Index Catalog // ScholarsArchive@OSU

Creator:: Verma, M., Friedl, M. A., Richardson, A. D., Kiely, G., Cescatti, A., Law, B. E., Wohlfahrt, G., Gielen, B., Roupsard, O., Moors, E. J., Toscano, P., Vaccari, P., Gianelle, D., Bohrer, G., Varlagin, A., Buchmann, N., van Gorsel, E., Montagnani, L., and Propastin, P.
Abstract:: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and...

Creator:: Verma, M., Friedl, M. A., Richardson, A. D., Kiely, G., Cescatti, A., Law, B. E., Wohlfahrt, G., Gielen, B., Roupsard, O., Moors, E. J., Toscano, P., Vaccari, P., Gianelle, D., Bohrer, G., Varlagin, A., Buchmann, N., van Gorsel, E., Montagnani, L., and Propastin, P.
Abstract:: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and...

Creator:: Verma, M., Friedl, M. A., Richardson, A. D., Kiely, G., Cescatti, A., Law, B. E., Wohlfahrt, G., Gielen, B., Roupsard, O., Moors, E. J., Toscano, P., Vaccari, P., Gianelle, D., Bohrer, G., Varlagin, A., Buchmann, N., van Gorsel, E., Montagnani, L., and Propastin, P.
Abstract:: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and...
Resource Type:: Article

Creator:: Barr, A. G., Richardson, A. D., Hollinger, D. Y., Papale, D., Arain, M. A., Black, T. A., Bohrer, G., Dragoni, D., Fischer, M. L., Gu, L., Law, B. E., Margolis, H. A., McCaughey, J. H., Munger, J. W., Oechel, W., and Schaeffer, K.
Abstract:: The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction-velocity (u*) threshold filters (u*[superscript Th]). This study modifies an accepted...
Resource Type:: Article

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