Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China

Remote sensing image data are often used as input in digital soil mapping (DSM). However, it is difficult to distinguish and identify soil types with less difference in reflectance spectral characteristics, because a small amount of input is not enough to provide enough common features. We consider that the hyper-temporal remote sensing data can be used to extract more common features of soil. The accuracy of DSM is improved by using the common features of soil or effective terrain attributes. We took Mingshui County of the Songnen Plain in northeast China as study area, which is known as a Black soil region. STRM DEM, legacy soil data, and 20 scenes Landsat images of bare soil period from 1984 to 2018 (April and May are considered a period of cultivated soil exposure in the study area), were used, with a maximum likelihood method classifier. A digital soil mapping model was constructed based on hyper-temporal data. Results from the study show that the accuracy of mapping with hyper-temporal classification characteristics, with an overall accuracy of 85.18% and a Kappa coefficient of 0.772, is higher than that of mono-temporal classification characteristics, with an average overall accuracy of 64.35% and an average Kappa coefficient of 0.467. After the introduction of relief degree of land surface (RDLS), the overall accuracy and Kappa coefficient of hyper-temporal mapping were 88.22% and 0.818, higher than the accuracy of other terrain factors. The research results signal the advantages of hyper-temporal remote sensing data in DSM, and the common features were able to improve the accuracy of DSM extracted from hyper-temporal data. This paper provided new insight to explain the impact of diverse terrain on DSM of Black soil region, and the mapping of soil type level could be accomplished more easily by the combination of the two characteristics.