基于土壤深度函数和校正方法的长三角三维土壤有机碳密度数据集

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Shurui Lin , Qing Zhu , Beini Yin , Guishan Yang , Kaihua Liao , Xiaoming Lai , Changqiang Guo
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引用次数: 0

摘要

准确、高分辨率、深度连续的土壤有机碳密度(SOCD)数据对各种研究和管理实践至关重要。基于593个土壤样本,对长三角地区0 ~ 1.0 m空间分辨率为90 m的SOCD三维数据集进行了不同土壤深度函数和校正方法的测试。对训练集中不同样本的深度函数(幂函数、指数衰减函数、对数函数和逆函数)进行拟合,得到的参数通过基于辅助变量的随机森林进行映射。然后在验证集中应用系数缩放、数据融合和残差校正三种校正方法对深度函数的预测进行校正。校正后,各深度的预测精度均有显著提高。通过构造校正系数的垂直连续分布,我们的数据集可以在任何特定深度区间生成精确的SOCD图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China
Accurate, high resolution, and depth continuous soil organic carbon density (SOCD) dataset is crucial for various research and management practices. Based on 593 soil samples, we tested different soil depth functions and correction methods for generating the 0–1.0 m three-dimensional SOCD dataset with the spatial resolution of 90-m in the Yangtze River Delta region. Depth functions (power function, exponential decay function, logarithmic function and inverse function) were fitted for different samples in the training set, and their obtained parameters were mapped by random forest based on ancillary variables. Then three correction methods, including coefficient scaling, data fusion and residual correction, were applied in the validation set to correct the predictions of depth functions. After correcting, the prediction accuracies have been significantly improved at all depths. Our dataset can generate accurate SOCD maps at any specific depth interval by constructing the vertical continuous distribution of the corrected coefficients.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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