Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Abstract

. The challenge of predicting soil organic carbon distribution accurately has received great attention in order to support urban green space soil management during climate change. This study compared four geostatistical methods: kriging combined with land use, ordinary kriging, inverse distance weighting and radial basis function, to predict the spatial distribution patterns of soil organic carbon content and soil organic carbon density in the Xiong'an New Area, estimate organic carbon stocks, and assess the role of land use types in the spatial prediction of soil organic carbon stocks. The results showed that the soil organic carbon content decreased with increasing soil depth, and was significantly affected by different land use types (p<0.05). The correlation coefficient values of kriging combined with land use were on average 0.229 higher than those of other methods. The root mean squared error and the mean absolute error of kriging combined with land use were on average 0.148 and 0.139 lower than those of the other methods. Kriging combined with land use has a greater advantage over other methods in predicting the spatial distribution of soil organic carbon content, and also the spatial distribution of soil organic carbon density and the spatial distribution of soil organic carbon, the prediction results of the four interpolation methods were similar. The average soil organic carbon density was 2085 Gg (0-30 cm) and 1363 Gg (30-60 cm). In conclusion, land use type clearly influences the spatial distribution of soil organic carbon in urban areas, and by using land use type as auxiliary data, we can obtain a more accurate spatial distribution of soil organic carbon and predict the total storage capacity of the soil. This study may result in significant advances in the spatial prediction of soil organic carbon for urban areas.