The following 16 publications cited the product LBA-ECO CD-32 Flux Tower Network Data Compilation, Brazilian Amazon: 1999-2006.
| Year | Citation |
|---|---|
| 2023 | Zhang, J., J. Wu, A.C. Hughes, J.O. Kaplan, and E.E. Maeda. 2023. Bio-geophysical feedback to climate caused by the conversion of Amazon Forest to soybean plantations. Science of The Total Environment. 905:166802. https://doi.org/10.1016/j.scitotenv.2023.166802 |
| 2021 | Hashimoto, H., W. Wang, J.L. Dungan, S. Li, A.R. Michaelis, H. Takenaka, A. Higuchi, R.B. Myneni, and R.R. Nemani. 2021. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nature Communications. 12(1): https://doi.org/10.1038/s41467-021-20994-y |
| 2021 | Melo, D.C.D., J.A.A. Anache, V.P. Borges, D.G. Miralles, B. Martens, J.B. Fisher, R.L.B. Nobrega, A. Moreno, O.M.R. Cabral, T.R. Rodrigues, B. Bezerra, C.M.S. Silva, A.A.M. Neto, M.S.B. Moura, T.V. Marques, S. Campos, J.S. Nogueira, R. Rosolem, R.M.S. Souza, A.C.D. Antonino, D. Holl, M. Galleguillos, J.F. Perez-Quezada, A. Verhoef, L. Kutzbach, J.R.S. Lima, E.S. Souza, M.I. Gassman, C.F. Perez, N. Tonti, G. Posse, D. Rains, P.T.S. Oliveira, and E. Wendland. 2021. Are Remote Sensing Evapotranspiration Models Reliable Across South American Ecoregions?. Water Resources Research. 57(11): https://doi.org/10.1029/2020WR028752 |
| 2021 | Sakschewski, B., W. von Bloh, M. Druke, A.A. Sorensson, R. Ruscica, F. Langerwisch, M. Billing, S. Bereswill, M. Hirota, R.S. Oliveira, J. Heinke, and K. Thonicke. 2021. Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests. Biogeosciences. 18(13):4091-4116. https://doi.org/10.5194/bg-18-4091-2021 |
| 2020 | Laipelt, L., A.L. Ruhoff, A.S. Fleischmann, R.H.B. Kayser, E.d.M. Kich, H.R. da Rocha, and C.M.U. Neale. 2020. Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest-Savanna Transition in Brazil. Remote Sensing. 12(7):1108. https://doi.org/10.3390/rs12071108 |
| 2019 | Gomis-Cebolla, J., J.C. Jimenez, J.A. Sobrino, C. Corbari, and M. Mancini. 2019. Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests. International Journal of Applied Earth Observation and Geoinformation. 80:280-294. https://doi.org/10.1016/j.jag.2019.04.009 |
| 2019 | Haynes, K.D., I.T. Baker, A.S. Denning, S. Wolf, G. Wohlfahrt, G. Kiely, R.C. Minaya, and J.M. Haynes. 2019. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling. Journal of Advances in Modeling Earth Systems. 11(12):4440-4465. https://doi.org/10.1029/2018MS001541 |
| 2019 | Kivalov, S.N. and D.R. Fitzjarrald. 2019. Observing the Whole-Canopy Short-Term Dynamic Response to Natural Step Changes in Incident Light: Characteristics of Tropical and Temperate Forests. Boundary-Layer Meteorology. 173(1):1-52. https://doi.org/10.1007/s10546-019-00460-5 |
| 2018 | Liu, L., Q. Zhuang, Q. Zhu, S. Liu, H. van Asperen, and M. Pihlatie. 2018. Global soil consumption of atmospheric carbon monoxide: an analysis using a process-based biogeochemistry model. Atmospheric Chemistry and Physics. 18(11):7913-7931. https://doi.org/10.5194/acp-18-7913-2018 |
| 2018 | Spracklen, D.V., J.C.A. Baker, L. Garcia-Carreras, and J.H. Marsham. 2018. The Effects of Tropical Vegetation on Rainfall. Annual Review of Environment and Resources. 43(1):193-218. https://doi.org/10.1146/annurev-environ-102017-030136 |
| 2017 | de Oliveira, G., N.A. Brunsell, E.C. Moraes, Y.E. Shimabukuro, G. Bertani, T.V. dos Santos, and L.E.O.C. Aragao. 2017. Evaluation of MODIS-based estimates of water-use efficiency in Amazonia. International Journal of Remote Sensing. 38(19):5291-5309. https://doi.org/10.1080/01431161.2017.1339924 |
| 2017 | Numata, I., K. Khand, J. Kjaersgaard, M. Cochrane, and S. Silva. 2017. Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests. Remote Sensing. 9(1):46. https://doi.org/10.3390/rs9010046 |
| 2016 | Itterly, K.F., P.C. Taylor, J.B. Dodson, and A.B. Tawfik. 2016. On the sensitivity of the diurnal cycle in the Amazon to convective intensity. Journal of Geophysical Research: Atmospheres. 121(14):8186-8208. https://doi.org/10.1002/2016JD025039 |
| 2016 | Mallick, K., I. Trebs, E. Boegh, L. Giustarini, M. Schlerf, D.T. Drewry, L. Hoffmann, C. von Randow, B. Kruijt, A. Araujo, S. Saleska, J.R. Ehleringer, T.F. Domingues, J.P.H.B. Ometto, A.D. Nobre, O.L.L. de Moraes, M. Hayek, J.W. Munger, and S.C. Wofsy. 2016. Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin. Hydrology and Earth System Sciences. 20(10):4237-4264. https://doi.org/10.5194/hess-20-4237-2016 |
| 2015 | Anber, U., P. Gentine, S. Wang, and A.H. Sobel. 2015. Fog and rain in the Amazon. Proceedings of the National Academy of Sciences. 112(37):11473-11477. https://doi.org/10.1073/pnas.1505077112 |
| 2014 | Lange, S., B. Rockel, J. Volkholz, and B. Bookhagen. 2014. Regional climate model sensitivities to parametrizations of convection and non-precipitating subgrid-scale clouds over South America. Climate Dynamics. 44(9-10):2839-2857. https://doi.org/10.1007/s00382-014-2199-0 |