Digital soil mapping in the Russian Federation: A review

GEODERMA REGIONAL(2024)

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摘要
Russia stands at the origins of world soil cartography. Given Russia's vast and diverse territories, the production of current soil maps is an important task in the context of global climate change and food demand. This article provides a review of the digital soil mapping (DSM) field in Russia by identifying trends and research gaps. We examined studies published in international journals in the Scopus and Web of Science databases between the 1990s and the year 2023. We identified 47 articles, an analysis of which, revealed that the growth of DSM began in the second decade of the century. Geographically, the studies were more frequently conducted in the European part of Russia, with the vast majority (79%) performed at the local level (<100,000 ha) and two studies at the national scale. The leading target variables were SOC\SOM and soil classes, whereas remote sensing data and terrain attributes were the most popular among the covariates. Among the DSM methods, linear approaches were predominant, whereas machine learning methods have been increasingly adopted recently. We also discuss the challenges of DSM in Russia and define the following desirable future directions: First, efforts should focus on rescuing existing data and creating new databases. Additionally, we propose a transition to more advanced DSM methods with their subsequent validation (e.g., uncertainty assessment) and the utilisation of diverse cartographic environmental materials accumulated during the USSR era that cover the entire country at different scales. Furthermore, we emphasise the importance of expanding research at the regional and national levels, as well as in the Arctic region and the Far East. This study provides a comprehensive overview of the current state and trends in DSM in Russia. It sheds light on its advancement, challenges, and potential areas for development, guiding future research and policy decisions in the fields of soil mapping and environmental studies.
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关键词
Digital soil mapping,Russia,SCORPAN,Machine learning,Soil science,Cartography,Environmental covariates,Spatial modelling
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