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Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing soil quality and achieving precision agriculture. The cropping system is an important factor that affects the soil carbon cycle in farmlands, and different agricultural managements under different cropping systems lead to spatial heterogeneity of SOC. However, current research often ignores differences in the main controlling factors of SOC under different cropping systems, especially when the cropping pattern is complex, which is not conducive to farmland zoning management. This study aims to (i) obtain the spatial distribution map of six cropping systems by using multi-phase HJ-CCD satellite images; (ii) explore the stratified heterogeneous relationship between SOC and environmental variables under different cropping systems by using the Cubist model; and (iii) predict the spatial map of SOC. The Xiantao, Tianmen, and Qianjiang cities, which are the core agricultural areas of the Jianghan Plain, were selected as the study area. Results showed that the SOC content in rice–wheat rotation was the highest among the six cropping systems. The Cubist model outperformed random forest, ordinary kriging, and multiple linear regression in SOC mapping. The results of the Cubist model showed that cropping system, climate, soil attributes, and vegetation index were important influencing factors of SOC in farmlands. The main controlling factors of SOC under different cropping systems were different. Specifically, summer crop types had a greater influence on spatial variations in SOC than winter crops. Paddy–upland rotation was more affected by river distance and NDVI, while upland–upland rotation was more affected by irrigation-related factors. This work highlights the differentiated main controlling factors of SOC under different cropping systems and provides data support for farmland zoning management. The Cubist model can improve the prediction accuracy of SOC under complex cropping systems. [ABSTRACT FROM AUTHOR]

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) 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]

Mapping soil organic carbon (SOC) distributions in coastal wetlands plays an important role in assessing ecosystem services and investigating the global carbon cycle. Little research has explored the effects of environmental variables with seasonal variations on digital soil mapping (DSM). Our research utilized machine learning methods and established multiple prediction models of SOC based on multi‐temporal data from dry and wet seasons, and mono‐temporal data from April. The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in the wet season showed stronger correlations and higher importance scores with SOC compared with those in the dry season. In addition, SOC prediction models in wet season and April had stronger prediction performance compared with those in the dry season. As a result, data fusion of multi‐temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil‐landscape attributes and spectral characteristics in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models in wet season than those in dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil‐landscape relationships. In general, the findings of this research demonstrated that seasonal variation of environmental variables should be considered in the establishment of a DSM model in coastal wetland. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia's soils. The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting, back-transformed ilr components were mapped across Australia. SOC fraction stocks for the 0-30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59% ±17.5%, whereas 28% ± 17.5% was PyOC and 13% ± 11.1% was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0-30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change. [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]

• 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]

• 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]

[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]

• Distances and directions from volcanos are effective covariates for SOC mapping. • 3D mapping with 10-m resolution can reproduce SOC along with microtopography. • SOC may accumulate more on ridges, highland slopes, and landslide masses. Geographic information on soil organic carbon (SOC) is important for climate change research and understanding the global carbon cycle. At present, this information is limited for forest soils in mountainous regions that have complex topographies and are affected by tephra deposition. This study predicted the SOC concentration (OC), bulk density (BD), apparent rock coarse fraction (CF), and SOC stock (OCS) at 0–5, 5–15, and 15–30 cm in the soil horizon for the forested areas (ca. 230 000 km2) of Japan. This was carried out with a 10 m grid resolution using a digital soil mapping (DSM) approach. To determine the appropriate spatial prediction model, we evaluated the accuracy of a two dimensional approach for ordinary kriging (OK) and regression kriging (RK), and a three dimensional approach for random forest model (RF). The RF was based on topography, climate and vegetation factors (RF env), distance and direction from 152 volcanos (RF env+vol), distance from survey points (RF env+sp); these factors were also combined (RF all) as explanatory attributes. The results demonstrated an improvement in the average root mean square errors (RMSEs) of RF env+vol , RF env+sp , and RF all by approximately 12%, 14%, 5%, and 21% for OC, BD, CF, and OCS using 10-fold cross-validation on a site-by-site basis, respectively, compared with OK. Based on the relatively small difference in improvement among RF env+vol , RF env+sp , and RF all (<0.5%), and the considerably high computational cost of RF env+sp and RF all , RF env+vol was considered as the appropriate model for the area. The R2 of RF env+vol was calculated at 0.59, 0.44, 0.30, and 0.38 in OC, BD, CF, and OCS, respectively, using 10-fold cross validation on a site-by-site basis. The predicted map by RF env+vol accurately reproduced the large spatial variation in OCS at small watershed, regional, and national scales; this have been derived from the effect of microtopography, tephra deposition, and the gradient of temperature with latitude, respectively. A larger OCS was predicted to accumulate on the ridges, highland slopes, near volcanos, and higher latitudes than at other areas. The mean OCS was estimated to be 7.6 kg·m−2 for a soil depth of 0–30 cm in Japanese forests. The results also suggest that the fine-scale three dimensional random forest approach using the distances from volcanoes showed promise for regional-scale OCS prediction. This approach was considered particularly effective in hills and mountainous areas that are strongly exposed to tephra deposition, this includes the Pacific Rim regions and other volcanic countries. [ABSTRACT FROM AUTHOR]

Abstract: Spatial estimates of tropical soil organic carbon (SOC) concentrations and stocks are crucial to understanding the role of tropical SOC in the global carbon cycle. They also allow for spatial variation of SOC in environmental process models. SOC is spatially highly variable. In traditional approaches, SOC concentrations and stocks have been derived from estimates for single or very few profiles and spatially linked to existing units of soil or vegetation maps. However, many existing soil profile data are incomplete and untested as to whether they are representative or unbiased. Also single means for soil or vegetation map units cannot characterize SOC spatial variability within these units. We here use the digital soil mapping approach to predict the spatial distribution of SOC. This relies on a soil inference model based on spatially referenced environmental layers of topographic attributes, soil units, parent material, and forest history. We sampled soils at 165 sites, stratified according to topography and lithology, on Barro Colorado Island (BCI), Panama, at depths of 0–10 cm, 10–20 cm, 20–30 cm, and 30–50 cm, and analyzed them for SOC by dry combustion. We applied Random Forest (RF) analysis as a modeling tool to the SOC data for each depth interval in order to compare vertical and lateral distribution patterns. RF has several advantages compared to other modeling approaches, for instance, the fact that it is neither sensitive to overfitting nor to noise features. The RF-based digital SOC mapping approach provided SOC estimates of high spatial resolution and estimates of error and predictor importance. The environmental variables that explained most of the variation in the topsoil (0–10 cm) were topographic attributes. In the subsoil (10–50 cm), SOC distribution was best explained by soil texture classes as derived from soil mapping units. The estimates for SOC stocks in the upper 30 cm ranged between 38 and 116 Mg ha−1, with lowest stocks on midslope and highest on toeslope positions. This digital soil mapping approach can be applied to similar landscapes to refine the spatial resolution of SOC estimates. [Copyright &y& Elsevier]

• Three SOC maps were compared in mainland France. • Five model averaging approaches were tested in improving SOC map. • SOC map can be improved using more than 100 soil samples for model averaging. • Variance Weighted approach performed best. • Merging maps using model averaging is also applicable to data-poor situations. The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monitoring of SOC dynamics due to land use change and climate change. However, current estimates of SOC stock and its spatial distribution have large uncertainties. In this study, we test whether we can improve the accuracy of the three existing SOC maps of France obtained at national (IGCS), continental (LUCAS), and global (SoilGrids) scales using statistical model averaging approaches. Soil data from the French Soil Monitoring Network (RMQS) were used to calibrate and evaluate five model averaging approaches, i.e., Granger-Ramanathan, Bias-corrected Variance Weighted (BC-VW), Bayesian Modelling Averaging, Cubist and Residual-based Cubist. Cross-validation showed that with a calibration size larger than 100 observations, the five model averaging approaches performed better than individual SOC maps. The BC-VW approach performed best and is recommended for model averaging. Our results show that 200 calibration observations were an acceptable calibration strategy for model averaging in France, showing that a fairly small number of spatially stratified observations (sampling density of 1 sample per 2500 km2) provides sufficient calibration data. We also tested the use of model averaging in data-poor situations by reproducing national SOC maps using various sized subsets of the IGCS dataset for model calibration. The results show that model averaging always performs better than the national SOC map. However, the Modelling Efficiency dropped substantially when the national SOC map was excluded in model averaging. This indicates the necessity of including a national SOC map for model averaging, even if produced with a small dataset (i.e., 200 samples). This study provides a reference for data-poor countries to improve national SOC maps using existing continental and global SOC maps. [ABSTRACT FROM AUTHOR]