Estimating Soil Organic Carbon Density in the Otindag Sandy Land, Inner Mongolia, China, for modelling spatiotemporal variations and evaluating the influences of human activities

Abstract

Accurate quantitative estimates of Soil Organic Carbon Density (SOCD) can effectively represent regional carbon cycle processes and regulation mechanisms, and can serve as reference data when making land management decisions. Limited research, however, has been carried out in arid or desert zones covered with sparse vegetation, despite the fact that these cover wide areas of the earth and play a significant role in global carbon cycles. In this study, the Otindag Sandy Land and its surroundings (OSLAIS) in the Inner Mongolia Autonomous Region of China was selected as the study area. The study introduces a useful technique for making high spatial coverage SOCD estimates for drylands by utilizing GF-1 WFV optical satellite images and a time series of MODIS satellite remote sensing datasets, and using these to optimize parameters for simulation models in conjunction with other technical procedures that are described. The results showed that the resulting model's accuracy was 77.87%, R2 = 0.8627, and so the SOCD estimates modelled by soil basal respiration (SBR) could be used for SOCD estimation and for analyzing the spatial distribution patterns across the OSLAIS. The average SOCD was 1.22 kgC/m2 for the whole of the OSLAIS, and it had a heterogenous distribution pattern. The SOCD was closely related to the way the land was used in each area, and the average SOCD for the main land use types were: forest land = 2.88 kgC/m2, farmland = 1.63 kgC/m2, shrub land = 1.41 kgC/m2, and grassland = 1.08 kgC/m2. In conclusion, we believe that the proposed method, based on high-resolution GF-1 WFV data and optimized estimation models constructed by integrating climate and vegetation characteristic data, can effectively describe the spatial distribution patterns of SOC and SOCD in the OSLAIS area, in depth and in detail, especially for the areas where the SOCD values are high. We expect this research to provide useful technical support and scientific reference data for land management and for land degradation/desertification assessments, for the study area monitored, as well as across the whole dryland area of China.