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• 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]

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

• Models could be built using a bare soil composite image from Landsat 5. • Soil organic carbon model was driven by near infrared and visible light bands. • Clay model was primarily dependent on the shortwave infrared bands. • Cation exchange capacity model was driven by shortwave and near infrared bands. There is an increasing need for detailed soil property maps to support land use planning, soil carbon accounting, and precision agriculture. While soil maps exist in Saskatchewan, they are at coarse scales (1:100,000), which are not always suitable for detailed soil management. One emerging technique for predictive soil mapping is the use of bare soil composite imagery derived from multi-temporal satellite imagery. This study focused on using bare soil composite imagery along with legacy soil data (1958–1998) with high location uncertainty to predict soil organic carbon, clay, and cation exchange capacity. The bare soil composite images were created from Landsat 5 imagery (1985 to 1995) using Google Earth Engine. Predictive models were built using a Random Forest model for each parameter and evaluated using a 75–25 train-test split. The soil organic carbon model had an R2 value of 0.55 with a root mean square error (RMSE) of 0.67 percent, with the near infrared and visible light bands being the most important features in the model. The clay predictive model has an R2 of 0.44 and a RMSE of 5.0 percent, with the shortwave infrared bands being most important. The cation exchange capacity model had an R2 of 0.50 with a RMSE of 5.7 meq 100 g−1, with the shortwave and near infrared bands as the most important predictors. Based on these results, bare soil composite imagery represents a valuable covariate for predictive soil mapping in the Canadian Prairies. This work also illustrates that for regions with extensive adoption of conservation farming practices, satellite imagery should be obtained for time periods before these practices were adopted from the months of the year where crop residues have decomposed. By combining historical soil survey data with historical imagery, maps of legacy soil properties can be generated to make comparisons against with modern data for applications such as monitoring soil organic carbon change over time. [ABSTRACT FROM AUTHOR]

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

• The feasibility of adding multi-temporal radar Sentinel-1A data to SOC prediction was explored. • Climate variables contributed the most to explaining SOC content variation. • In this ecosystem, RF and BRT models showed similar performance and produced similar SOC spatial distribution patterns. Soil organic carbon (SOC) has a large impact on soil quality and global climate change. It is therefore important to be able to predict SOC accurately to promote sustainable soil management. Although the synthetic aperture radar (SAR) has many advantages and has been widely used in soil science research, it has rarely been used in previous SOC mapping studies based on remote sensing images. The purpose of this study was to investigate the ability of multi-temporal Sentinel-1A data in SOC prediction, by comparing the predictive performance of random forest (RF) and boosted regression tree (BRT) models in the Heihe River Basin in northwestern China. A set of 162 topsoil (0–20 cm) samples were taken and 15 environmental variables were obtained including land use, topography, climate, and remote sensing images (optical and SAR data). Using a cross-validation procedure to evaluate the performance of the models, three statistical indices were calculated. Overall, both RF and BRT models effectively predicted SOC content, exhibiting similar performance and producing similar spatial distribution patterns of SOC. The results showed that the addition of multi-temporal Sentinel-1A images improved prediction accuracy, with the root mean squared error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) improving by 9.0%, 8.3% and 13.5%, respectively. Furthermore, the combination of all environmental variables had the best prediction performance explaining 75% of SOC variation. The most important environmental variables explaining SOC variation were precipitation, elevation, and temperature. The multi-temporal Sentinel-1A data in RF and BRT models explained 9% and 7%, respectively. The results from our case study highlight the usefulness of multi-temporal Sentinel-1 data in SOC mapping. [ABSTRACT FROM AUTHOR]

• Wet year forage growth clearly associated with 0–30 cm soil organic carbon stocks. • Terrain attributes help explain half of 0–30 cm soil organic carbon variability. • 0–10 cm soil organic carbon stocks are noisier: variability is 75% unexplainable. • Complex rangeland terrain presents a challenge for soil organic carbon inventories. Accurate assessments of soil organic carbon (SOC) stocks are needed at multiple scales given their importance to both local soil health and global C cycles. Rangelands cover 54% of California, representing a large stock of SOC, but existing SOC estimates are uncertain. To improve understanding of fine-resolution SOC stocks in complex terrain and provide guidance to rangeland SOC inventories, we grid-sampled 105 locations (21-m grid cells) at two depths (0–10 and 10–30 cm) in a 10-ha annual grassland catchment in California's Central Coast Range. Soils were analyzed for bulk density, coarse fragments, SOC and texture. Monthly aerial imagery was acquired by an unmanned aerial vehicle to compare surface reflectance during two contrasting years (wet vs. dry) to SOC stocks. We found that the 0–30 cm soil thickness held 3.64 ± 0.71 kg SOC m−2 (mean ± SD) with a range of 1.97–5.49 kg SOC m−2. The 0–10 cm soil thickness stored 47% of the 0–30 cm SOC stock with SOC concentrations twice as high in the 0–10 cm layer (1.40 ± 0.38%) as in the 10–30 cm layer (0.71 ± 0.15% SOC). Multiple linear regression (MLR) models explained 50–57% of SOC variability at 0–30 and 10–30 cm, but only 25% of variability at 0–10 cm. Based on cross-validation tests, MLR outperformed spatial interpolation methods and Random Forest models, best explaining SOC stocks with five environmental covariates: wet-year greenness, mean curvature, elevation, insolation, and slope. Lower hillslope positions, concave landforms, and enhanced wet-year greenness were associated with more SOC, and explained 11%, 24%, and 31% of variability in 0–30 cm SOC stocks, respectively. This study demonstrates that the accuracy of regional-scale SOC mapping of California rangelands benefits from considering microclimatic and topographic controls at the catchment-scale, in addition to broader scale mineralogical and macroclimatic controls identified in previous SOC studies. [ABSTRACT FROM AUTHOR]

• Soil workability threshold was mapped using PTFs and predicted maps of SOC and clay. • Random Forest outperformed Generalized Boosted Regression for predicting SOC and clay. • Workability map can be used for effective on-farm and regional agricultural management. Climate change is presenting sizeable challenges for agricultural production around the world. In some regions, shifting precipitation patterns in the spring and fall are negatively impacting farm operation by reducing the number of "workable days" or the days fields can be worked with heavy equipment without damaging soil structure. This can be particularly problematic for farms on clay soils and/or poor drainage. Approximating a water content threshold at which a soil is not workable due to soil structure destruction can be helpful for planning effective farm operations. In this study, we applied advanced remote sensing and machine learning tools to produce digital maps of soil organic carbon (SOC) and clay (CL) content and used them in existing pedotransfer functions (PTFs) to predict a workability threshold (WT) across a study area in Delta, British Columbia, Canada. We combined field data, soil and vegetation indices derived from multiple Landsat satellite images, topographic indices, and soil survey information to digitally map SOC and CL of the agricultural lands in Delta using random forest (RF) and generalized boosted regression model (GBM). When validated against an independent field dataset, the RF model outperformed GBM for all accuracy measures (coefficient of determination – R2, concordance correlation coefficient – CCC, and normalized root mean square error – nRMSE). We then spatially applied several PTFs using our digital maps to estimate the plasticity limits of the soil and produce WT map. The WT map was then tested against independent field samples of the soil water content at −10 kPa and we achieved R2 of 0.59, CCC of 0.70, and nRMSE of 0.15. Our analysis showed that 40% of the fields in the study area had WT < 30%, a threshold that is already being impacted by reduced workable days. This WT map could be used to improve spatial prioritizations of investments for climate change adaptation at farm to regional scales. [ABSTRACT FROM AUTHOR]