数字农科院2.0

基于土壤高光谱特征波段优选的盐分预测模型构建

文献类型： 中文期刊

关键词： 土壤盐分;;光谱变换;;特征波段选择;;相关性分析;;最小角回归;;竞争性自适应重加权采样

期刊名称：中国农业科学

ISSN： 0578-1752

年卷期： 2025 年

页码：

收录情况： 北大核心（2023版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）

摘要： 【目的】土壤盐渍化导致土壤结构恶化、作物减产及生态系统退化，是威胁干旱区农业可持续发展的关键环境问题。本研究旨在利用光谱变换、波段筛选以及多种机器学习方法，构建土壤盐分预测模型，快速、准确地预测土壤盐分，为盐渍化农田的科学管理提供技术支撑。【方法】以达拉特旗农田土壤为研究对象，系统采集土壤样品并测定其电导率（EC）与光谱反射率数据。首先，采用 Savitzky-Golay（S-G）滤波对原始光谱（R）进行平滑去噪，在此基础上系统开展包括倒数、对数、一阶微分、二阶微分等在内的 12 种光谱变换处理，以挖掘隐含光谱特征。进而，分别采用相关性分析（CA）和最小角回归（LAR）方法进行特征降维，并结合竞争性自适应重加权采样（CARS）算法进一步筛选敏感特征波段。最后，基于优选特征分别构建偏最小二乘回归（PLSR）、支持向量机（SVM）、反向传播神经网络（BPNN）和随机森林（RF）模型，通过决定系数（R2）和均方根误差（RMSE）综合评价模型性能，对比特征集在不同算法中的建模效果。【结果】原始光谱经过光 谱变换后，相关系数均有不同程度的提升，表明光谱变换能显著增强土壤盐分与光谱特征的相关性；在使用竞争性自适应重加权采样进行特征波段优选时，最小角回归比相关性分析具有更好的特征降维效果；倒数对数一阶微分（ATFD）结合偏最小二乘回归模型表现最优，其验证集精度为 R2=0.81、RMSE=2.04 dS·m-1；不同建模方法对比显示，偏最小二乘回归模型的预测性能优于其他 3 种模型（反向传播神经网络/随机森林/支持向量机），表明偏最小二乘回归模型更适合该区域土壤盐分的预测。【结论】基于 ATFD-LAR-CARS-PLSR 的土壤盐分高光谱预测模型精度高、预测能力最优，证实了高光谱技术结合多维度特征优化可有效实现干旱区土壤盐分预测。

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