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• 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]

• 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]