A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
<p>Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in terms of the availability...
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Copernicus Publications
2025-02-01
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| Series: | Earth System Science Data |
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| author | G. Shi W. Sun W. Shangguan Z. Wei H. Yuan L. Li X. Sun Y. Zhang H. Liang D. Li F. Huang Q. Li Q. Li Y. Dai |
| author_facet | G. Shi W. Sun W. Shangguan Z. Wei H. Yuan L. Li X. Sun Y. Zhang H. Liang D. Li F. Huang Q. Li Q. Li Y. Dai |
| author_sort | G. Shi |
| collection | DOAJ |
| description | <p>Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in terms of the availability and quality of spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilised 11 209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, the World Soil Information Service, the First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterisation. Using advanced ensemble machine learning and a high-performance parallel-computing strategy, we developed comprehensive maps of 23 soil physical and chemical properties at six standard depth layers from 0 to 2 m in China at a 90 m spatial resolution (China dataset of soil properties for land surface modelling version 2, CSDLv2). Data-splitting and independent-sample validation strategies were employed to evaluate the accuracy of the predicted maps' quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e. CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of soil properties at all depth intervals ranged from good to moderate, with median model efficiency coefficients for most soil properties ranging from 0.29 to 0.70 during data-splitting validation and from 0.25 to 0.84 during independent-sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil property and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China, conducted during the 1970s and 1980s, they could provide new perspectives on soil changes, together with existing maps based on soil profiles from the 2010s. The findings of this study make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modelling and land surface modelling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available at <span class="uri">https://www.scidb.cn/s/ZZJzAz</span> (last access: 17 November 2024) or <span class="uri">https://doi.org/10.11888/Terre.tpdc.301235</span> (Shi and Shangguan, 2024).</p> |
| format | Article |
| id | doaj-art-cb8af57414ce4b0d9f4d85cc4f23afda |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
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| spelling | doaj-art-cb8af57414ce4b0d9f4d85cc4f23afda2025-02-07T06:27:25ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-02-011751754310.5194/essd-17-517-2025A China dataset of soil properties for land surface modelling (version 2, CSDLv2)G. Shi0W. Sun1W. Shangguan2Z. Wei3H. Yuan4L. Li5X. Sun6Y. Zhang7H. Liang8D. Li9F. Huang10Q. Li11Q. Li12Y. Dai13Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaCollege of Computer Science and Technology, Changchun Normal University, Changchun 130032, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China<p>Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in terms of the availability and quality of spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilised 11 209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, the World Soil Information Service, the First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterisation. Using advanced ensemble machine learning and a high-performance parallel-computing strategy, we developed comprehensive maps of 23 soil physical and chemical properties at six standard depth layers from 0 to 2 m in China at a 90 m spatial resolution (China dataset of soil properties for land surface modelling version 2, CSDLv2). Data-splitting and independent-sample validation strategies were employed to evaluate the accuracy of the predicted maps' quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e. CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of soil properties at all depth intervals ranged from good to moderate, with median model efficiency coefficients for most soil properties ranging from 0.29 to 0.70 during data-splitting validation and from 0.25 to 0.84 during independent-sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil property and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China, conducted during the 1970s and 1980s, they could provide new perspectives on soil changes, together with existing maps based on soil profiles from the 2010s. The findings of this study make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modelling and land surface modelling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available at <span class="uri">https://www.scidb.cn/s/ZZJzAz</span> (last access: 17 November 2024) or <span class="uri">https://doi.org/10.11888/Terre.tpdc.301235</span> (Shi and Shangguan, 2024).</p>https://essd.copernicus.org/articles/17/517/2025/essd-17-517-2025.pdf |
| spellingShingle | G. Shi W. Sun W. Shangguan Z. Wei H. Yuan L. Li X. Sun Y. Zhang H. Liang D. Li F. Huang Q. Li Q. Li Y. Dai A China dataset of soil properties for land surface modelling (version 2, CSDLv2) Earth System Science Data |
| title | A China dataset of soil properties for land surface modelling (version 2, CSDLv2) |
| title_full | A China dataset of soil properties for land surface modelling (version 2, CSDLv2) |
| title_fullStr | A China dataset of soil properties for land surface modelling (version 2, CSDLv2) |
| title_full_unstemmed | A China dataset of soil properties for land surface modelling (version 2, CSDLv2) |
| title_short | A China dataset of soil properties for land surface modelling (version 2, CSDLv2) |
| title_sort | china dataset of soil properties for land surface modelling version 2 csdlv2 |
| url | https://essd.copernicus.org/articles/17/517/2025/essd-17-517-2025.pdf |
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