Alpine grassland ecosystems play a crucial role in the global carbon (C) balance by contributing to the soil organic carbon (SOC) pool; thus, quantifying SOC stocks in these ecosystems is essential for understanding potential gains or losses in soil C under the threat of climate change and anthropogenic activities. Remote sensing plays a vital role in estimating SOC stocks; however, identifying reliable remote sensing proxies to enhance SOC prediction remains a challenge. Information on soil C cycling proxies can reveal how the balance between C inputs and outputs affects SOC. Therefore, these proxies could be effective indicators of SOC variations. In this study, we explored the potential of satellite-derived attributes related to soil C cycling proxies for predicting SOC stocks. We derived remote sensing indices such as gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance and assessed the relationships between these indices and SOC stocks via partial least squares structural equation modeling (PLS-SEM). We evaluated the effectiveness of these indices in predicting SOC stocks, we compared PLS-SEM and quantile regression forest (QRF) models across different variable combinations, including static, intra-annual, and inter-annual information. The PLS-SEM results demonstrated the suitability of the derived remote sensing indices and their interactions in reflecting processes related to soil C balance. The QRF models, using these indices, achieved promising prediction accuracies, with a coefficient of determination (R2) of 0.54 and a root mean square error (RMSE) of 0.79 kg m-2 at the topmost 10 cm of soil. However, the prediction performance generally decreased with increasing soil depth, up to 30 cm. The results also revealed that adding intra- and inter-annual information to remotely sensed proxies did not increase the prediction accuracy. Our study revealed that gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance are effective proxies for representing factors influencing soil C balance and mapping SOC stocks in alpine grasslands.

Seasonally frozen ground (SFG) significantly contributes to global carbon sinks. Global warming and anthropogenic-induced disturbances threaten the carbon storage capacity of SFG. Challenges in evaluating the SFG carbon storage potential include the lack of understanding of the control mechanisms of soil organic carbon (SOC) variations and timely spatial estimates of SOC. In this study, we investigated SOC stocks in SFG in the Tibet Autonomous Region, China, in 2020 and 2021. We employed partial least squares structural equation modeling (PLS-SEM) to explore the effect of complex processes (interacting roles of climate, plant physiology and phenology, freeze-thaw cycle, soil environment, and C inputs) on SOC and mapped SOC stocks in the topmost 30 cm. We identified four causal pathways: (1) an indirect pathway representing the effect of climate on plant physiology and phenology through changes in freeze-thaw cycles and soil environment, (2) an indirect pathway representing the effect of climate on C inputs through changes in freeze-thaw cycles, soil environment and plant physiology and phenology, (3) an indirect pathway representing the effect of climate on freeze-thaw cycles, and (4) an indirect pathway representing the effect of climate on the soil environment through changes in freeze--thaw cycles. C inputs exerted the greatest control on SOC. The effect of these factors decreased with increasing soil depth. We used PLS-SEM to generate maps of SOC stocks in SFG at a 500 m resolution with a moderate accuracy. The estimated mean SOC stocks in the 0-30 cm layer reached 6.87 kg m(-2), with a 95% confidence interval ranging from 6.2 to 7.5 kg m(-2). Our results indicated that it is critical to consider the depth dependence of the direct and indirect effects of environmental factors when assessing the control mechanisms of SOC vari-ations. In this work, we also demonstrated that spatially explicit SOC estimates based on timely investigations are important for assessing C stocks against the background of considerable environmental changes across the Ti-betan Plateau.