC spatial variability in the study area. Generally, using hybrid machine learning models in combination with geo-morphometric and remote sensing covariates make it possible to model and predict SOC with acceptable accurate in the field-scale croplands. [ABSTRACT FROM AUTHOR]