Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests
Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need t...
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Gespeichert in:
| Autor*in: |
Le Li [verfasserIn] Yaolong Zhao [verfasserIn] Yingchun Fu [verfasserIn] Qinchuan Xin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 10(2018), 7, p 1008 |
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| Übergeordnetes Werk: |
volume:10 ; year:2018 ; number:7, p 1008 |
| Links: |
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| DOI / URN: |
10.3390/rs10071008 |
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DOAJ014734141 |
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| 520 | |a Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. | ||
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10.3390/rs10071008 doi (DE-627)DOAJ014734141 (DE-599)DOAJ1f04fa49b30043ffb3d2dc139d5a2cce DE-627 ger DE-627 rakwb eng Le Li verfasserin aut Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. light use efficiency growing production day model comparison remote sensing carbon cycle Science Q Yaolong Zhao verfasserin aut Yingchun Fu verfasserin aut Qinchuan Xin verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 7, p 1008 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:7, p 1008 https://doi.org/10.3390/rs10071008 kostenfrei https://doaj.org/article/1f04fa49b30043ffb3d2dc139d5a2cce kostenfrei http://www.mdpi.com/2072-4292/10/7/1008 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 7, p 1008 |
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10.3390/rs10071008 doi (DE-627)DOAJ014734141 (DE-599)DOAJ1f04fa49b30043ffb3d2dc139d5a2cce DE-627 ger DE-627 rakwb eng Le Li verfasserin aut Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. light use efficiency growing production day model comparison remote sensing carbon cycle Science Q Yaolong Zhao verfasserin aut Yingchun Fu verfasserin aut Qinchuan Xin verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 7, p 1008 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:7, p 1008 https://doi.org/10.3390/rs10071008 kostenfrei https://doaj.org/article/1f04fa49b30043ffb3d2dc139d5a2cce kostenfrei http://www.mdpi.com/2072-4292/10/7/1008 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 7, p 1008 |
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10.3390/rs10071008 doi (DE-627)DOAJ014734141 (DE-599)DOAJ1f04fa49b30043ffb3d2dc139d5a2cce DE-627 ger DE-627 rakwb eng Le Li verfasserin aut Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. light use efficiency growing production day model comparison remote sensing carbon cycle Science Q Yaolong Zhao verfasserin aut Yingchun Fu verfasserin aut Qinchuan Xin verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 7, p 1008 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:7, p 1008 https://doi.org/10.3390/rs10071008 kostenfrei https://doaj.org/article/1f04fa49b30043ffb3d2dc139d5a2cce kostenfrei http://www.mdpi.com/2072-4292/10/7/1008 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 7, p 1008 |
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10.3390/rs10071008 doi (DE-627)DOAJ014734141 (DE-599)DOAJ1f04fa49b30043ffb3d2dc139d5a2cce DE-627 ger DE-627 rakwb eng Le Li verfasserin aut Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. light use efficiency growing production day model comparison remote sensing carbon cycle Science Q Yaolong Zhao verfasserin aut Yingchun Fu verfasserin aut Qinchuan Xin verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 7, p 1008 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:7, p 1008 https://doi.org/10.3390/rs10071008 kostenfrei https://doaj.org/article/1f04fa49b30043ffb3d2dc139d5a2cce kostenfrei http://www.mdpi.com/2072-4292/10/7/1008 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 7, p 1008 |
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Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests |
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Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. |
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Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. |
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Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. |
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