Assessing the representativeness of the AmeriFlux network using MODIS and GOES data

Yang, Fan; Zhu, A‐Xing; Ichii, Kazuhito; White, Michael A.; Hashimoto, Hirofumi; Nemani, Ramakrishna R.

doi:10.1029/2007jg000627

Cited by 28 publications

(27 citation statements)

References 23 publications

(40 reference statements)

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“…An analysis of the monthly variations of the environmental variables revealed that the EVI had the largest variation, which is in accordance with the findings of Yang et al (2008). The great uncertainties in the EVI measurements indicated the potential of under-representation of the flux towers in terms of the vegetation types.…”

Section: Representativeness Of Different Vegetation Typessupporting

confidence: 86%

“…The enhanced vegetation index (EVI) was selected because the vegetation physiological properties have an important effect on carbon exchange (Yang et al, 2008). The EVI is less sensitive to soil and atmospheric effects than the normalized difference vegetation index (NDVI) because it incorporates the blue spectral band.…”

Section: Selection Of the Environmental Variablesmentioning

confidence: 99%

“…We estimated the representativeness of the eddy covariance network using the Euclidean distance between each pixel and tower in the attribute space, following the method of Yang et al (2008). The Euclidean distance is defined as the shortest straight line distance between two points, in this case, the distance between the studied tower and the coterminous pixel in the 4-dimensional environmental space (Yang et al, 2008).…”

Section: Spatial Representativenessmentioning

confidence: 99%

“…The Euclidean distance is defined as the shortest straight line distance between two points, in this case, the distance between the studied tower and the coterminous pixel in the 4-dimensional environmental space (Yang et al, 2008). First, the monthly environment matrix over the period of 2005-2010 was standardized to zero mean and unit variance.…”

Section: Spatial Representativenessmentioning

confidence: 99%

“…The second approach directly calculates the Euclidean distances between the pixels and the tower sites. Recently, this approach has been used in many previous investigations to determine the network representativeness at a range of spatial and temporal scales (Carvalhais et al, 2010;Xiao et al, 2011;Yang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Regional representativeness assessment and improvement of eddy flux observations in China

Zhang

Gao

et al. 2015

Science of The Total Environment

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• The network of flux towers performed well in representing the environment of more than half of China.• The towers in wetlands and barelands were poorly represented.• Representativeness increased with towers added in forests, grasslands, wetlands and barelands.• Substantial gains in representation were achieved by adding new towers on the Tibet Plateau. Both the amounts of data describing the site-scale carbon flux at a high temporal and spatial resolution collected in China and the number of eddy covariance flux towers have been increasing during the last decade. To correctly upscale these fluxes to the regional and global level, the representativeness of the current network of flux towers must be known. The present study quantifies the representativeness of the flux network for the regional carbon exchange. This analysis combined the total solar radiation, air temperature, vapor pressure and the enhanced vegetation index to indicate the environmental characteristics of each 1-km pixel cell and flux tower. Next, the Euclidean distance from each pixel to the tower was calculated to determine the representativeness of the existing flux towers. To improve the regional representativeness, additional tower locations were pinpointed by identifying and clustering the underrepresented areas. The existing network of flux towers performed well in representing the environmental conditions of the middle and the northeastern portions of China. The wellrepresented areas covered 60.9% of the total areas. The towers in croplands and grasslands represented the vegetation types well, but the wetlands and barelands were poorly represented. The representativeness of the flux network increased with the addition of nine towers located in forests, grasslands, wetlands and barelands. The representativeness of 27.5% of the land areas improved. In addition, the well-represented areas were enlarged by 15.2%. Substantial gains in representation were achieved by adding new towers on the Tibet Plateau. The representativeness of the northwest and southwest was improved less significantly, suggesting that more towers are required to capture certain ecosystem behaviors. a b s t r a c t a r t i c l e i n f o

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Section: Representativeness Of Different Vegetation Typessupporting

confidence: 86%

Section: Selection Of the Environmental Variablesmentioning

confidence: 99%

Section: Spatial Representativenessmentioning

confidence: 99%

Section: Spatial Representativenessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regional representativeness assessment and improvement of eddy flux observations in China

Zhang

Gao

et al. 2015

Science of The Total Environment

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Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO₂ exchanges: GOSAT CO₂ inversion and empirical eddy flux upscaling

et al. 2015

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We examined the consistency between terrestrial biosphere fluxes (terrestrial CO 2 exchanges) from data-driven top-down (GOSAT CO 2 inversion) and bottom-up (empirical eddy flux upscaling based on a support vector regression (SVR) model) approaches over 42 global terrestrial regions from June 2009 to October 2011. Seasonal variations of the biosphere fluxes by the two approaches agreed well in boreal and temperate regions across the Northern Hemisphere. Both fluxes also exhibited strong anomalous signals in response to contrasting anomalous spring temperatures observed in North America and boreal Eurasia. This indicates that the CO 2 concentration data integrated in the GOSAT inversion and the meteorological and vegetation data in the SVR models are equally effective in producing spatiotemporal variations of biosphere flux. Meanwhile, large differences in seasonality were found in subtropical and tropical South America, South Asia, and Africa. The GOSAT inversion showed seasonal variations that pivoted around CO 2 neutral, while the SVR model showed seasonal variations that tended toward CO 2 sink. Thus, a large difference in CO 2 budget was identified between the two approaches in subtropical and tropical regions across the Southern Hemisphere. Examination of the integrated data revealed that the large tropical sink of CO 2 by the SVR model was an artifact due to the underrepresented biosphere fluxes predicted by limited eddy flux data for tropical biomes. Because of the global coverage of CO 2 concentration data, the GOSAT inversion provides better estimates of continental CO 2 flux than the SVR model in the Southern Hemisphere.

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Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

et al. 2022

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Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local‐to‐global scales. Here, we provide a quantitative assessment of the three‐dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest‐regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5‐km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite‐derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three‐dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.

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Assessing the representativeness of the AmeriFlux network using MODIS and GOES data

Cited by 28 publications

References 23 publications

Regional representativeness assessment and improvement of eddy flux observations in China

Regional representativeness assessment and improvement of eddy flux observations in China

Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO₂ exchanges: GOSAT CO₂ inversion and empirical eddy flux upscaling

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

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Assessing the representativeness of the AmeriFlux network using MODIS and GOES data

Cited by 28 publications

References 23 publications

Regional representativeness assessment and improvement of eddy flux observations in China

Regional representativeness assessment and improvement of eddy flux observations in China

Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO2 exchanges: GOSAT CO2 inversion and empirical eddy flux upscaling

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

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Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO₂ exchanges: GOSAT CO₂ inversion and empirical eddy flux upscaling