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A novel sampling design considering the local heterogeneity of soil for farm field-level mapping with multiple soil properties
Precision Agriculture ( IF 5.4 ) Pub Date : 2022-06-15 , DOI: 10.1007/s11119-022-09926-y
Yongji Wang , Qingwen Qi , Zhengyi Bao , Lili Wu , Qingling Geng , Jun Wang

Soil sampling is critical to obtaining reliable input for farm field-level digital soil mapping (DSM). Sample size and location are the key issues for soil sampling. However, sample size is often restricted by available budgets. In this case, recognizing the key sample locations is necessary. Existing methods have optimized the sample locations in a global manner without considering the impacts of local heterogeneity of soil. In this paper, a novel sampling approach based on the local heterogeneity of soil with a limited sample size (40 samples in this research) was developed. First, the local heterogeneity of soil was inferred. Second, the sub-regions were divided based on the level of local soil heterogeneity and the corresponding sample numbers were determined. Finally, the key sample locations were determined based on the fuzzy memberships. To validate the proposed method, it was compared with stratified random sampling, k-means sampling and conditional Latin hypercube sampling. The ordinary kriging method was applied to map five soil properties, including soil organic matter, pH, total nitrogen, available phosphorus and available potassium. The comparative experiments showed that the proposed method has better robustness in satisfying good mapping accuracy for multi-soil properties at the farm field level compared with the competing sampling methods, as indicated by the relatively lower and more stable mean bias error (MBE) and root mean square error (RMSE) values. It can be concluded that the consideration of local heterogeneity of soil is helpful to recognize the key sample locations for limited sample sizes.



中文翻译:

一种考虑土壤局部异质性的新型采样设计,用于具有多种土壤特性的农田水平制图

土壤采样对于获得可靠的农田级数字土壤测绘 (DSM) 输入至关重要。样本大小和位置是土壤采样的关键问题。但是,样本量通常受到可用预算的限制。在这种情况下,识别关键样本位置是必要的。现有方法在没有考虑土壤局部异质性的影响的情况下,以全局方式优化了样本位置。在本文中,开发了一种基于土壤局部异质性的新型采样方法,样本量有限(本研究为 40 个样本)。首先,推断土壤的局部异质性。其次,根据当地土壤异质性程度划分子区域,并确定相应的样本数。最后,根据模糊隶属度确定关键样本位置。为了验证所提出的方法,将其与分层随机抽样、k-均值抽样和条件拉丁超立方抽样进行了比较。采用普通克里金法绘制土壤有机质、pH、全氮、速效磷和速效钾五种土壤性质。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。它与分层随机抽样、k-means 抽样和条件拉丁超立方抽样进行了比较。采用普通克里金法绘制土壤有机质、pH、全氮、速效磷和速效钾五种土壤性质。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。它与分层随机抽样、k-means 抽样和条件拉丁超立方抽样进行了比较。采用普通克里金法绘制土壤有机质、pH、全氮、速效磷和速效钾五种土壤性质。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。采用普通克里金法绘制土壤有机质、pH、全氮、速效磷和速效钾五种土壤性质。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。采用普通克里金法绘制土壤有机质、pH、全氮、速效磷和速效钾五种土壤性质。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。对比实验表明,与竞争采样方法相比,所提出的方法在满足农田层面多土壤特性的良好映射精度方面具有更好的鲁棒性,这表现为相对较低和更稳定的平均偏差误差(MBE)和根均方误差 (RMSE) 值。可以得出结论,考虑土壤的局部异质性有助于识别有限样本量的关键样本位置。

更新日期:2022-06-16
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