利用实地土壤表观导电率测量结果改进区域泥炭厚度图

IF 2.1 Q3 SOIL SCIENCE
Raphaël Deragon, Brandon Heung, Nicholas Lefebvre, Kingsley John, A. Cambouris, Jean Caron
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引用次数: 0

摘要

越来越多地采用近端传感器有助于生成泥炭制图产品:它们能迅速收集数据,并能探测到泥炭矿物的后期边界。第三层由沉积泥炭构成(湖沼层,gyttja),有时可以在它们之间找到。这种物质在空间上是高度可变的,当位于地表附近时,它与退化的土壤性质有关。本研究旨在评估直流电阻率测量在预测最大泥炭厚度(MPT)(定义为非沼泽泥炭厚度)方面的潜力,以促进农田尺度上的土壤保持和管理实践。结果还与先前研究中使用的MPT区域图进行了比较,并作为协变量进行了测试。本研究在加拿大quacimubec的浅层(MPT = 8-138 cm)栽培有机土壤中进行。使用Veris Q2800的视电导率(ECa)和数字高程模型绘制MPT图,并使用或不使用区域MPT图(RM)作为协变量来缩小其比例。将三种机器学习算法(立体主义、随机森林和支持向量回归)与普通克里格(OK)、多元线性回归和多元线性回归克里格(MLRK)模型进行了比较。预测效果最好的是OK (Lin’s CCC = 0.89, RMSE = 13.75 cm),其次是MLRK-RM (CCC = 0.85, RMSE = 15.7 cm)。所有模型都比RM更准确(CCC = 0.65, RMSE = 29.85 cm),尽管它们低估了MPT > 100 cm。此外,添加RM作为协变量导致所有模型的预测误差更低,精度更高。总体而言,通过产生更精确的管理区域,田间尺度的方法可以更好地支持精确的土壤保持干预措施。未来的研究应该测试多传感器融合和其他地球物理传感器,以进一步提高模型性能并检测更深的边界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale
The increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface.This study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the non-limnic peat thickness, to facilitate soil conservation and management practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models.The best predictive performance was achieved with OK (Lin’s CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generating more accurate management zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries.
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