Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements
Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements
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基于 Landsat 数据、改进的升级模型框架和涡流协方差通量测量来估计高时空分辨率下的景观网络生态系统交换
DOI：
10.1016/j.rse.2013.10.029
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发表时间：
2014-02-05
影响因子：
13.5
通讯作者：
Verma, Shashi
中科院分区：
文献类型：
--
作者：
Fu, Dongjie;Chen, Baozhang;Verma, Shashi
对二氧化碳通量的更准确估计取决于对陆地碳循环的更好的科学理解。基于遥感的方法来估计净生态系统交换（NEE）的大陆尺度已经开发出来，但粗糙的空间分辨率是一个错误的来源。在这里，我们展示了一个基于卫星的方法估计NEE使用Landsat TM/ETM +数据和一个升级框架。尺度升级框架包括通量足迹气候学建模、改进的回归树（MRT）分析和图像融合。通过将通量塔测量的NEE缩放到景观和区域尺度，相对于基于具有较粗时空分辨率的MODIS数据的方法，这种基于卫星的方法可以在景观尺度上提高高时空分辨率的NEE估计。这种方法被应用到16个通量网站从加拿大碳计划和AmeriFlux网络位于北美，覆盖森林，草地和农田生物群落。与利用MODIS数据的类似方法相比，在相同的时间分辨率和更高的空间分辨率下，我们的估计方法对诊断景观NEE更有效（30 m vs. 1 km）（r（2）= 0.7548 vs. 0.5868，RMSE = 1.3979 vs. 1.7497 g Cm-2 day（-1），平均误差= 0.8950 vs. 1.0178 g Cm-2 day（-1），Landsat和MODIS融合图像的相对误差分别为0.47和0.54）。我们还比较了区域NEE估计使用碳跟踪，我们的方法和涡度协方差观测。本研究表明，数据驱动的基于卫星的NEE诊断模型可以用来升级的涡动通量观测景观尺度与高时空分辨率。(C)2013 Elsevier Inc. All rights reserved.
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.