Estimating carbon stocks at a regional level using soil information and easily accessible auxiliary variables

One of the most important challenges of digital soil mapping is the development of methods that allow the characterisation of large areas with a high-resolution. Surface soils, forming the largest pool of terrestrial organic carbon, may be able to sequester atmospheric carbon and thus mitigate climate change. However, this remains controversial, largely due to insufficient information on SOC stocks worldwide. One reason for this is the generally limited number of available data points, especially when large areas are considered, while another reason lies on the accuracy of interpolation techniques used for SOC mapping. The study was performed in Laos, a 230,566 km2 area mostly forested and with steep slopes, and where soil data from 2806 pits is available. Our objective was to estimate SOC stocks to a depth of 1 m over the whole country while improving regional digital soil mapping (RDSM). SOC mapping by using purely spatial approaches of ordinary kriging (OK), inverse distance weighting (IDW) and regularized spline with tension (RST) was compared with the use of additional information on relief, climate and soils through co-kriging (OCK). The generation and validation data sets were composed of 2665 and 141 data points respectively. Overall, OCK using a multiple correlation with elevation above sea level, compound topographic index, mean slope gradient, average annual rainfall, and soil clay content (R2 = 0.42; P level < 0.001) as covariate, yielded the most accurate predictions (19.7 kg C m− 2 with standard error of ± 3.2 kg C m− 2; and 4.54 ± 0.74 billion tons of SOC for Laos). The pure interpolation techniques were less accurate with 4.51 ± 1.02 billion tons of SOC for OK and 4.88 ± 0.94 billion tons of SOC for RST. Besides providing nationwide estimates of SOC stocks these results indicate that using collectively soil punctual information on SOC stocks and their inter-relationships with controlling factors which are easy to gather might be an efficient way to improve RDSM.