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匈牙利土壤有机碳的网格化、时间参考空间信息。

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

作者信息

Szatmári Gábor, Laborczi Annamária, Mészáros János, Takács Katalin, Benő András, Koós Sándor, Bakacsi Zsófia, Pásztor László

机构信息

Institute for Soil Sciences, HUN-REN Centre for Agricultural Research, Budapest, Hungary.

Doctoral School of Earth Sciences, University of Debrecen, Debrecen, Hungary.

出版信息

Sci Data. 2024 Dec 2;11(1):1312. doi: 10.1038/s41597-024-04158-3.

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.

摘要

土壤有机碳(SOC)是最重要的土壤属性,影响着各种土壤功能和服务,而这些功能和服务对于营养丰富的食物和清洁的饮用水至关重要。自从认识到其在许多环境挑战中的关键作用以来,对土壤有机碳空间信息的需求日益增加。我们的目标是展示一项制图活动的结果,该活动旨在生成1992年和2000年匈牙利表层土壤有机碳含量、密度和储量的空间详尽信息。我们采用了“以时间换空间”的数字土壤制图方法,以100×100米的分辨率预测和绘制这些土壤有机碳属性及其相关不确定性。特别关注验证地图的准确性和不确定性量化的可靠性。建议将已发布的地图用作匈牙利的基线地图。其空间分辨率使其适用于各种实际应用(例如,温室气体清单、可持续农业、碳固存)。这些地图对研究人员、从业者和政策制定者具有吸引力,有助于取得科学合理的结果并做出明智的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ed3/11612427/1e9dff8e1720/41597_2024_4158_Fig1_HTML.jpg

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