基于数字土壤制图和多元线性回归的忠清地区土壤碳储量统计估算

Yun-Gu Kang, Jae-Han Lee, Jun-Yeong Lee, Taek-Keun Oh
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摘要

数字土壤制图(DSM)是一种利用土壤特征和环境因素来创建表征土壤特性的空间分布图的统计技术。SCORPAN模型由土壤(S)、气候(C)、生物(O)、地形(R)、母质(P)、年龄(A)和空间(N)组成,描述了DSM技术中使用的环境因素。本研究的目的是评估忠清地区土壤碳储量的空间分布图,并利用DSM技术预测0 ~ 30 cm深度的土壤碳储量。预测碳储量最小值为25.11 t C ha-1,最大值为183.55 t C ha-1,平均值为46.92±13.66 t C ha-1。土壤碳储量空间分布图显示,以丹阳郡为代表的忠北地区土壤碳储量较高,而忠南沿海地区土壤碳储量较低。以平均土壤碳储量、农地面积、碳补偿交易价格为标准,忠清地区土壤碳储量的经济价值为4063亿韩元。DSM的验证结果总结如下:模型的决定系数(R2)为0.15,表明验证数据的置信水平为15%。平均绝对误差(MAE)为20.78,均方根误差(RMSE)为29.51。土壤碳储量的预测值与预测值之间的散点图显示,预测值低于实测值,表明模型的预测性能有待提高。因此,本研究估算的土壤碳储量及其空间分布图可作为评估农业土壤潜在固碳能力的基础信息,为减缓气候变化和实现碳中和做出贡献。农业土壤碳储量(0 ~ 30 cm)空间分布图及观测值与预测值的散点图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Estimation of Soil Carbon Stocks in Chungcheong Province through Digital Soil Mapping and Multiple Linear Regression
Digital soil mapping (DSM) is a statistical technique that utilizes soil characteristics and environmental factors to create spatial distribution maps representing soil properties. The SCORPAN model, consisting of soil (S), climate (C), organisms (O), relief (R), parent materials (P), age (A) and space (N), describes the environmental factors used in DSM techniques. The objectives of this study were to assess the spatial distribution map of soil carbon stocks in Chungcheong province and predict soil carbon stocks within the 0 - 30 cm depth using DSM technique. The minimum and maximum predicted carbon stocks were 25.11 ton C ha-1 and 183.55 ton C ha-1, respectively, with a mean of 46.92 ± 13.66 ton C ha-1. The spatial distribution map of soil carbon stocks revealed higher carbon stock in Chungcheongbuk-do, particularly in Danyang-gun, while lower carbon stocks were observed in the coastal areas of Chungcheongnam-do. The estimated economic value of soil carbon stocks in Chungcheong province was 406.3 billion won, based on the average soil carbon stock, agricultural land area and carbon offset trading price. The validation outcomes of the DSM are summarized as follows: the model achieved a coefficient of determination (R2) of 0.15, indicating the 15% confidence levels to the validation data. The mean absolute error (MAE) was 20.78, and the root mean square error (RMSE) was 29.51, respectively. The scatter plot between observed and predicted soil carbon stocks revealed that the predicted values were lower than the observed values, indicating a need for improvement in the model’s predictive performance. Therefore, the estimated soil carbon stocks and its spatial distribution map in this study can serve as fundamental information for assessing the potential carbon sequestration capacity of agricultural soils and contributing to climate change mitigation and carbon neutrality efforts.Spatial distribution map for agricultural soil carbon stock (0 - 30 cm) and scatter plot between observed and predicted values.
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