A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling
Soil Organic Carbon (SOC) is vital for the global carbon cycle, agricultural sustainability, and climate change. Process-based models like Rothamsted carbon model (RothC) simulate SOC dynamics, but their accuracy relies on revisited soil samples for calibration, which are often scarce, especially in...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322500189X |
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| author | Wenkai Cui Lin Yang Lei Zhang Chenconghai Yang Chenxu Zhu Chenghu Zhou |
| author_facet | Wenkai Cui Lin Yang Lei Zhang Chenconghai Yang Chenxu Zhu Chenghu Zhou |
| author_sort | Wenkai Cui |
| collection | DOAJ |
| description | Soil Organic Carbon (SOC) is vital for the global carbon cycle, agricultural sustainability, and climate change. Process-based models like Rothamsted carbon model (RothC) simulate SOC dynamics, but their accuracy relies on revisited soil samples for calibration, which are often scarce, especially in environmentally heterogeneous regions. This study proposes a novel approach to generate pseudo revisited samples using environmental similarity, addressing the lack of revisited samples. For each intervening-year sample, pseudo SOC stocks in unsampled years were constructed by calculating environmental similarity with existing samples and applying weighted averaging. These pseudo SOC stocks served as revisited samples for model calibration. Bayesian optimization was used to adjust RothC’s microbial activity parameters. Validation results showed the approach significantly improved predictive accuracy, with an RMSE of 5.28 t/ha (31.6 % lower than global parameter optimization and 10.7 % lower than spatiotemporal Random Forest) and an R2 improved from 0.319 (spatiotemporal Random Forest) to 0.456. The approach enhances SOC model accuracy by leveraging environmental similarity and parameter optimization, offering a practical solution for regions lacking revisited samples and improving long-term SOC dynamics simulations. This approach not only addresses data scarcity but also provides more reliable predictions for climate and agricultural management. |
| format | Article |
| id | doaj-art-786ecffd584a42dd93789b396107fbcc |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-786ecffd584a42dd93789b396107fbcc2025-08-20T02:31:22ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910454210.1016/j.jag.2025.104542A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modellingWenkai Cui0Lin Yang1Lei Zhang2Chenconghai Yang3Chenxu Zhu4Chenghu Zhou5School of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing 210023, China; Corresponding author.School of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSoil Organic Carbon (SOC) is vital for the global carbon cycle, agricultural sustainability, and climate change. Process-based models like Rothamsted carbon model (RothC) simulate SOC dynamics, but their accuracy relies on revisited soil samples for calibration, which are often scarce, especially in environmentally heterogeneous regions. This study proposes a novel approach to generate pseudo revisited samples using environmental similarity, addressing the lack of revisited samples. For each intervening-year sample, pseudo SOC stocks in unsampled years were constructed by calculating environmental similarity with existing samples and applying weighted averaging. These pseudo SOC stocks served as revisited samples for model calibration. Bayesian optimization was used to adjust RothC’s microbial activity parameters. Validation results showed the approach significantly improved predictive accuracy, with an RMSE of 5.28 t/ha (31.6 % lower than global parameter optimization and 10.7 % lower than spatiotemporal Random Forest) and an R2 improved from 0.319 (spatiotemporal Random Forest) to 0.456. The approach enhances SOC model accuracy by leveraging environmental similarity and parameter optimization, offering a practical solution for regions lacking revisited samples and improving long-term SOC dynamics simulations. This approach not only addresses data scarcity but also provides more reliable predictions for climate and agricultural management.http://www.sciencedirect.com/science/article/pii/S156984322500189XSoil organic carbon (SOC)Pseudo revisited samplesThird law of geography (similar principle)Environmental similarityRothamsted carbon model (RothC) |
| spellingShingle | Wenkai Cui Lin Yang Lei Zhang Chenconghai Yang Chenxu Zhu Chenghu Zhou A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling International Journal of Applied Earth Observations and Geoinformation Soil organic carbon (SOC) Pseudo revisited samples Third law of geography (similar principle) Environmental similarity Rothamsted carbon model (RothC) |
| title | A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling |
| title_full | A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling |
| title_fullStr | A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling |
| title_full_unstemmed | A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling |
| title_short | A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling |
| title_sort | novel approach of generating pseudo revisited soil sample data based on environmental similarity for space time soil organic carbon modelling |
| topic | Soil organic carbon (SOC) Pseudo revisited samples Third law of geography (similar principle) Environmental similarity Rothamsted carbon model (RothC) |
| url | http://www.sciencedirect.com/science/article/pii/S156984322500189X |
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