[基于多尺度地理权重回归 (MGWR) 的土壤pH空间预测建模及其影响因素]
Ming-Song Zhao1,2,3, Xuan-Qiang Chen1,2,3, Shao-Jie Xu1
1School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China.
Huan jing ke xue= Huanjing kexue
|December 15, 2023
概括
多尺度地理加权回归 (MGWR) 模型最好地预测了中国四个省份的土壤pH空间分布和影响因素. MGWR揭示了降水,平坦度和高度如何影响土壤pH的显著空间异质性.
科学领域:
- 土壤科学 土壤科学
- 地理空间分析的研究.
- 环境建模环境建模
背景情况:
- 土壤pH值是影响土壤质量和农业生产力的关键因素.
- 了解土壤pH值的空间分布和影响因素对于有效的土地管理至关重要.
研究的目的:
- 为模拟安,河南,江苏和山东省土壤pH的空间分布.
- 识别和分析影响土壤pH值的环境因素的空间变化.
- 将多尺度地理加权回归 (MGWR) 的性能与用于土壤pH预测的其他回归模型进行比较.
主要方法:
- 在整个研究区域收集了599个土壤样本和环境数据.
- 应用多重线性回归 (MLR),地理加权回归 (GWR),混合地理加权回归 (混合GWR) 和MGWR模型.
- 利用定量回归来分析环境因素对不同分布水平的土壤pH的不同影响.
主要成果:
- 土壤pH值表现出显著的空间自相关性和聚类.
- MGWR模型显示出最合适的 (R2adj = 0.64),表现优于MLR,GWR和混合GWR.
- 空间分布显示,河南北部的土壤pH值较高,安南部的土壤pH值较低.
- 平均年降水量 (MAP),多分辨率谷底平面度 (MRVBF) 和海拔显示出对土壤pH的空间异质影响.
- 在所有水平上,MAP对土壤pH有显著的负面影响,在更高的pH量度下,强度下降.
结论:
- MGWR是预测土壤pH值和了解其影响因素在大地区的卓越模型.
- 诸如MAP,MRVBF和海拔等环境因素对土壤pH的影响具有复杂的空间异质性.
- 这些发现为土壤属性绘制和有针对性的土壤管理策略提供了关键的见解.
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