基于物候与极端气候信息的耕地土壤有机碳空间分布预测研究
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江西农业大学

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基金项目:

国家重点研发计划项目(2020YFD1100603)和国家自然科学基金项目(41361049)


Prediction of Spatial Distribution of Soil Organic Carbon in Cultivated Land Based on Phenology and Extreme Climate Information
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Affiliation:

Academy of Land Resource and Environment, Jiangxi Agricultural University

Fund Project:

National Key R&D Program of China (No. 2020YFD1100603) and the National Natural Science Foundation of China (No. 41361049)

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    摘要:

    土壤有机碳(Soil Organic Carbon,SOC)作为陆地生态系统中最大的碳库,在农田土壤质量和作物产量方面发挥着重要作用。准确预测耕地SOC的空间分布对于制定农业管理措施至关重要。在数字土壤制图(Digital Soil Mapping,DSM)框架下,选择有效的环境协变量是提高SOC空间预测精度的重要方法。以往遥感指数和气候变量通常使用某个时段或时点的(平均)值作为输入变量,而很少有研究将时间特性和事件用于土壤有机碳预测。因此,引入物候变量、极端气候变量弥补部分损失的地物信息和气候特征,探讨其对研究区耕地SOC空间变异的响应特性及预测SOC空间分布的可行性。以江西省上高县为研究区域,采用随机森林模型,选取遥感数据、DEM衍生变量、物候参数、气候特征因子等作为环境协变量引入模型中,并用普通克里格(Ordinary Kriging,OK)对模型结果进行残差修正,最后对比不同类型变量组合下模型的预测效果及预测精度。结果表明,时序变量、物候变量及极端气候变量能够改善模型的预测性能,并且残差作为误差项还能进一步提升模型的精度。结合时序变量、物候变量、极端气候变量、地形变量和残差的组合拥有最高的预测精度,相较于地形变量、遥感变量和气候变量的组合,将R^2、MAE和RMSE提升了90.00%、58.95%和57.14%。变量贡献率分析显示,SU、a3和TXx是影响研究区耕地SOC分布的重要变量。因此,物候变量和极端气候变量具有较好的应用前景,未来还需验证极端气候变量作为环境变量在不同土地利用、大尺度研究区下预测土壤属性的有效性。

    Abstract:

    【Objective】As the largest Carbon pool in the terrestrial ecosystem, Soil Organic Carbon (SOC) plays an important role in Soil quality and crop yield. Accurate prediction of the spatial distribution of SOC on cropland is essential for the development of agricultural management measures. In the framework of Digital Soil Mapping (DSM), an important method to improve the precision of SOC spatial prediction is to select an effective environmental covariate. In previous studies, the mean values of remote sensing indices and climate variables for a certain period or point in time were usually used as input variables, while temporal characteristics and events were rarely used for SOC prediction. Therefore, in order to reduce the impact of the lack of part of physical information and climate characteristics, phenological variables and extreme climate variables were added in this study. The response characteristics to the spatial variability of SOC of cultivated land and the feasibility of predicting the spatial distribution of SOC were discussed. 【Method】The research area of this paper is Shanggao County, Jiangxi province. A random forest model was used, in which remote sensing data, DEM-derived variables, phenological parameters and climatic characteristics were selected as environmental covariates, and the model results were corrected for residuals using Ordinary Kriging (OK). The prediction effect and prediction accuracy of the model under different types of variable combinations were compared. 【Result】The results show that chronological variables, phenological variables, and extreme climate variables can improve the prediction performance of the model, and the residual error as an error item can further improve the accuracy of the model. The combination of chronological variables, phenological variables, extreme climate variables, topographic variables, and residuals had the highest prediction accuracy, improving R^2 , MAE, and RMSE by 90.00%, 58.95%, and 57.14% compared to the combination of topographic variables, remote sensing variables, and climate variables. The analysis of variable contribution rates shows that SU, a3 and TXx were important variables affecting the distribution of cultivated land SOC in the study area. 【Conclusion】Phenological variables and extreme climate variables have good application prospects. In the future, it is necessary to verify the validity of extreme climate variables as environmental variables in predicting soil properties under different land use and large-scale study areas.

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周琪清,赵小敏,郭 熙,周 洋.基于物候与极端气候信息的耕地土壤有机碳空间分布预测研究[J].土壤学报,2024,61(3). DOI:10.11766/trxb202211020602 ZHOU Qiqing, ZHAO Xiaomin, GUO Xi, ZHOU Yang. Prediction of Spatial Distribution of Soil Organic Carbon in Cultivated Land Based on Phenology and Extreme Climate Information[J]. Acta Pedologica Sinica,2024,61(3).

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  • 收稿日期:2022-11-02
  • 最后修改日期:2023-09-24
  • 录用日期:2023-11-16
  • 在线发布日期: 2023-11-20
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