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Although algorithms are well developed to predict soil organic carbon (SOC), selecting appropriate covariates to improve prediction accuracy is an ongoing challenge. Terrain attributes and remote sensing data are the most common covariates for SOC prediction. This study tested the predictive performance of nine different combinations of topographic variables and multi-season remotely sensed data to improve the prediction of SOC in the cultivated lands of a middle mountain catchment of Nepal. The random forest method was used to predict SOC contents, and the quantile regression forest for quantifying the prediction uncertainty. Prediction of SOC contents was improved when remote sensing data of multiple seasons were used together with the terrain variables. Remote sensing data of multiple seasons capture the dynamic conditions of surface soils more effectively than using an image of a single season. It is concluded that the use of remote sensing images of multiple seasons instead of a snapshot of a single period may be more effective for improving the prediction of SOC in a digital soil mapping framework. However, an image with the right timing of cropping season can provide comparable results if a parsimonious model is preferred. [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]