Preprints
https://doi.org/10.5194/essd-2024-299
https://doi.org/10.5194/essd-2024-299
29 Aug 2024
 | 29 Aug 2024
Status: a revised version of this preprint is currently under review for the journal ESSD.

A China dataset of soil properties for land surface modeling (version 2)

Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Ye Zhang, Hongbin Liang, Lu Li, Xiaolin Sun, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai

Abstract. 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 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 utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples 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 most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 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 properties 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 during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Ye Zhang, Hongbin Liang, Lu Li, Xiaolin Sun, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-299', Anonymous Referee #1, 02 Oct 2024
    • AC1: 'Reply on RC1', Gaosong Shi, 23 Oct 2024
    • AC2: 'Reply on RC1', Gaosong Shi, 08 Nov 2024
  • RC2: 'Comment on essd-2024-299', Anonymous Referee #2, 01 Nov 2024
    • AC3: 'Reply on RC2', Gaosong Shi, 09 Nov 2024
    • AC4: 'Reply on RC2', Gaosong Shi, 15 Nov 2024
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Ye Zhang, Hongbin Liang, Lu Li, Xiaolin Sun, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai

Data sets

A China dataset of soil properties for land surface modeling (version 2) Gaosong Shi and Wei Shangguan https://doi.org/10.11888/Terre.tpdc.301235

Model code and software

A China dataset of soil properties for land surface modeling (version 2) Gaosong Shi https://github.com/shgsong/CSDLv2

Interactive computing environment

A China dataset of soil properties for land surface modeling (version 2) Gaosong Shi https://www.python.org/

IGSN

A China dataset of soil properties for land surface modeling (version 2) Gaosong Shi https://doi.org/10.11888/Terre.tpdc.301235

Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Ye Zhang, Hongbin Liang, Lu Li, Xiaolin Sun, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai

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Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological, ecological analyses, and earth system modeling, enhancing understanding of soil roles in environmental processes.
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