Estimating soil organic carbon and pH in Jilin Province using Landsat and ancillary data

Remote sensing provides possibilities to support digital soil mapping at large scales, but this technology over complex landscapes is still challenging due to the high spatial heterogeneity of soil properties. The objective of this work was to check the predictive capability of SOC and soil pH over different ecoregions. First, we collected a series of satellite images from 2017 and ancillary variables (e.g., climate and terrain) as spectral and environmental indicators, respectively. A total of 201 topsoil samples (0-20 cm) from the Northeastern Black Soil Region of China were also used. Then, the relationships between the two soil properties and indicators were analyzed. The indicators that showed significant correlation with SOC or soil pH were involved in the subsequent models. Finally, the SOC and soil pH prediction models were constructed by using a stepwise regression model. The results showed that the visible and shortwave infrared bands, along with the thermal infrared band, had strong correlations with the two soil properties. Environmental factors such as precipitation, temperature, and elevation all had an impact on the distribution of SOC and soil pH. The SOC and soil pH prediction models had R-2 values of 0.66 and 0.73, root mean square errors of 0.21 and 0.58, and residual prediction deviations of 1.46 and 1.53, respectively, indicating reliable precision and average prediction abilities. This approach to estimating SOC and soil pH can contribute to improving our understanding and the modeling of soil processes at a large scale and in areas with relatively complex landscapes.