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Abstract (in English)
Digital Soil Mapping (DSM) has revolutionized the field of soil science by integrating advanced statistical techniques, environmental data, and remote sensing technologies to create high-resolution soil maps. Unlike traditional soil mapping, which relies on qualitative estimates and is labor-intensive, DSM provides a more efficient and reproducible approach to soil characterization. This paper explores the advancements, applications, and challenges of DSM, highlighting its role in precision agriculture, environment management, and land-use planning. To accomplish these objectives, a narrative review approach was employed, facilitating a comprehensive exploration of the topic through the collection, summarization, and synthesis of findings from previous research. Previous research has demonstrated that Mapping DSM has significantly enhanced the accuracy and accessibility of soil data. However, several challenges persist, including data availability, model selection, and uncertainty quantification. The future trajectory of DSM is closely dependent on technological advancements, particularly in machine learning, big data analytics, and real-time soil monitoring. Overcoming these challenges requires a multidisciplinary approach involving interdisciplinary collaboration, policy support, and the development of open-source tools. These findings underscore the need for continued investment in innovative technologies and collaborative frameworks to maximize DSM’s potential in sustainable land management and agricultural decision-making.