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首页> 外文期刊>Journal of Hydrology >Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques
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Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques

机译：基于卫星数据和数据挖掘技术的土壤水分保留特性的数字映射

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Soil moisture retention is an important environmental factor that controls water availability in agro-ecosystems. Comprehensive information on spatial distribution and patterns of soil properties controlling moisture retention such as organic carbon (OC), clay content, and saturation percentage (SP) are crucial for effective land management and sustainable development. This study seeks to employ two data mining algorithms named Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programming (GEP) as predictive models in digital soil mapping (DSM) and quantify the associated uncertainty at a grid resolution of 30 m using satellite-based covariates. For each model, the features selected based on their interior algorithm during the training of the models. The performance and accuracy of MARS and GEP were evaluated through nine statistical/quantitative and graphical criteria, including mean error (ME), mean absolute error (MAE), Root mean squared error (RMSE) and coefficient of determination (R-2), relative RMSE (RMSE%), Taylor diagram, scatter, curve fitting, and point density plots. For each model, the prediction maps of soil properties and their associated uncertainty maps were generated. The results revealed that MARS outperformed GEP in providing predictions with superior performance and accuracy. Moreover, MARS performed better in showing the spatial distributions and patterns of all the studied soil properties. In addition, the MARS model produced less prediction uncertainty and can predict soil moisture retention properties more accurately. This study highlights the key role of data mining/numerical modeling algorithms as predictive models in DSM toward the most accurate predictions. Moreover, the study expressed the capabilities of remote sensing derived data in predicting complex soil properties. This study opened a new research line for accurate DSM.
机译：土壤水分潴留是一个重要的环境因素是控制水的供应农业生态系统。上空间分布和土壤性质控制湿度保持诸如有机碳（OC），粘土含量，和饱和度的百分比（SP）的模式的综合信息是有效的土地管理和可持续发展是至关重要的。这项研究旨在使用两种数据挖掘命名多元自适应回归样条（MARS）的算法和基因表达式编程（GEP）在数字土壤制图（DSM）预测模型，并使用基于卫星在30米网格分辨率量化相关的不确定性协变量。对于每个模型，特征选择模型的训练过程中根据其内部算法。 MARS和GEP的性能和准确度通过9统计/定量和图形的基准进行评价，其中包括平均误差（ME），平均绝对误差（MAE），均方根误差（RMSE）和决定系数（R-2），相对RMSE（RMSE％），泰勒图，散点图，曲线拟合，和点密度图。对于每个模型，预测土壤性质的映射和产生其相关的不确定性图。结果表明，火星在提供具有卓越的性能和准确度的预测优于GEP。此外，MARS执行在表示所有研究的土壤性质的空间分布和模式更好。此外，MARS模型产生较少的预测不确定性，并且可以更准确地预测土壤水分保持性质。这项研究强调数据挖掘/数字建模算法，如DSM预测模型向最准确预测的关键作用。此外，该研究在预测复杂土壤性质表示遥感导出的数据的能力。这项研究开辟了新的研究路线进行准确DSM。

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