Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and v...

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Vydáno v:	The Science of the total environment Ročník 944; s. 173887
Hlavní autoři:	Gao, Dexiang, Yao, Jingyu, Gao, Zhongming, Yuan, Wenping, He, Yingzhe, Wang, Bojun, Li, Lei, Ma, Yulong, Russell, Eric, Pressley, Shelley N., Zou, Xudong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 20.09.2024
Témata:	algorithms carbon dioxide Carbon flux upscaling climate cropland Eddy covariance environment image analysis Machine learning Net ecosystem exchange Object-oriented image analysis phenology prediction soil water temperature Net ecosystem exchange Eddy covariance Carbon flux upscaling Object-oriented image analysis Machine learning
ISSN:	0048-9697, 1879-1026, 1879-1026
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Abstract	Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties. [Display omitted] •Evaluated the performance of an object-based integrated machine learning framework for upscaling CO2 fluxes in croplands•Explored key drivers affecting NEE prediction accuracy in conjunction with interpretable machine learning SHAP models•Generated maps of upscaled NEE and associated uncertainties for croplands in the study areas
AbstractList	Accurately estimating the net ecosystem exchange of CO₂ (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R² value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties. Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties. Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties. [Display omitted] •Evaluated the performance of an object-based integrated machine learning framework for upscaling CO2 fluxes in croplands•Explored key drivers affecting NEE prediction accuracy in conjunction with interpretable machine learning SHAP models•Generated maps of upscaled NEE and associated uncertainties for croplands in the study areas
ArticleNumber	173887
Author	Yao, Jingyu Yuan, Wenping Ma, Yulong Gao, Dexiang Russell, Eric He, Yingzhe Pressley, Shelley N. Gao, Zhongming Li, Lei Zou, Xudong Wang, Bojun
Author_xml	– sequence: 1 givenname: Dexiang surname: Gao fullname: Gao, Dexiang organization: School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, China – sequence: 2 givenname: Jingyu surname: Yao fullname: Yao, Jingyu organization: School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, China – sequence: 3 givenname: Zhongming surname: Gao fullname: Gao, Zhongming email: gaozhm3@mail.sysu.edu.cn organization: School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, China – sequence: 4 givenname: Wenping surname: Yuan fullname: Yuan, Wenping organization: Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100091, China – sequence: 5 givenname: Yingzhe surname: He fullname: He, Yingzhe organization: School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong 510006, China – sequence: 6 givenname: Bojun surname: Wang fullname: Wang, Bojun organization: School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, Guangdong 519082, China – sequence: 7 givenname: Lei surname: Li fullname: Li, Lei organization: School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, China – sequence: 8 givenname: Yulong surname: Ma fullname: Ma, Yulong organization: Guangdong-Hong kong-Macau Greater Bay Area Weather Research Center for Monitoring Warning and Forecasting (Shenzhen Institute of Meteorological Innovation), Shenzhen, Guangdong 518040, China – sequence: 9 givenname: Eric surname: Russell fullname: Russell, Eric organization: Laboratory for Atmospheric Research, Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington 99163, USA – sequence: 10 givenname: Shelley N. surname: Pressley fullname: Pressley, Shelley N. organization: Laboratory for Atmospheric Research, Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington 99163, USA – sequence: 11 givenname: Xudong surname: Zou fullname: Zou, Xudong organization: Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
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Snippet	Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and... Accurately estimating the net ecosystem exchange of CO₂ (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and...
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Title	Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches
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