Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning
IntroductionSoil respiration (SR), the release of carbon dioxide (CO2) from soil due to the decomposition of organic matter and root respiration, is an important indicator for understanding agricultural carbon cycling and assessing anthropogenic impacts on the environment. Hyperspectral remote sensi...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2024.1505987/full |
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| author | Fanchao Zeng Fanchao Zeng Jinwei Sun Huihui Zhang Lizhen Yang Xiaoxue Zhao Jing Zhao Xiaodong Bo Yuxin Cao Fuqi Yao Fenghui Yuan Fenghui Yuan |
| author_facet | Fanchao Zeng Fanchao Zeng Jinwei Sun Huihui Zhang Lizhen Yang Xiaoxue Zhao Jing Zhao Xiaodong Bo Yuxin Cao Fuqi Yao Fenghui Yuan Fenghui Yuan |
| author_sort | Fanchao Zeng |
| collection | DOAJ |
| description | IntroductionSoil respiration (SR), the release of carbon dioxide (CO2) from soil due to the decomposition of organic matter and root respiration, is an important indicator for understanding agricultural carbon cycling and assessing anthropogenic impacts on the environment. Hyperspectral remote sensing offers a potential rapid, non-destructive approach for monitoring in agriculture. However, it remains uncertain whether hyperspectral remote sensing can provide an accurate and efficient method for estimating SR rate in croplands, particularly across different maize growth stages of under varying drought conditions.MethodsIn the study, we investigated the potential of combining hyperspectral remote sensing data with machine learning model (ML) to quantify SR rate in croplands. A drought field experiment was conducted, and SR and hyperspectral imagery were collected during four maize growth stages: Jointing Stage (JS), Tasseling Stage (TS), Flowering Stage (FS), and Grain Filling Stage (GFS). We compared the performance of traditional multiple linear regression (MLR) with that of an ML model (extreme gradient boosting, XGBoost), in simulating SR rate across these four growth stages.ResultsOur findings demonstrated that the simulation of the XGBoost model, utilizing soil temperature (Ts) and hyperspectral data, outperformed the MLR model. Across different growth stages, the SR simulated by the XGBoost model (R2 = 0.8103) was more reliable than that of the MLR model (R2 = 0.7451). The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. This study highlights the great promise of ML combined with hyperspectral imaging in predicting SR rate in croplands, which will help guide future agricultural management and environmental informatics. |
| format | Article |
| id | doaj-art-63334485c6d046d4bd6f2f1069376808 |
| institution | Kabale University |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-63334485c6d046d4bd6f2f10693768082025-01-07T06:40:39ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-01-011210.3389/fenvs.2024.15059871505987Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learningFanchao Zeng0Fanchao Zeng1Jinwei Sun2Huihui Zhang3Lizhen Yang4Xiaoxue Zhao5Jing Zhao6Xiaodong Bo7Yuxin Cao8Fuqi Yao9Fenghui Yuan10Fenghui Yuan11School of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaSchool of Resources and Environmental Engineering, Ludong University, Yantai, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaChangjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaSchool of Hydraulic and Civil Engineering, Ludong University, Yantai, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaDepartment of Soil, Water, and Climate, University of Minnesota, Saint Paul, MN, United StatesIntroductionSoil respiration (SR), the release of carbon dioxide (CO2) from soil due to the decomposition of organic matter and root respiration, is an important indicator for understanding agricultural carbon cycling and assessing anthropogenic impacts on the environment. Hyperspectral remote sensing offers a potential rapid, non-destructive approach for monitoring in agriculture. However, it remains uncertain whether hyperspectral remote sensing can provide an accurate and efficient method for estimating SR rate in croplands, particularly across different maize growth stages of under varying drought conditions.MethodsIn the study, we investigated the potential of combining hyperspectral remote sensing data with machine learning model (ML) to quantify SR rate in croplands. A drought field experiment was conducted, and SR and hyperspectral imagery were collected during four maize growth stages: Jointing Stage (JS), Tasseling Stage (TS), Flowering Stage (FS), and Grain Filling Stage (GFS). We compared the performance of traditional multiple linear regression (MLR) with that of an ML model (extreme gradient boosting, XGBoost), in simulating SR rate across these four growth stages.ResultsOur findings demonstrated that the simulation of the XGBoost model, utilizing soil temperature (Ts) and hyperspectral data, outperformed the MLR model. Across different growth stages, the SR simulated by the XGBoost model (R2 = 0.8103) was more reliable than that of the MLR model (R2 = 0.7451). The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. This study highlights the great promise of ML combined with hyperspectral imaging in predicting SR rate in croplands, which will help guide future agricultural management and environmental informatics.https://www.frontiersin.org/articles/10.3389/fenvs.2024.1505987/fullmachine learningsoil respirationmaizesoil temperaturehyperspectral image |
| spellingShingle | Fanchao Zeng Fanchao Zeng Jinwei Sun Huihui Zhang Lizhen Yang Xiaoxue Zhao Jing Zhao Xiaodong Bo Yuxin Cao Fuqi Yao Fenghui Yuan Fenghui Yuan Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning Frontiers in Environmental Science machine learning soil respiration maize soil temperature hyperspectral image |
| title | Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| title_full | Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| title_fullStr | Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| title_full_unstemmed | Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| title_short | Modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| title_sort | modeling soil respiration in summer maize cropland based on hyperspectral imagery and machine learning |
| topic | machine learning soil respiration maize soil temperature hyperspectral image |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2024.1505987/full |
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