Sadegh Ranjbar, Daniele Losos, Sophie Hoffman, Matthias Cuntz, Paul C. Stoy
{"title":"利用地球静止卫星观测数据和机器学习模型估算涡动协方差站点每半小时时间步长的生态系统碳吸收和呼吸量","authors":"Sadegh Ranjbar, Daniele Losos, Sophie Hoffman, Matthias Cuntz, Paul C. Stoy","doi":"10.1029/2024MS004341","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <p>Polar-orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub-daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5-min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES-R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES-R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree-based ensemble regressions showed promising performance for predicting GPP (R<sup>2</sup> of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m<sup>−2</sup> s<sup>−1</sup>) and RECO (R<sup>2</sup> of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m<sup>−2</sup> s<sup>−1</sup>) on a half-hourly time step using GOES-R surface products and top-of-atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near-real time.</p>\n </section>\n </div>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 10","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004341","citationCount":"0","resultStr":"{\"title\":\"Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites\",\"authors\":\"Sadegh Ranjbar, Daniele Losos, Sophie Hoffman, Matthias Cuntz, Paul C. Stoy\",\"doi\":\"10.1029/2024MS004341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <p>Polar-orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub-daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5-min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES-R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES-R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree-based ensemble regressions showed promising performance for predicting GPP (R<sup>2</sup> of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m<sup>−2</sup> s<sup>−1</sup>) and RECO (R<sup>2</sup> of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m<sup>−2</sup> s<sup>−1</sup>) on a half-hourly time step using GOES-R surface products and top-of-atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near-real time.</p>\\n </section>\\n </div>\",\"PeriodicalId\":14881,\"journal\":{\"name\":\"Journal of Advances in Modeling Earth Systems\",\"volume\":\"16 10\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004341\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advances in Modeling Earth Systems\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1029/2024MS004341\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Modeling Earth Systems","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024MS004341","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Using Geostationary Satellite Observations and Machine Learning Models to Estimate Ecosystem Carbon Uptake and Respiration at Half Hourly Time Steps at Eddy Covariance Sites
Polar-orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub-daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5-min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES-R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES-R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree-based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half-hourly time step using GOES-R surface products and top-of-atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near-real time.
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