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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]

[Display omitted] • Soil salinity can be well reflected by vegetation and salinity indicators. • The conversion of spectral indicators can effectively reduce the upscaling error. • Precise land regionalization will improve the accuracy of scale conversion. • The risk of soil salinization gradually decreases from coastal to inland. • Soil salinization experienced a change of increasing first and then decreasing. As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture in coastal plains. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing technology has become an important tool for digital soil mapping because of its rich sources, real-time and low cost. In order to meet the objective demand for rapid, accurate, and efficient acquisition and monitoring of soil salinization. This paper collected 61 soil samples from the Kenli District (experimental area) and extracted vegetation and salinity indicators from the Landsat image to construct the salinity inversion model by random forest algorithm. Then, taking the Yellow River Delta as the study area, the conversion coefficient of spectral indicators between Landsat and MODIS images was constructed in the form of the ratio of the mean value. Through optimization, the upscaling conversion method based on land use regionalization was proposed to realize the upscaling inversion and dynamic monitoring of soil salinization. The results showed that: (1) The random forest model based on NDVI , RVI , EVI , SI3 , and SI5 can better predict the soil salinity in the experimental area, with R2 = 0.821 and RMSE = 2.811 (validation accuracy). (2) The upscaling conversion method based on land use regionalization can effectively reduce the statistical error and collinearity of spectral indicators constructed by MODIS images and improve their correlation with OLI data and soil salinity. (3) From coastal to inland, soil salinization gradually decreases in the Yellow River Delta. From 2000 to 2020, soil salinization increased first and then decreased, and the salinized soil accounted for 20.35%∼35.10%. This study used multi-source remote sensing data to realize the collaborative inversion at different scales, which was significant for the quantitative estimation of soil salinity, salinization control, and sustainable agricultural development in coastal plains. [ABSTRACT FROM AUTHOR]

• The ranking of environmental factors for soil type mapping was determined. • The optimal environmental covariates for DSM were clarified. • The optimal variable combination was determined. • The best overall accuracy produced by the optimal variable combination was 65.74%. The scientific selection of environmental covariates is an important factor that affects the accuracy of digital soil mapping (DSM).An investigation of the influences of different environmental covariates on soil type identification is vital to understand soil development and predictive soil mapping. In this study, the soil groups in the typical plain and hilly areas of the Jiaodong Peninsula in China were taken as the objects of investigation. Based on five environmental characteristics and remotely sensed indexes (texture, terrain, biology, major spectral components and moisture), 21 covariates sourced from Landsat 8 remote sensing images and a digital elevation map were utilized for the predictive mapping of soils. The identification accuracies of the soil groups were acquired through maximum likelihood classification, and the relevant environmental factors that are important for soil mapping and the influence of different environmental covariates on soil mapping were explored. The results show that texture features had the greatest influence on DSM, followed by terrain, major spectral components, moisture and biological factors. The single environmental covariate with the greatest impact on DSM was the texture entropy parameter. For areas containing plain and hilly landforms, the entropy, elevation, slope, normalized difference vegetation index (NDVI), third principal component (PC3) and normalized difference moisture index (NDMI) indicators compose the optimal covariate combination for soil identification. Clarifying the impacts of different environmental covariates on DSM can improve the soil classification applicability and accuracy. The environmental covariates discussed in this study would be useful in similar environmental areas and would provide a reference for DSM. [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]

• Soil TN is controlled by soil moisture and geographic attributes. • Soil AK is controlled by soil salinity, vegetation and geographic attributes. • Spatial distribution of TN and AK were characterized in YRD. • Digital soil mapping can be widely applied to map soil properties in wetland. Coastal wetland soils play a vital role in providing habitats for wildlife and vegetation, and in affecting water quality and primary production in coastal wetland ecosystem. The spatial prediction of soil properties provides important data support to help sustainable wetland management. This research explored the relationships between soil total nitrogen (TN) and soil available potassium (AK) and multiple environmental variables such as remote sensing spectral indices, geographic attributes and land use types of coastal wetlands in the Yellow River Delta (YRD). Soil moisture and geographic attributes were the major factors controlling the spatial distribution of soil TN. Soil salinity, vegetation and geographic attributes were the key factors affecting the spatial distribution of soil AK. The research established spatial prediction models of soil TN and AK by random forest method, and showed heterogeneous spatial patterns of TN and AK in the study area. Soil TN model showed relatively higher prediction performance compared with soil AK model. The research framework utilized in this study can be promoted to predict and map other soil properties, and has significant implication for the site-specific soil management of coastal wetlands in the YRD. [ABSTRACT FROM AUTHOR]