Mapping Soil Organic Carbon Research Articles

Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors?

Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R2 > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.

Estimating soil organic carbon using UAV and Sentinel-2 images and multilayer perceptron regression in Coastal Wetland

ABSTRACT Hyperspectral imagery from an Unmanned Aerial Vehicle (UAV) has been used to predict soil organic carbon (SOC) content. This study compared UAV hyperspectral imagery with Sentinel-2A multispectral imagery and soil spectra in the laboratory to predict the SOC content in Suaeda salsa (S. salsa) coastal wetland. Characteristic variables were determined through correlation analysis comparing observed SOC with constructed spectral indices of SOC, index variables, and spectral bands. Predict Models of SOC were constructed by multilayer perceptron (MLP), the prediction accuracy was evaluated and the spatial maps of SOC were produced. The results showed that estimating SOC using UAV, Sentinel imagery, and laboratory spectra has great potential when using MLP algorithm. The SOC content can be accurately predicted by the laboratory spectra without spatial map. The accuracy of SOC estimation was better when based on Sentinel-2A imagery than UAV imagery, with the model determination coefficients (R 2) of 0.838 and 0.638, the root mean square error (RMSE) of 0.398 and 0.596 g kg−1, ratio of performance to deviations (RPD) of 2.485 and 1.660, and bias of − 0.028 and 0.085 g kg−1, respectively. The SOC estimation model using Sentinel-2A imagery demonstrated better stability than that using UAV imagery, with the mean uncertainty standard deviations (SD) of 0.226 and 0.291 g kg−1, respectively. The spatial SOC maps generated from both UAV and Sentinel-2A imagery presented more subtle variation information when compared with the spatial interpolation results. Although the prediction accuracy of SOC from UAV was lower than that from Sentinel-2A, UAV imagery could disclose more fine distribution information of SOC at the local level. The UAV imagery can be used to estimate SOC in coastal wetlands but with a higher cost than freely available Sentinel images. We recommend using sentinel imagery to predict SOC at regional, national, or even global levels.

Rapid urbanization through cropland encroachment in the Jiangsu-Zhejiang-Shanghai region of China leads to substantial soil organic carbon loss

Abstract Soil organic carbon (SOC) is a major terrestrial carbon reservoir, crucial for the global carbon cycle and climate change. However, the impact of urbanization-induced cropland encroachment on SOC remains underexplored. This study quantified SOC loss in the top 20 cm (SOC20) and 100 cm (SOC100) soil layers in the Jiangsu-Zhejiang-Shanghai (JZH) region from 1985 to 2019 using high-resolution China Land Cover Dataset and multi-temporal SOC maps. Our results show that the cumulative cropland encroachment area in the study area reached 18,925.65 km2, approximately three times the area of Shanghai. The encroached areas of cropland in Jiangsu, Zhejiang, and Shanghai accounted for 59.72%, 31.49%, and 8.79% of the total, respectively. The cumulative SOC100 loss in the JZH region was approximately 65.31 ± 32.45 Tg C, with the SOC20 loss contributing about 32.97%, emphasizing the importance of deep SOC pool. The cumulative SOC20 (SOC100) losses in Jiangsu, Zhejiang, and Shanghai contributed approximately 55.36% (57.74%), 35.76% (31.96%), and 8.87% (10.3%) to the total SOC loss in the JZH region, respectively. Moreover, the annual average SOC100 loss accounted for about 8.6% to 25.59% of the terrestrial carbon sink flux (11.24 Tg C yr–1) in the JZH region, emphasizing that SOC loss due to cropland encroachment cannot be overlooked when evaluating the regional carbon sink capacity. Additionally, the positive correlation between SOC loss and regional Gross Domestic Product highlights the trade-off between economic development model of urban expansion through cropland encroachment and the resulting substantial SOC loss. This study emphasizes the importance of assessing the impacts of urbanization on regional SOC stocks, especially with regard to deep soil, and provides scientific insights for future urban planning and land management in this region.

A new approach in soil organic carbon estimation using machine learning algorithms: a study in a tropical forest in Vietnam

ABSTRACT Soil organic carbon (SOC) dataset augmentation, which enables the comprehensive monitoring of carbon sinks at regional and global scales, is vital for global carbon cycle management and soil fertility. SOC maps built by conventional laboratory or field measurements are time- and cost-consuming and especially difficult in forests. A new approach to build SOC maps with good accuracy and time efficiency and promptly respond to changes in SOC dynamics is, therefore, being identified. This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. The results indicate that the SVM and XGBoost models demonstrated the best predictive abilities (R2 = 0.70 and 0.74, %RMSE = 8.8 and 8.3, MAE = 0.176 and 0.155) when using remote sensing variables and soil property variables, respectively. Band7_IDM, Band 5, Band4_IDM, RVI, NSMI, NDVI, Band 6, Band 7, and Band 4 were the most valuable variables in the SVM model, while NDVI, DVI, GVMI, NSMI, and Band4_Dive in the XGBoost model. The SVM model using remote sensing data may be applied to build SOC maps in Vietnamese forests instead of using conventional methods with a high accuracy (R2 = 0.74).

Prediction and Mapping of Soil Organic Carbon in the Bosten Lake Oasis Based on Sentinel-2 Data and Environmental Variables

Prediction and Mapping of Soil Organic Carbon in the Bosten Lake Oasis Based on Sentinel-2 Data and Environmental Variables

Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou

Research on soil organic carbon (SOC) is crucial for improving soil carbon sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based on the data from a 2021 land quality survey in Zhengzhou and a multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well as classical machine learning (ML) models, including random forest (RF) and support vector machine (SVM), to map soil organic carbon density (SOCD) in the topsoil layer (0 − 20 cm) of cultivated land. It partitions the sampling data to assess the generalization capability of the machine learning models, with Zhongmu County designated as an independent test set (dataset2) and the remaining data as the training set (dataset1). The three models are trained using dataset1, and the trained machine learning models are directly applied to dataset2 to evaluate and compare their generalization performance. The distribution of SOCD and SOCS in soils of various types and textures is analyzed using the optimal interpolation method. The results indicated that: (1) The average SOC densities predicted by OK interpolation, RF, and SVM are 3.70, 3.74, and 3.63 kg/m2, with test set precisions (R2) of 0.34, 0.60, and 0.81, respectively. (2) ML achieves a significantly higher predictive precision than traditional OK interpolation. The RF model's precision is 0.21 higher than the SVM model and more precise in estimating carbon stock. (3) When applied to the dataset2, the RF model exhibited superior generalization capabilities (R2 = 0.52, MSE = 0.32) over the SVM model (R2 = 0.32, MSE = 0.45). (4) The spatial distribution of surface SOCD in the study area exhibits a decreasing gradient from west to east and from south to north. The total carbon stock in the study area is estimated at approximately 10.76 × 106t. (5) The integration of soil attribute variables, climatic variables, remote sensing data, and machine learning techniques holds significant promise for the high-precision and high-quality mapping of soil organic carbon density (SOCD) in agricultural soils.

Soil carbon in the world’s tidal marshes

Tidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3710 unique locations, landscape-level environmental drivers and a global tidal marsh extent map to produce a global, spatially explicit map of SOC storage in tidal marshes at 30 m resolution. Here we show the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes’ 0–30 and 30–100 cm soil layers are estimated at 83.1 Mg C ha−1 (average predicted error 44.8 Mg C ha−1) and 185.3 Mg C ha−1 (average predicted error 105.7 Mg C ha−1), respectively.

A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale

The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches.

Including soil spatial neighbor information for digital soil mapping

Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon

This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least squares regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths’ spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nová Ves: RMSE = 0.19%, Údrnice: RMSE = 0.12%, Klučov: RMSE = 0.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.

Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions

Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid models and individual models with varying spatial resolutions on SOC prediction at a national scale. In this study, by combining remote sensing data, we utilized the LUCAS 2018 soil dataset to evaluate the potential capacities of hybrid models for predicting SOC content at different spatial resolutions in Germany. The hybrid models PLSRK and RFK consisted of partial least square regression (PLSR) with residual original kriging (OK) models, and random forest (RF) models with residual OK models, respectively. Individual PLSR and RF models were used as reference models. All these models were applied to estimate SOC content at 10 m, 50 m, 100 m, and 200 m spatial resolutions. Sentinel-2 bands, band indices, and topography variables were as predictors. The results revealed that hybrid models had a more accurate prediction of SOC content with higher explanations and lower prediction errors compared with individual models. The RFK model at the spatial resolution of 100 m was the fittest model with R2 = 0.416, RMSE = 0.545, and RPIQ = 1.647, which enhanced 3.74% of explanation compared with the performance of RF model. The results also showed that hybrid models at a relatively coarse resolution (100 m) had better accuracy instead of those at high spatial resolution (10 m, 50 m). Sentinel-2 remote sensing data showed significant predictive capabilities for estimating SOC content. The predicted spatial distribution of SOC content revealed that the high SOC concentrated in the northwest grassland, central and southwestern mountains, and the Alps in Germany. Our study provided a benchmark SOC map in Germany for monitoring the changes resulting from land use and climate impacts, and we illustrated the accuracy of hybrid models and the effects of spatial resolutions on SOC predictions at a national scale.

Application of Ordinary Kriging in Mapping Soil Organic Carbon in Chad using SoilGrids data

Application of Ordinary Kriging in Mapping Soil Organic Carbon in Chad using SoilGrids data

On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping

Soil organic carbon (SOC) mapping delivers invaluable information to the global carbon budget and climate change mitigation endeavour. Environmental variables at sample locations are frequently used as explanatory variables for simulating the spatial distribution of soil properties. However, these may not fully capture the spatial information generated by soil-forming processes. We applied multi-scale landscape metrics that can comprehensively characterize the surrounding landscape information of sample points. The metrics were extracted at two levels (landscape level and class level) and include the diversity, shape and area, fragmentation and connectivity with a buffer distance from 500 m to 5,000 m. We then investigated its effectiveness as environmental variables via recursive feature elimination, random forests, and quantile regression forests. The Jianghan Plain, China, was selected as the study area, where over 19,000 topsoil samples were collected. Results indicated that multi-scale landscape metrics enhanced the predictability of SOC mapping, with R2 increased by 43 %. Specifically, Shannon’s diversity index, the percentage of landscape index, interspersion and juxtaposition index, and patch cohesion index outperformed environmental variables that were extracted at the sample location. In addition, the relationships between SOC and landscape metrics were found to be scale-dependent. Landscape metrics demonstrated significant explanatory capacity for SOC across various spatial scales. Notably, as the scale surpassed 3,000 m, there was a discernible improvement in the explanatory effectiveness of the landscape metrics for SOC. Our findings highlight that landscape metrics are effective in characterising the soil-landscape relationship that is generated by multi-scale natural and anthropogenic soil-forming processes. Meanwhile, knowledge of the intricate relationship between landscape characteristics and SOC is crucial for informing land management decisions aimed at enhancing carbon sequestration, mitigating climate change, and maintaining soil fertility.

Geospatial mapping of soil organic carbon in Sirsi Forst division using remote sensing techniques

Geospatial mapping of soil organic carbon in Sirsi Forst division using remote sensing techniques

Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear; both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone

The quantification of soil organic carbon (SOC) and its vertical distribution is crucial for understanding carbon dynamics in terrestrial ecosystems. This study aimed to 2.5D digital mapping of SOC content in the Trans-Ural steppe zone (Russia) using a quantile regression forest (QRF) approach. The study utilized a dataset comprising 2495 SOC measurements collected from 1316 locations across three soil depths: 0–20 cm, 20–40 cm, and 40–60 cm. Environmental covariates were incorporated into the SOC modeling process, capturing major soil formation factors, and the uncertainty of the generated maps was estimated. The results revealed that SOC content ranged from 0.59 to 9.05 % in the topsoil, from 0.5 to 6.61 % in the subsurface layer and from 0.06 to 4.64 % in the subsoil. Based on the error metrics, including root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency coefficient (NSE), we found a decrease in prediction accuracy with increasing soil depth. Furthermore, climate and vegetation variables, as well as elevation, emerged as key factors influencing the prediction of SOC concentrations at all depths. We also made an attempt to assess the future change of SOC under the influence of climate and anthropogenic impact. We anticipate that climate aridization and plowing will lead to a decline in SOC content in the Trans-Ural steppe region. Our findings contribute to the existing knowledge of SOC dynamics in steppe ecosystems at several depths, supporting informed decision-making for sustainable land use and climate change mitigation strategies.

Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework

Soil organic carbon (SOC) is a critical component that affects soil quality and global carbon cycling. Current SOC mapping approaches are based on the spatial stationarity relationship of SOC and soil formation processes. Nevertheless, the spatial pattern of SOC is the consequence of different soil-forming factors and processes that operate at different scales. In this work, we hypothesized that the covariation of environmental variables and SOC might differ spatially, and proposed a global (whole area) and local analysis framework that aimed to enhance our comprehension of the explanatory scale of environmental variables on SOC variation. This framework primarily incorporates Geographically Weighted correlation and the Multi-scale Geographically Weighted Regression (MGWR) model. With 216 farmland topsoil samples collected from the Qilu Lake watershed in Yunnan Province, China (area of 354 km2), we explored both global and local relationships between environmental variables and SOC to verify the feasibility of this framework. Results showed that the explanatory power of environmental variables on SOC variation is scale-dependent. Our analysis revealed that certain variables, which may explain local variations of SOC, are often overlooked due to their insignificant global correlation with SOC (p > 0.05). For example, in this case study, soil porosity and two landscape metrics that characterize the anthropogenic processes of land use patterns can effectively explain the local spatial variation of SOC. They improved the model performance of MGWR, but their global correlation with SOC is not significant. The proposed framework highlights the necessity of investigating the explanatory power of environmental variables on a global and local scale.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Analyze QRF model for soil organic carbon map building with digital soil mapping (case study: Sumatra island and Java island)

Abstract In the current era of global warming, soil organic content is one of the most important soil properties. The goal of the entire globe is for carbon neutrality to be achieved and regularly assessed. It is hoped that a dynamic, quick and effective soil organic carbon mapping method will be able to distribute the presence of soil organic carbon to support calculations for changes in carbon stocks and carbon sequestration so that carbon neutrality can be achieved. Digital Soil Mapping (DSM) recently has become the ultimate framework for accurately representing spatial distribution based on its quantitative result and uncertainty analysis. These advantages allow DSM to be replicated uniquely in each mapped area. Digital soil mapping requires input in the form of laboratory and field observation results that are spatially modeled using machine learning techniques. Field observations and laboratory data for Sumatra and Java Island from the Indonesian Center for Agricultural Land Resources Standard Testing (1970-2022) were used in this study, and the results were modeled using Quantile Regression Forests (QRF) in the R Software. Evaluation results from this model with 5738 observation points covering a 47.3 million-hectare-sized island of Sumatra and 3398 observation points covering a 12.8 million-hectare-sized island of Java show an RMSE value of 0.78 with a coefficient of determination (R2) of 0.31 for Sumatra Island and RMSE value of 0.68 with a coefficient of determination (R2) of 0.71 for Java Island. These findings indicate that the neighborhoods for the organic carbon content on the islands of Sumatra and Java differ quite noticeably. This may be due to the relatively wide range in some soils in the Sumatra region, which are peat soils with relatively high carbon content values compared to regions in Java where mineral soils predominate. In conclusion, the evaluation results for digital mapping with the QRF model for soil organic carbon content in Indonesia referring to these 2 large islands show good results with sufficient coefficients of determination in mineral soil areas and there is a need a different modeling approach in areas where peat soil predominates.