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区域土壤盐度分析使用阶段式M5决策树

Khalil Ghorbani¹, Soraya Bandak², Laleh Rezaei Ghaleh³

¹Department of Water and Soil Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. ghorbani.khalil@gau.ac.ir.

BMC research notes

|March 3, 2025

概括

多谱卫星图像显示土壤盐度评估的前景. M5决策树模型的表现明显优于线性回归,对估计电导率 (EC) 的精度提高了37.18%.

科学领域:

地球和环境科学地球和环境科学
遥感遥感遥感遥感
土壤科学土壤科学

背景情况:

土壤盐度是全球农业生产力的重大威胁.
准确有效地评估土壤盐度对于可持续的土地管理至关重要.
传统的土壤盐度评估方法往往是耗时和劳动密集的.

研究的目的:

评估多光谱卫星图像对土壤盐度评估的有效性.
为了比较线性复数回归和M5决策树回归模型的性能.
确定用于估计土壤电导率 (EC) 的关键光谱指数.

主要方法:

采集和分析了96个土壤样本.
土壤样本与15个独立光谱变量和Landsat 8指数的相关性.
应用线性复数回归和M5决策树回归技术.

主要成果:

由于非线性关系,线性回归产生了不满意的结果 (最高R2 = 58%,RMSE = 0.78).
通过估计EC的自然对数,M5决策树回归实现了更高的相关系数 (73%) 和更低的RMSE (0.29).
确定B64,NDII和S2指数是最有影响力的光谱指数.
在多变量线性回归中,M5模型的准确性比多变量线性回归提高了37.18%.

结论:

与线性回归相比,M5决策树回归是使用多谱卫星数据进行土壤盐度评估的更有效方法.
来自卫星图像的光谱指数可以为估计土壤电导率提供有价值的信息.
诸如植被覆盖,土壤湿度和采样不一致等因素可能会影响基于遥感的盐度评估的准确性.

关键词:

决策树决策树是一个决策树.欧洲共同体欧洲共同体欧洲共同体机器学习是机器学习.遥感是一种远程传感.土壤盐度土壤盐度

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