Gridded, temporally referenced spatial information on soil organic carbon for Hungary.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Gábor Szatmári, Annamária Laborczi, János Mészáros, Katalin Takács, András Benő, Sándor Koós, Zsófia Bakacsi, László Pásztor
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引用次数: 0

Abstract

Soil organic carbon (SOC), known as the most important soil attribute, affects various soil functions and services, essential for nutritious food and clean drinking water. Since recognizing its key role in many environmental challenges, there has been an increasing demand for spatial information on SOC. Our objective is to present the results of a mapping activity aimed at producing spatially exhaustive information on SOC content, density, and stock for the topsoils of Hungary for 1992 and 2000. A "time-for-space" digital soil mapping approach was pursued to predict and map these SOC properties, with the associated uncertainty, at a resolution of 100 × 100 m. Particular attention was paid to validating the accuracy of the maps and the reliability of the uncertainty quantifications. The published maps are recommended to be used as baseline maps for Hungary. The spatial resolution makes them suitable for various practical applications (e.g., GHG inventory, sustainable agriculture, carbon sequestration). The maps are of interest to researchers, practitioners, and policymakers, helping to achieve scientifically sound results and informed decision-making.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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