基于地形与遥感辅助信息的小流域尺度高分辨率有机碳空间分布预测研究
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南京农业大学资源与环境学院

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国家自然科学基金项目(面上项目)


High-resolution Digital Mapping of Soil Organic Carbon at Small Watershed Scale Using Landform Element Classification and Assisted Remote Sensing Information
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南京农业大学资源与环境学院

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The National Natural Science Foundation of China (General Program)

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    摘要:

    土壤有机碳(Soil organic carbon,SOC)既是衡量土壤质量的重要指标,也是影响全球碳氮循环的关键因素之一。作为数字土壤制图(Digital soil mapping,DSM)研究中起主要作用的环境变量,地形元素在SOC预测制图中也是无可替代的。应用机器学习模型,通过引入不同超参数设置下获得的高分辨率(5m)Geomorphons(GM)地形分类图作为丘陵地形特征信息的补充,结合数字高程模型(Digital elevation model,DEM)衍生变量和光学、合成孔径雷达(Synthetic aperture radar,SAR)遥感数据对句容市黄梅镇北部小流域尺度(1:25 000)丘陵地貌区地表层SOC含量进行预测制图,并评估不同GM变量在SOC含量预测中的表现。基于74个土壤样本和不同环境变量组合,分别采用袋装决策回归树(Bagged CART)、随机森林(Random forest,RF)和立体派(Cubist)三种方法构建SOC含量预测模型,并通过四个精度验证指标,采用十折交叉验证对生成的模型性能进行分析评价。总体上,Cubist模型的预测表现优于Bagged CART和RF模型。分析显示,与单独使用DEM衍生变量相比,引入GM变量能提供更准确的SOC含量预测,其中设置20像元(cells)搜索半径(L)与5°平坦度阈值(t)的GM变量表现出最高的模型贡献度,两者与遥感类变量的组合产生了最高的预测精度(R2=0.53)。引入GM变量后,使用Cubist模型估算SOC含量的R2提高了14.3%。研究表明,在小流域尺度丘陵地貌区,地形类变量是SOC预测的主要解释变量,其中谷底平坦综合指数(Multi resolution index of valley bottom flatness,MRVBF)和高程是模型中最重要的两个环境变量;同时,在建立SOC预测模型时,高分辨率GM图像具有作为输入环境变量的应用潜力。

    Abstract:

    【Objective】 Soil organic carbon (SOC) is an important indicator of soil fertility and plays a fundamental role in the terrestrial ecosystem carbon cycle. As one of the primary environmental factors in digital soil mapping (DSM), landform elements are irreplaceable in predicting SOC. The purpose of this study was to simulate the complex and nonlinear relationship between SOC and environmental variables and evaluate the importance of each variable to accuracy in SOC mapping. 【Method】We applied machine learning techniques to map SOC content in a small watershed (1: 25000) of Huangmei Town, Jurong City using high-resolution landform elements classification maps known as geomorphons, digital elevation model (DEM) derivatives, optical and synthetic aperture radar (SAR) remote sensing data. The performance of all geomorphon (GM) variables under different hyperparameter settings was evaluated to predict SOC content. Three machine-learners including bagged classification and regression tree (Bagged CART), random forest (RF) and Cubist were used to construct predictive models of SOC content based on 74 soil samples and different combinations of environmental covariates. Model A, Model B, and Model D included only GM variables, DEM derivatives, and remote sensing variables, respectively. Model B was a combination of GM data and DEM derivatives, while Model E included all predictor variables. The performance of these models was evaluated based on a 10-fold cross-validation method by four statistical indicators. Concordance index (C index), root mean square errors (RMSE), bias and coefficient of determination (R2) of the three models were worked out for evaluation of the accuracy of their predictions. The best model was screening-out for mapping SOC in the study area based on the raster datasets of all environmental variables. 【Result】Overall, the Cubist model performed better than RF and Bagged CART, and these models yielded similar spatial distribution patterns of SOC, i.e. an ascending trend from the northern hilly area to the southern flatter land of the study area. Our results showed that more accurate predictions of SOC content were provided with the introduction of GM variables than individual DEM derivatives. The GM map with 20 cells search radius (L) and 5° flatness threshold (t) showed the highest relative importance within four GM variables in three models. The Cubist E model that functioned based on GM landform elements classification variables, DEM derivatives and remote sensing variables was much better than the others in performance and could explain most of the spatial heterogeneity of SOC (R2 = 0.53). Also, the prediction accuracy changed with and without the GM predictors with the R2 for estimating SOC content using the Cubist model increasing by 14.3%. The SOC contents of the hilly region predicted with the Cubist E model ranged from 5.65 to 13.31 g/kg. In addition, topographic variables were the main explanatory variables for SOC predictions and the multi-resolution index of valley bottom flatness (MRVBF) and elevation were assigned as the two most important variables. 【Conclusion】The Cubist model that functions based on GM variables, DEM derivatives, as well as remote sensing variables, is a promising approach to predicting the spatial distribution of SOC in hilly regions at a small watershed scale. The results of this study illustrate the potential of GM landform elements classification data as input when developing SOC prediction models.

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魏宇宸,卢晓丽,朱昌达,张秀秀,潘剑君.基于地形与遥感辅助信息的小流域尺度高分辨率有机碳空间分布预测研究[J].土壤学报,,[待发表]
weiyuchen, luxiaoli, zhuchangda, zhangxiuxiu, panjianjun. High-resolution Digital Mapping of Soil Organic Carbon at Small Watershed Scale Using Landform Element Classification and Assisted Remote Sensing Information[J]. Acta Pedologica Sinica,,[In Press]

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