| 基于连续小波变换、SHAP和XGBoost的土壤有机质含量高光谱反演 |
| 摘要点击 877 全文点击 86 投稿时间：2023-04-12 修订日期：2023-06-25 |
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| 中文关键词 土壤有机质（SOM） 高光谱反演 连续小波变换 SHAP方法 XGBoost模型 |
| 英文关键词 soil organic matter（SOM） hyperspectral inversion continuous wavelet transform SHAP method XGBoost model |
| 作者 | 单位 | E-mail | | 叶淼 | 首都师范大学资源环境与旅游学院, 北京 100048
首都师范大学城市环境过程和数字模拟国家重点实验室培育基地, 北京 100048
首都师范大学水资源安全北京实验室, 北京 100048 | 2200901018@cnu.edu.cn | | 朱琳 | 首都师范大学资源环境与旅游学院, 北京 100048
首都师范大学城市环境过程和数字模拟国家重点实验室培育基地, 北京 100048
首都师范大学水资源安全北京实验室, 北京 100048 | lin.zhu@cnu.edu.cn | | 刘旭东 | 首都师范大学资源环境与旅游学院, 北京 100048
首都师范大学城市环境过程和数字模拟国家重点实验室培育基地, 北京 100048
首都师范大学水资源安全北京实验室, 北京 100048 | | | 黄勇 | 北京市生态地质研究所, 北京 100120 | | | 陈蓓蓓 | 首都师范大学资源环境与旅游学院, 北京 100048
首都师范大学城市环境过程和数字模拟国家重点实验室培育基地, 北京 100048
首都师范大学水资源安全北京实验室, 北京 100048 | | | 李欢 | 北京市生态地质研究所, 北京 100120 | |
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| 中文摘要 |
| 针对土壤有机质含量高光谱反演中存在的光谱有效信号薄弱和光谱信息冗余问题,提出结合连续小波变换、SHAP和XGBoost的土壤有机质含量高光谱反演框架.以北京市延庆区和房山区永久基本农田土壤为研究对象,首先,基于连续小波变换处理的土壤光谱反射率数据构建初始XGBoost模型；然后,利用SHAP方法分析模型中各波段的贡献度以筛选特征波段；最后,基于特征波段重新构建和优化XGBoost模型,实现土壤有机质含量高光谱反演.发现连续小波变换尺度为25时,利用SHAP方法选取的40个特征波段构建的XGBoost模型准确性最高,有机质含量反演值和实测值之间的R2为0.80,RMSE为3.60 g·kg-1；随着连续小波变换尺度的增大,R2呈现先升高后降低的趋势,25尺度下的R2比21尺度的高0.37；SHAP方法选取的特征波段比Pearson相关分析法少682个,RMSE低0.69 g·kg-1；XGBoost模型的R2分别比随机森林和支持向量机模型高4%和8%.验证了结合连续小波变换、SHAP和XGBoost在土壤有机质含量高光谱反演中的有效性,为快速、准确监测土壤有机质含量提供技术支撑. |
| 英文摘要 |
| Aiming to address the problems of weak spectral signals and redundant spectral information existing in hyperspectral inversion of soil organic matter content, a hyperspectral inversion framework combining continuous wavelet transform, SHAP, and XGBoost was proposed. Taking the permanent basic farmland soil in Yanqing District and Fangshan District of Beijing as the research object, an initial XGBoost model was first constructed based on the spectral reflectance data of soil processed by continuous wavelet transform. Then, the SHAP method was used to analyze the contribution of each band in the model to select the characteristic bands. Finally, the XGBoost model was reconstructed and optimized based on the characteristic bands to realize the hyperspectral inversion of soil organic matter content. It was found that the XGBoost model based on the 40 characteristic bands of continuous wavelet transform at the 25 scale selected by the SHAP method showed the highest accuracy, with the R2 and RMSE between the inversed and measured value of the organic matter content being 0.80 and 3.60 g·kg-1, respectively. The R2 first increased and then decreased with the increase in the scale of continuous wavelet transform, and the R2 at the 25 scale was 0.37 higher than that at the 21 scale. The number of characteristic bands selected by the SHAP method was 682 less than that by the Pearson correlation analysis method, and the RMSE was 0.69 g·kg-1 lower. The R2 of the XGBoost model was 4% and 8% higher than that of the random forest and support vector machine models, respectively. The results demonstrated the effectiveness of the combination of continuous wavelet transform, SHAP, and XGBoost in the hyperspectral inversion of soil organic matter content, which provides technical support for rapid and accurate monitoring of soil organic matter content. |
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