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Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R2 = 0.69, RMSE = 6.06 g kg−1, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg−1, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R2 accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping. [ABSTRACT FROM AUTHOR]

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.

Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

Estimations of the patterns and controls of soil organic carbon (SOC) could provide instructive insights into the potential impact of future global change on soil carbon (C). In this work, we combined GeoDetector and random forest (RF) to estimate SOC stocks in Chinese grassland ecosystems with uncertainty assessments, and identified a network of cross-correlated environmental covariates for determining SOC based on a dataset collected from 813 sampling sites (0–20 cm) collected from 2000 to 2014. We predicted that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm and that the average SOC density was 4.69 kg C/m−2(−|−). The effectiveness of using RF to predict SOC was demonstrated by an accuracy assessment based on 10-fold cross-validation, with a ratio of performance to deviation (RPD) of 2.89. The SOC stocks in southern China were lower than those in northern China. A high SOC density was found in northeastern China and on the Qinghai–Tibet Plateau. Soil properties had the strongest direct effect on SOC. Climate was significantly negatively associated with SOC and indirectly affected SOC via its effect on soil properties. Topography had a significant direct impact on vegetation, but its direct effect on SOC was relatively weak. This study emphasizes the patterns and heterogeneity of SOC stocks as well as the relative significance of climate, vegetation, soil characteristics, topography, and their complex interrelationships in controlling SOC. These results may provide a theoretical foundation for developing sustainable management systems and calibrating C process models.

Abstract 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.

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) 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]

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.

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]

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.

To improve the accuracy of soil organic carbon (SOC) mapping, an adaptive zoning regression kriging (AZ_RK) framework was proposed, which simultaneously considers spatial heterogeneity and the influence of multiple environmental variables. In AZ_RK, the study area was first adaptively divided into several spatially homogeneous zones. Then, the SOC distribution was mapped in each zone separately considering the relationships between SOC and environmental variables. The results indicated that the influence of environmental variables on SOC in different zones differed. The proposed zoning strategy was proven to be valid, which can account for 36.6% of the spatial variance in SOC. Furthermore, AZ_RK outperformed other methods with the lowest RMSE and the highest R2 ([Formula: see text]= 0.495 and [Formula: see text]= 0.481). We concluded that AZ_RK can effectively cluster spatially homogeneous zones and adaptively map SOC with high estimation accuracy, which can serve as a valuable model reference for precise fertilization in regions characterized by spatial heterogeneity.

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 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.

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]

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.

Accurately mapping soil organic carbon (SOC) is conducive to evaluating carbon storage and soil quality. However, the high spatial heterogeneity of SOC caused by river-related factors and agricultural management brings challenges to digital soil mapping in floodplain farmland. Moreover, current studies focus on the non-linear relationship between SOC and covariates, but ignore the effective range of environmental variables on SOC, which prevents the revelation of the SOC differentiation mechanism. Using the 375 samples collected from the Jiangchang Town near Han River, we aim to determine the main controlling factors of SOC, reveal the effective range of environmental variables, and obtain the spatial map of SOC by using the gradient boosting decision tree (GBDT) model and partial dependence plots. Linear regression was used as a reference. Results showed that GBDT outperformed linear regression. GBDT results show that the distance from the river was the most important SOC factor, confirming the importance of the Han River to the SOC pattern. The partial dependence plots indicate that all environmental variables have their effective ranges, and when their values are extremely high or low, they do not respond to changes in SOC. Specifically, the influential ranges of rivers, irrigation canals, and rural settlements on SOC were within 4000, 200, and 50 m, respectively. The peak SOC was obtained with high clay (≥31%), total nitrogen (≥1.18 g/kg), and total potassium contents (≥11.1 g/kg), but it remained steady when these covariates further increased. These results highlight the importance of revealing the effective range of environmental variables, which provides data support for understanding the spatial pattern of SOC in floodplain farmland, achieving carbon sequestration in farmland and precision agriculture. The GBDT with the partial dependence plot was effective in SOC fitting and mapping. [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]

The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils' properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Traditional land-use systems can be modified under the conditions of climate change. Higher air temperatures and loss of productive soil moisture lead to reduced crop yields. Irrigation is a possible solution to these problems. However, intense irrigation may have contributed to land degradation. This research assessed the ameliorative potential of soil and produced large-scale digital maps of soil properties for arable plot planning for the construction and operation of irrigation systems. Our research was carried out in the southern forest–steppe zone (Southern Ural, Russia). The soil cover of the site is represented by agrochernozem soils (Luvic Chernozem). We examined the morphological, physicochemical and agrochemical properties of the soil, as well as its heavy metal contents. The random forest (RF) non-linear approach was used to estimate the spatial distribution of the properties and produce maps. We found that soils were characterized by high organic carbon content (SOC) and neutral acidity and were well supplied with nitrogen and potassium concentrations. The agrochernozem was characterized by favorable water–physical properties and showed good values for water infiltration and moisture categories. The contents of heavy metals (lead, cadmium, mercury, cobalt, zinc and copper) did not exceed permissible levels. The soil quality rating interpretation confirms that these soils have high potential fertility and are convenient for irrigation activities. The spatial distribution of soil properties according to the generated maps were not homogeneous. The results showed that remote sensing covariates were the most critical variables in explaining soil properties variability. Our findings may be useful for developing reclamation strategies for similar soils that can restore soil health and improve crop productivity. [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) 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.

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.

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.

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]

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff. [ABSTRACT FROM AUTHOR]

• Sentinel-2 bare soil spectra was used for up-to-date soil organic carbon mapping in croplands. • The predictive capability of single-date versus multitemporal bare soil images was compared. • Performances of single-date prediction models were temporally unstable. • High-dimensional geometric median composite outperformed 11 out of 12 single-date models. • Incorporation of SOC-related spectral indexes further improved the SOC prediction accuracy. Sustainable cropland management requires quantitative and up-to-date information on the spatial pattern of soil organic carbon (SOC) at scales relevant for implementing targeted conservation measures. Spectra-based remote sensing of SOC in croplands is promising, but it requires the extraction of high-quality bare soil pixels that enable spatially continuous coverage. Here, we aim to compare the SOC predictive capability of single-date versus multitemporal compositing of bare soil images in an intensively cultivated region (4,700 km2) of northeast China. A series of 12 bare soil images within 2017–2022 were processed and passed onto three multitemporal compositing approaches (geometric median, and univariate mean and median) to create bare soil mosaics. Both single-date and multitemporal spectral images, together with laboratory-simulated Sentinel-2 benchmark data, were used to develop partial least squares regression, Cubist and random forest models via 100 bootstrapped validations. With Cubist consistently being the best performing algorithm for all three data sources, results show that the 12 single-date images exhibited temporally unstable performance (R2: 0.30–0.67). Among the three compositing approaches, the high-dimensional geometric median composite was the most suitable because of (i) its close resemblance to laboratory reference and robustness to outliers, which yielded a Cubist model (R2: 0.64; RMSE: 2.24 g/kg) outperforming 11 out of 12 single-date models; and (ii) its ability to retain between-band spectral relationships that allowed further incorporation of SOC-relevant bare soil indexes, which led to a further 6.5 % increase in model prediction accuracy. The resultant SOC map highlighted the capability of Sentinel-2 bare soil imaging to reveal field-scale soil degradation patterns. Future work should explore the possibility of extending the purely spectra-based framework to integrated SOC mapping and monitoring with additional soil biophysical and management information. [ABSTRACT FROM AUTHOR]

• Higher model performance for granulometry than SOC% using seedbed spectra. • PLSR outperformed other algorithms for SOC% estimation, CR for granulometry. • Low contribution of seedbed spectra to the DSM of SOC% using environmental covariates. Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates. [ABSTRACT FROM AUTHOR]

• This study estimated that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm, and the average SOC density was 4.69 kg C m−2. • A path network showing interacting effects of climate, topography, soil characteristics, and vegetation on SOC. • The direct effect of topography on SOC was relatively weak, but it did have a significant positive impact on vegetation at a broad geographical scale. Estimations of the patterns and controls of soil organic carbon (SOC) could provide instructive insights into the potential impact of future global change on soil carbon (C). In this work, we combined GeoDetector and random forest (RF) to estimate SOC stocks in Chinese grassland ecosystems with uncertainty assessments, and identified a network of cross-correlated environmental covariates for determining SOC based on a dataset collected from 813 sampling sites (0–20 cm) collected from 2000 to 2014. We predicted that 17.50 Pg C was stored in Chinese grasslands to a depth of 20 cm and that the average SOC density was 4.69 kg C/m−2(−|−). The effectiveness of using RF to predict SOC was demonstrated by an accuracy assessment based on 10-fold cross-validation, with a ratio of performance to deviation (RPD) of 2.89. The SOC stocks in southern China were lower than those in northern China. A high SOC density was found in northeastern China and on the Qinghai–Tibet Plateau. Soil properties had the strongest direct effect on SOC. Climate was significantly negatively associated with SOC and indirectly affected SOC via its effect on soil properties. Topography had a significant direct impact on vegetation, but its direct effect on SOC was relatively weak. This study emphasizes the patterns and heterogeneity of SOC stocks as well as the relative significance of climate, vegetation, soil characteristics, topography, and their complex interrelationships in controlling SOC. These results may provide a theoretical foundation for developing sustainable management systems and calibrating C process models. [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.

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]

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 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]

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.

Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content. This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index (NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry), and the number of bootstrap replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results at our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies. [ABSTRACT FROM AUTHOR]

Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models' results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring. [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]

• 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 problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

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.

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.

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.

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]

Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content. This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index (NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry), and the number of bootstrap replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results at our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies. [ABSTRACT FROM AUTHOR]

Digital soil mapping (DSM) has been developed for decades and aims to accurately simulate the soil factors with low cost. However, it still cost considerably in coastal wetlands because of the high difficulty in field survey. A zonal simulation approach was proposed for decreasing the cost of field survey in the premise of high accuracy for soil organic carbon stock mapping in coastal wetlands, and Chongming Island, an important estuarine wetland in China, was used to demonstrate the study. A subzone with an area proportion lower than 20% was identified based on the typicalness of land surface characteristics and soil influencing factors for the whole study area. Entire and zonal simulations were conducted using abundant predictors sourced from remote sensing, partial least square regression, and 10-fold cross validation. The results revealed that root mean squared errors of entire and zonal simulations were 1.70 g/kg and 1.95 g/kg, respectively, which were in a low level compared with those in previous studies and indicated the high simulation accuracy. The zonal simulation exhibited a slightly lower accuracy than the entire simulation, while considerably decreased the cost by more than 50%. The decrease in the cost showed a much more practical significance than the increase in the accuracy in areas with high difficulty in field survey, and the geographical integrity and continuity of the whole study area, the typicalness of the selected subzone, and the spatial extent were the three key points when promoting the zonal simulation in DSM. The soil organic carbon stock in the study area was generally high in areas with good vegetation condition, low soil salinity, complex landscape configuration, and long distances from the sea. Human activities and the resulting landscape fragmentation have been the dominant factor that drives the spatial pattern of soil organic carbon stock.

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.

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.