用确定性和协变量集成的地缘统计模型对土壤碳的空间预测
Nandaluru Kalpana1, V Divya Vijayan2, Sahar Shaikh3
1Department of Soil Science & Agriculture Chemistry, College of Agriculture, Kerala Agricultural University, Vellanikkara, Thrissur, 680656, Kerala, India.
Environmental monitoring and assessment
|October 30, 2025
概括
土壤碳含量在热带景观中各不相同. 实证贝叶斯克里格回归预测 (EBKRP) 准确地绘制了使用环境因素如海拔和温度的土壤总碳 (TC).
科学领域:
- 土壤科学 土壤科学
- 环境科学 环境科学
- 地理空间分析的研究.
背景情况:
- 土壤碳是土壤质量的关键指标,其空间分布受到景观异质性的影响.
- 了解土壤碳变性对于热带地区有效的土地管理和环境监测至关重要.
研究的目的:
- 评估和预测土壤总碳 (TC) 在印度喀拉拉邦多样化的热带景观中的空间变化.
- 评估生物物理驱动因素,如海拔,土地表面温度 (LST) 和植被绿色 (NDVI) 对TC分布的影响.
- 为了比较TC映射的不同空间建模技术的预测精度.
主要方法:
- 多重线性回归被用来分析TC和生物物理驱动因素 (海拔,LST,NDVI) 之间的关系.
- 地理统计方法,包括普通Kriging (OK) 和实证贝叶斯式Kriging回归预测 (EBKRP),用于空间预测.
- 模型的性能使用诸如根平均平方误差 (RMSE) 和林的一致性相关系数 (CCC) 等指标进行了评估.
主要成果:
- 土壤总碳含量 (TC) 在整个景观中差异很大,森林和种植园的度更高,在田的度更低.
- 高度和土地表面温度 (LST) 与TC有显著的负相关性,而NDVI与TC有积极但不显著的关系.
- 结合LST和海拔的EBKRP显示出最高的预测准确度 (RMSE = 0.48,林的CCC = 0.87),表现优于IDW和OK.
结论:
- 整合LST和海拔等环境共变量对于在异质景观中准确地绘制土壤总碳 (TC) 是必不可少的.
- 实证贝叶斯式造回归预测 (EBKRP) 是一种适合于各种农业生态系统的土壤碳评估的方法.
- 地形,微气候和土地利用是影响热带环境中碳封存和分布的关键因素.
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