Abstract: The explanatory power of traditional topographic factors on the spatial variability of soil organic carbon (SOC) is limited due to the small topographic relief in flat areas, which restricts the accuracy in digital soil mapping. Based on data collected from 136 sampling points in Fengqiu County in Henan Province (located in the alluvial plain of the Yellow River) in 2023, we constructed random forest, random forest regression-kriging (RFRK), extreme gradient boosting, and other SOC digital models. Within those models, we introduced environmental variables such as climate and multi-temporal remote sensing image-derived phenological variables, including growth trend parameters such as the peak and mean values of normalized difference vegetation index (NDVI), enhanced vegetation index, soil-adjusted vegetation index, and normalized difference water index (NDWI) during different crop growth stages. We used the Boruta algorithm to screen environmental covariates. The simulation performance of different models and variable combinations was compared and analyzed using the coefficient of determination (R²), mean error, root mean square error, and consistency correlation coefficient (CCC) to determine the optimal digital model, and SOC was simulated for the years 2013 and 2023. The results showed that environmental factors related to water conditions (such as Euclidean distance), multi-temporal phenological variables derived from NDWI, and NDVI played dominant roles in SOC digital soil mapping. The RFRK model was the optimal model in terms of overall accuracy (R² of 0.45), consistency (CCC of 64.4%), and spatial continuity. The introduction of multi-temporal phenological variables could effectively improve SOC simulation results in flat areas (R² increased by 0.08, CCC increased by 14.0%). RFRK model simulation results showed that from 2013 to 2023, SOC exhibited an overall increasing trend. There were differences in the magnitude of change across different regions, with the largest increase in the northwest region, reaching 80.6%. In summary, introducing multi-temporal phenological variables in flat areas could improve the accuracy of SOC digital soil mapping and provide a methodological reference for refined mapping of SOC in flat areas.

Key words: soil organic carbon, digital soil mapping, flat area, phenological variable