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：SCI/SSCI论文
| 作者 | Wang J.; Wang J. |
| 发表日期 | 2014 |
| 关键词 | Net ecosystem exchange Eddy-covariance Regression tree Image fusion Footprint climatology Upscaling carbon-dioxide exchange principal component analysis gross primary production difference water index cloud-cover assessment leaf-area index image fusion vegetation indexes interannual variability surface-temperature |
| 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. (C) 2013 Elsevier Inc. All rights reserved. |
| 出处 | Remote Sensing of Environment |
| 卷 | 141 |
| 页 | 90-104 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0034-4257 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/29889]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Wang J.,Wang J.. Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements. 2014. |
入库方式： OAI收割
来源：地理科学与资源研究所
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