Publications

Fu, Dongjie; Chen, Baozhang; Zhang, Huifang; Wang, Juan; Black, T. Andy; Amiro, Brian D.; Bohrer, Gil; Bolstad, Paul; Coulter, Richard; Rahman, Abdullah F.; Dunn, Allison; McCaughey, J. Harry; Meyers, Tilden; Verma, Shashi (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.

Abstract
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.

DOI:
10.1016/j.rse.2013.10.029

ISSN:
0034-4257; 1879-0704