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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Prediction of topsoil texture for Region Centre (France) applying model ensemble methods
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Prediction of topsoil texture for Region Centre (France) applying model ensemble methods

机译：地区中心（法国）应用模型集合方法预测地区纹理

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摘要

With the rapid development of digital soil mapping it is not unusual to find several maps for the same soilproperty in an area of interest. We applied two standard methods of model averaging for combining two regional maps and a European map of topsoil texture in agricultural land for the Region Centre (France). The two methods for model ensemble were the Granger-Ramanathan (G-R) and the Bates-Granger (B-G). A calibration dataset was used for fitting the coefficients of the G-R model, and for calculating a global variance: prediction error ratio which was then used to re-scale the weights of the B-G model. The prediction performance of the three primary maps and the two ensemble maps was compared with an independent validation dataset consisting on 100 observations from the French soil monitoring network. The prediction accuracy of the ensemble models improved only for day in comparison to the primary maps (Delta R-2 = 0.02-0.06, Delta RMSE = -1.56- - 4.97 g kg(-1)). Overall, the G-R models obtained smaller RMSE and greater bias than B-G, and G-R estimated better the prediction uncertainty. The dissimilarities between the methods for estimating the prediction variance and non-optimal estimated uncertainties were important limitations for the B-G models despite applying a global correction factor for the prediction variances. The results suggested that both the calibration and validation datasets should represent the patterns of spatial variation and range of values of the soil property for the prediction space. Nonetheless, model ensemble methods proved to be useful for merging maps with different types of datasets, spatial coverage, and methodological approaches. (C) 2017 Elsevier B.V. All rights reserved.
机译：随着数字土壤映射的快速发展，在感兴趣的领域寻找同样的土壤资产的地图并不罕见。我们应用了两个标准的模型平均方法，将两个区域地图和欧洲地图在地区中心（法国）的农业用地中的地产土地上的欧洲土地纹理。模型集合的两种方法是格兰杰 - ramanathan（G-R）和Bates-Granger（B-G）。校准数据集用于拟合G-R模型的系数，并计算全局方差：预测误差比，然后用于重新缩放B-G模型的权重。将三个主要贴图的预测性能与来自法国土壤监测网络的100个观察组成的独立验证数据集进行了比较。与主要映射相比，集合模型的预测精度仅改善了（Delta R-2 = 0.02-0.06，Delta RMSE = -1.56- - 4.97g kg（-1））。总的来说，G-R模型比B-G更小的RMSE和更大的偏置，G-R估计更好的预测不确定性。估计预测方差和非最佳估计不确定性的方法之间的异化是B-G模型的重要局限性，尽管应用了预测方差的全局校正因子。结果表明，校准和验证数据集应该代表空间变化模式和预测空间的土壤性质的范围。尽管如此，模型集合方法证明是有用的，可用于合并不同类型的数据集，空间覆盖率和方法方法的映射。 （c）2017 Elsevier B.v.保留所有权利。

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