State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Department of Plant Nutrition, Sanya Institute of China Agricultural University, 572025 Sanya, China
Tingyao Cai
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Xingshuai Tian
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Zhong Chen
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Kai He
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Zihan Wang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Haiqing Gong
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Qi Miao
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Yingcheng Wang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Yiyan Chu
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Minghao Zhuang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
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4,494
1,078
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6,030
410
128
208
HTML: 4,494
PDF: 1,078
XML: 458
Total: 6,030
Supplement: 410
BibTeX: 128
EndNote: 208
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Cumulative views and downloads
(calculated since 15 Jul 2024)
Total article views: 4,148 (including HTML, PDF, and XML)
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Total
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EndNote
3,304
740
104
4,148
231
80
118
HTML: 3,304
PDF: 740
XML: 104
Total: 4,148
Supplement: 231
BibTeX: 80
EndNote: 118
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Cumulative views and downloads
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Total article views: 1,882 (including HTML, PDF, and XML)
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1,190
338
354
1,882
179
48
90
HTML: 1,190
PDF: 338
XML: 354
Total: 1,882
Supplement: 179
BibTeX: 48
EndNote: 90
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Cumulative views and downloads
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Total article views: 6,030 (including HTML, PDF, and XML)
Thereof 5,954 with geography defined
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Total article views: 4,148 (including HTML, PDF, and XML)
Thereof 4,142 with geography defined
and 6 with unknown origin.
Total article views: 1,882 (including HTML, PDF, and XML)
Thereof 1,812 with geography defined
and 70 with unknown origin.
Accurately quantifying the distribution of soil profile SOC (soil organic carbon) stocks is crucial for carbon sequestration and mitigation. The detailed spatial subsoil SOC data are the scientific basis for environmental protection, as well as for the development of Earth system models. Based on multiple environmental variables and soil profile data, this study used machine learning approaches to evaluate the SOC stocks and their spatial distribution at a depth interval of 20–100 cm in various ecosystems.
Accurately quantifying the distribution of soil profile SOC (soil organic carbon) stocks is...
