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| 基于机器学习的江汉平原土壤有机碳预测及制图 |
| Prediction and mapping soil organic carbon in Jianghan Plain by machine learning |
| Received:August 25, 2022 Revised:January 17, 2023 |
| DOI：10.13254/j.jare.2022.0583 |
| 中文关键词: 碳库，土壤-景观模型，机器学习，环境因子，深层土壤 |
| 英文关键词: carbon pool, soil-landscape model, machine learning, environmental factor, deep soil |
| 基金项目:国家自然科学基金青年科学基金项目（41601370）；中央高校基本科研业务费专项（CCNU22JC022）；江西省教育厅科技项目（GJJ210541） |
| Author Name | Affiliation | E-mail | | SHEN Jiali | Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China | | | CHEN Songchao | ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China | | | HU Bifeng | School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China | | | LI Shuo | Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China | shuoguoguo@zju.edu.cn |
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| 中文摘要: |
| 土壤有机碳（SOC）不仅显著响应于表层，而且随深度的变化呈现不同的响应。江汉平原作为长江经济带农田生态系统的重要组成部分，其SOC垂向分布状况仍有待考察。本研究收集了2009—2012年湖北省土系调查的66个剖面数据，基于9个环境因子为协变量使用随机森林法构建 0~30、>30~60、>60~100 cm土层深度的 SOC含量预测模型，绘制了 30 m空间分辨率的 SOC含量分布图，并估算了 SOC 储量。结果表明，SOC 含量随土层深度的增加而减少，总体呈中东部高、西部低的特征。模型对表层（0~30 cm）SOC的预测精度最高（R2=0.45，RMSE=3.28 g·kg-1），温度、黏粒含量和降水对模型重要性居前三。江汉平原 1 m深 SOC储量为 183.75 Tg，>30~100 cm土层 SOC储量约占 1 m深度总储量的 59%。因此，土壤碳库估算及固碳潜力评估时需着重考虑深层土壤。本研究可为掌握SOC空间分布及垂直分异的响应规律提供参考。 |
| 英文摘要: |
| Soil organic carbon（SOC）responds significantly to the surface layer, but also shows different responses with depth. Jianghan Plain plays an important role in the agroecosystem of the Yangtze River Economic Belt, but its SOC baseline remains unclear. In this study, 66 profiles were collected from 2009 to 2012 in Jianghan Plain, and SOC content prediction models were constructed based on nine environmental factors as covariates using the random forest method for three depths：0–30, >30–60 cm, and >60–100 cm. We constructed a baseline map of SOC at 30 m resolution and estimated the SOC stock. The results showed that SOC contents decreased with increasing soil depth, and were generally high in the east-central part and low in the west. The model had the highest prediction accuracy（R2 =0.45, RMSE= 3.28 g·kg-1）for the topsoil（0–30 cm）. Temperature, clay content, and precipitation were the three most important covariables for the model. The soil organic carbon storage at 1 m depth in Jianghan Plain was 183.75 Tg, and >30–100 cm accounted for approximately 59% of the total storage at 1 m depth. We conclude that it is necessary to focus on deep soils when evaluating the soil carbon pool and sequestration potential. Our new estimate can serve as a base for both horizontal and vertical variation of SOC. |
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