Abstract
The accuracy of digital soil maps (DSMs) is influenced by the quality of the input data. The application of legacy data obtained from past soil surveys was considered a potential method to improve DSM input data. However, changes in soil classification systems have occurred in many countries, and legacy data are therefore not directly applicable in the generation of the latest DSMs. This research established a cross-system framework for digital soil mapping, which enabled the conversion of legacy data and application to a new classification system for digital soil mapping. Jiangxi Province, China, was considered to evaluate this framework, where the classification system transitioned from the Genetic Soil Classification of China (GSCC) to the China Soil Taxonomy (CST). Second National Soil Survey legacy point data acquired based on the GSCC were converted and updated. Moreover, boundary placement of legacy polygon maps (BP-LPMs) containing GSCC soil class maps and soil parent material maps were converted into the expert knowledge of CST soil class boundaries. Fuzzy logic inference was employed to construct DSMs. The actual sampling points were extracted and considered in simple digital soil map (S-DSM) construction. The actual sampling points, the converted legacy point, and expert knowledge of soil class boundaries were used for hybrid digital soil map (H-DSM) construction. Cross-validation was used to evaluate the accuracy of digital soil mapping. The evaluation results indicated that compared to the S-DSM, the total accuracy of the H-DSM were improved by 17.0%, 15.1%, and 9.4% at the soil Order, Suborder, and Group class levels, respectively, and the producer's accuracy was further improved for most soil classes. This study demonstrated that legacy data obtained from past soil classification systems could be applied to improve digital soil maps under new soil classification systems through updating and conversion. This cross-system framework provides important implications for many countries with similar legacy data contexts.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
