Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements
文献类型：期刊论文
| 作者 | Fu, DongJie(付东杰) ; Chen, BZ ; Zhang, HF ; Wang, J ; Black, TA ; Amiro, BD ; Bohrer, G ; Bolstad, P ; Coulter, R ; Rahman, AF ; Dunn, A ; McCaughey, JH ; Meyers, T ; Verma, S |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2014 |
| 卷号 | 141页码:90-104 |
| 关键词 | Net ecosystem exchange Eddy-covariance Regression tree Image fusion Footprint climatology Upscaling |
| 通讯作者 | Chen, BZ |
| 英文摘要 | More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM + data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial-temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30 m versus 1 km) (r(2) = 0.7548 vs. 0.5868, RMSE = 1.3979 vs. 1.7497 g C m-(2) day(-1), average error = 0.8950 vs. 1.0178 g C m(-2) day(-1), relative error = 0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial-temporal resolutions. |
| 收录类别 | SCI |
| 资助信息 | Chinese Ministry of Science and Technology 2010CB950704；IGSNRR, CAS 2012ZD010；National Science Foundation of China；Research Plan of LREIS, CAS O88RA900KA；"One Hundred Talents" program；Chinese Academy of Sciences；US Department of Energy DE-SC0006708；US National Science Foundation DEB-0911461；Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) ；Natural Science and Engineering Council of Canada (NSERC)；41071059；41271116 |
| 公开日期 | 2014-03-31 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/28740]  |
| 专题 | 地理科学与资源研究所_研究生部 |
| 推荐引用方式 GB/T 7714 | Fu, DongJie,Chen, BZ,Zhang, HF,et al. Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements[J]. REMOTE SENSING OF ENVIRONMENT,2014,141:90-104. |
| APA | Fu, DongJie.,Chen, BZ.,Zhang, HF.,Wang, J.,Black, TA.,...&Verma, S.(2014).Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements.REMOTE SENSING OF ENVIRONMENT,141,90-104. |
| MLA | Fu, DongJie,et al."Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements".REMOTE SENSING OF ENVIRONMENT 141(2014):90-104. |
入库方式： OAI收割
来源：地理科学与资源研究所
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