在罗马尼亚使用地缘统计方法和机器学习进行了土壤表层特性的空间建模
Cristian Valeriu Patriche1, Bogdan Roşca1, Radu Gabriel Pîrnău1
1Geographic Research Center, Romanian Academy, Iaşi Branch, Iaşi, Romania.
PloS one
|August 23, 2023
概括
罗马尼亚的数字土壤绘制有效地使用地缘统计和机器学习方法制作了高分辨率的土壤属性地图. 回归耕作和机器学习,特别是支持矢量机器和随机森林,在预测土壤特征方面表现最佳.
科学领域:
- 土壤科学 土壤科学
- 数字土壤绘图 (DSM) 是指土壤的数字地图.
- 地质统计学 在地质统计学
- 机器学习 机器学习
背景情况:
- 土壤特性数字地图对于各种农业和土壤科学研究应用至关重要.
- 数字土壤绘图 (DSM) 技术已经取得了重大进展,使得这些基本数据层的创建成为可能.
- 罗马尼亚的土壤属性数据需要高分辨率的数字绘图,以改善农业管理和研究.
研究的目的:
- 应用地质统计和机器学习方法制作罗马尼亚地表土壤特性的高分辨率数字地图.
- 评估不同DSM技术的性能,包括普通 kriging,回归-kriging和机器学习算法.
- 确定最优的方法来空间预测关键的土壤化学性质和颗粒大小分数.
主要方法:
- 采用了地理统计学方法 (普通 kriging,回归-kriging,地理加权回归) 和机器学习算法.
- 利用了六个连续预测指标:数字海拔模型,地形湿度指数,正常化差异植被指数,斜率,度和经度.
- 使用独立样本数据集的验证方法,包括卢卡斯土壤数据和遗留的土壤概况,以及200k罗马尼亚土壤地图.
主要成果:
- 回归-kriging和机器学习算法,特别是支持矢量机器和随机森林,被确定为最佳的DSM方法.
- 地理加权回归对于预测pH和碳酸的表现良好.
- 对于pH (0.417-0.469),有机碳 (0.302-0.443) 和碳酸 (0.300-0.330) 实现了良好的预测准确度 (R平方).
结论:
- 机器学习和回归耕作对罗马尼亚的数字土壤绘图非常有效.
- 卢卡斯数据库是土壤属性数据的可靠来源,支持准确的空间预测.
- 生成的数字土壤地图为国家和地区的土壤研究和农业应用提供了宝贵的数据.
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