Developing a national black soil map of China through machine learning classification

Black soil (BS) is an important soil resource. However, there is a lack of accurate information on BS spatial distribution, which affects the sustainable use and protection of BS. In this study, we linked BS distribution and machine learning. Random forest (RF) classifier and recent soil survey data were used to produce a high–precision BS distribution map in China. We analyzed environmental control factors of BS distribution. The results showed that the RF classifier method can well estimate the spatial distribution of BS at the national scale with an overall accuracy of 0.63 ∼ 0.91. The hot spots of BS distribution were mainly located in the Songnen Plain, Sanjiang Plain, the eastern side of the Greater Khingan Mountains and the western side of Changbai Mountains in Northeast China, the northeastern foot of Qilian Mountains in Qinghai–Tibet Plateau and the northern foot of Tianshan Mountains in Northwest China. Nearly 45 % BS area was covered by cropland. The daily mean land surface temperature and vegetation coverage had important controling effects on the BS distribution. Excluding the alpine mountains, BS area with potential for conversion to cropland can increase by 18.12 % (3.27 × 104 km2). The mapped BS distribution of China can serve as a benchmark for BS resource monitoring and play an important role in its assessment and protection.