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Soil Organic Matter Estimation Model Integrating Spectral and Profile Features

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成果类型:
期刊论文
作者:
He, Shaofang;Tan, Siqiao;Shen, Luming;Zhou, Qing
通讯作者:
He, SF
作者机构:
[Tan, Siqiao; Shen, Luming; He, Shaofang; He, SF] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
[Zhou, Qing] Hunan Agr Univ, Coll Resources, Changsha 410128, Peoples R China.
通讯机构:
[He, SF ] H
Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Peoples R China.
语种:
英文
关键词:
fusion feature;PCA;lasso feature selection;SCARS
期刊:
Sensors
ISSN:
1424-3210
年:
2023
卷:
23
期:
24
页码:
9868-
基金类别:
Conceptualization, S.H.; methodology, S.H.; software, S.T.; validation, S.H.; investigation, Q.Z.; resources, Q.Z.; data curation, L.S.; writing—original draft preparation, S.H.; writing—review and editing, S.H.; visualization, S.T.; supervision, Q.Z.; project administration, L.S.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Hunan Province, grant number 2023JJ30304, the Scientific Research Program of Hunan Province Department of Education, grant number 23A0197, the National Science and Technology Basic Work Special Project, grant number 2014FY110200, and the Changsha Soft Science Research Program, grant number kh2302050.
机构署名:
本校为第一且通讯机构
摘要:
The accurate measurement of soil organic matter (SOM) is vital for maintaining soil quality. We present an innovative model for SOM prediction by integrating spectral and profile features. We use PCA, Lasso, and SCARS methods to extract important spectral features and combine them with profile data. This hybrid approach significantly improves SOM prediction across various models, including Random Forest, ExtraTrees, and XGBoost, boosting the coefficient of determination (R2) by up to 26%. Notably, the ExtraTrees model, enriched with PCA-extracted features, achieves the highest accuracy with an...

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