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A Spatiotemporal Dataset of Soil Properties in Northeast China Based on Soil Sampling and Interpolation From 2009 to 2020

文献类型：期刊论文

作者Li, Shuzhen1,2; Wang, Jieyong1,3; Lin, Xu1,2; Liu, Yaqun1
刊名GEOSCIENCE DATA JOURNAL
出版日期2025-07-01
卷号12期号:3页码:e70012
关键词agricultural practices Northeast China soil testing spatiotemporal kriging interpolation spatiotemporal soil dataset
ISSN号2049-6060
DOI10.1002/gdj3.70012
产权排序1
文献子类Article ; Data Paper
英文摘要The Northeast region of China, serving as a crucial hub for grain production and an ecological security barrier, confronts significant challenges such as soil degradation and nutrient imbalance. Addressing the need for dynamic soil quality monitoring in the major grain-producing areas of Northeast China, this study innovatively develops a spatiotemporal sparse grid modelling framework and produces high-precision soil spatiotemporal datasets, based on soil testing and fertiliser recommendation data collected from various locations between 2009 and 2020. By integrating a spatiotemporal covariance function with the Kriging interpolation algorithm, the study systematically resolves the challenge of spatiotemporal collaborative modelling for multi-year discontinuous observational data. Consequently, continuous spatiotemporal datasets for soil pH, soil organic matter (SOM), total nitrogen (TN) and available potassium (AK) at a 500-m resolution in Yian County were successfully reconstructed. Various error metrics, including RMSE, MAE, MAXE, MINE and SE were employed to verify the high accuracy and reliability of the spatiotemporal Kriging interpolation method, with the relative error controlled at a minimum of 0.04. Geodetector analysis revealed significant spatial variability in soil properties (q > 0.8, p < 0.001). A spatiotemporal trend analysis framework, coupling Theil-Sen Median with Mann-Kendall, quantitatively demonstrated significant decreasing trends in pH, SOM and TN during the study period (with decreasing area proportions of 49.02%, 47.32% and 43.17%, respectively), while AK exhibited a significant increase of 41.96%. The spatial variability patterns were highly coupled with the spatial gradient characteristics of agricultural management measures. This dataset transcends the limitations of traditional static soil databases in spatiotemporal representation. Through a high-precision spatiotemporal continuous modelling technique system, it provides multi-scale spatiotemporal benchmark data support for precision agriculture, optimising conservation tillage of black soil, and simulation of agricultural carbon neutrality pathways. It holds significant scientific value for the sustainable management of farmland ecosystems in the context of global change. This dataset can be downloaded from .
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WOS关键词SPATIAL VARIABILITY ; ORGANIC-MATTER ; ECOSYSTEM ; CLASSIFICATION ; PATTERNS ; NITROGEN
WOS研究方向Geology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001499730300001
出版者WILEY
源URL[http://ir.igsnrr.ac.cn/handle/311030/214665]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
通讯作者Wang, Jieyong
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China;
2.Univ Chinese Acad Sci, Beijing, Peoples R China;
3.Minist Agr & Rural Affairs, Key Lab Black Soil Protect & Utilizat, Harbin, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shuzhen,Wang, Jieyong,Lin, Xu,et al. A Spatiotemporal Dataset of Soil Properties in Northeast China Based on Soil Sampling and Interpolation From 2009 to 2020[J]. GEOSCIENCE DATA JOURNAL,2025,12(3):e70012.
APA Li, Shuzhen,Wang, Jieyong,Lin, Xu,&Liu, Yaqun.(2025).A Spatiotemporal Dataset of Soil Properties in Northeast China Based on Soil Sampling and Interpolation From 2009 to 2020.GEOSCIENCE DATA JOURNAL,12(3),e70012.
MLA Li, Shuzhen,et al."A Spatiotemporal Dataset of Soil Properties in Northeast China Based on Soil Sampling and Interpolation From 2009 to 2020".GEOSCIENCE DATA JOURNAL 12.3(2025):e70012.

入库方式： OAI收割

来源：地理科学与资源研究所

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