Monitoring carbon assimilation in South America's tropical forests: Model specification and application to the Amazonian droughts of 2005 and 2010

Nunes, Edson Luís; Costa, Marcos Heil; Malhado, Ana C. M.; Dias, Lívia Cristina Pinto; Vieira, Simone A.; Pinto, Luiz Fernando Spinelli; Ladle, Richard J.

doi:10.1016/j.rse.2011.10.022

Cited by 17 publications

(13 citation statements)

References 35 publications

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“…The Fraction of Photosynthetically Active Radiation (FPAR) is an important index for detecting the vegetation water, energy and carbon balance and is a key parameter in the ecosystem productivity model, crop yield model, and other models [1][2][3][4][5]. FPAR is most often defined as the proportion of available photosynthetically active radiation absorbed by the green vegetation canopy in the specific spectrum of 400-700 nm.…”

Section: Introductionmentioning

confidence: 99%

Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel

Yang

Ren

et al. 2014

Remote Sensing

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Fraction of Photosynthetically Active Radiation (FPAR) is a critical parameter in land surface energy balance and climate modeling. Several global FPAR products are available, but these still require considerable assessment and validation due to low spatial resolution. Three major FPAR products that have covered China and provided continuous time series data-MODIS, MERIS and GEOV1-were assessed from 2006-2010. Based on the ground measurement data, the accuracies of these three FPAR products were directly validated for maize and winter wheat over northern China. This investigation also assessed the consistencies among the three FPAR products, and analyzed the residential area in mixed pixels effect on the FPAR products accuracy, at each of the main growth stages of maize and winter wheat. The GEOV1 FPAR product was found to be the most accurate with regression R 2 values of 0.818 and 0.655 for ground measured maize and winter wheat FPAR. The maize FPAR data were generally more accurate than the winter wheat FPAR data. The MODIS, MERIS and GEOV1 products all indicated that FPAR variations among the growth stages differed from year to year. The scattered residential areas in mixed pixels were found to significantly affect the FPAR data uncertainties, and these were also analyzed in detail. The effect of residential area percentage in mixed pixels on FPAR values differed for different crops, and this was not necessarily in accordance 5429 with the FPAR product accuracy. For the mixed pixels, a quadratic polynomial was able to fit the residential area and FPAR data reasonably well with R 2 values higher than 0.9 for most relationships. Quadratic polynomial fitting may provide a simple and convenient method to assess and reduce the residential area effect on FPAR in the mixed pixels.

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Section: Introductionmentioning

confidence: 99%

Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel

Yang

Ren

et al. 2014

Remote Sensing

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“…The simulated NPP in the central part of the Amazon is approximately 0.6~1.2 kg C m À2 yr À1 , which is smaller than the estimations derived from MODIS and IGBP data. It is also smaller than estimates about 1.15 kg C m À2 yr À1 in Senna et al [2009] and 1.273~1.350 kg C m À2 yr À1 in Nunes et al [2012]. This discrepancy is probably caused by insufficient precipitation in the Amazon simulated in BCC_CSM1.1.…”

Section: Geographical Distributionmentioning

confidence: 67%

“…It is also smaller than estimates about 1.15 kg C m −2 yr −1 in Senna et al [2009] and 1.273 ~ 1.350 kg C m −2 yr −1 in Nunes et al . []. This discrepancy is probably caused by insufficient precipitation in the Amazon simulated in BCC_CSM1.1.…”

Section: Resultsmentioning

confidence: 99%

Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century

et al. 2013

JGR Atmospheres

231

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[1] The paper examines terrestrial and oceanic carbon budgets from preindustrial time to present day in the version of Beijing Climate Center Climate System Model (BCC_CSM1.1) which is a global fully coupled climate-carbon cycle model. Atmospheric CO 2 concentration is calculated from a prognostic equation taking into account global anthropogenic CO 2 emissions and the interactive CO 2 exchanges of land-atmosphere and ocean-atmosphere. When forced by prescribed historical emissions of CO 2 from combustion of fossil fuels and land use change, BCC_CSM1.1 can reproduce the trends of observed atmospheric CO 2 concentration and global surface air temperature from 1850 to 2005. Simulated interannual variability and long-term trend of global carbon sources and sinks and their spatial patterns generally agree with other model estimates and observations, which shows the following: (1) Both land and ocean in the last century act as net carbon sinks. The ability of carbon uptake by land and ocean is enhanced at the end of last century. (2) Interannual variability of the global atmospheric CO 2 concentration is closely correlated with the El Niño-Southern Oscillation cycle, in agreement with observations. (3) Interannual variation of the land-to-atmosphere net carbon flux is positively correlated with surface air temperature while negatively correlated with soil moisture over low and midlatitudes. The relative contribution of soil moisture to the interannual variation of land-atmosphere CO 2 exchange is more important than that of air temperature over tropical regions, while surface air temperature is more important than soil moisture over other regions of the globe.Citation: Wu, T., et al. (2013), Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century,

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“…Another uncertainty emerges in the both empirical-and process-based models when they link remotely sensed data with the field investigation. However, these empirical models usually have a relatively higher accuracy (Huang et al 2010), compare with the process models (Turner et al 2003(Turner et al , 2006Qin et al 2008;Nunes et al 2012). …”

Section: Uncertainties In Drought Monitoring By Remote Sensingmentioning

confidence: 99%

Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges

et al. 2013

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Alongside global warming, droughts are expected to increase in frequency, severity, and extent in the near future, which will likely result in significant impacts on forest growth, production, structure, composition, and ecosystem services. However, due to spatial and temporal characteristics, it is difficult to monitor and assess the potential effects of droughts. Remote sensing can provide an effective way to obtain real-time conditions of forests affected by drought and offer a range of spatial and temporal insights into drought-induced changes to forest ecosystem structure, function, and services. Remote sensing is rapidly developing as more satellites are launched. In situ and remotely sensed data fusion techniques have achieved notable success in assessing drought-induced damage to forests and carbon cycles. Even so, constraints still exist when using satellite data. The objectives of this review are to (1) briefly review existing data sources and methods of remote sensing;(2) synthesize current applications and contributions of remote sensing in monitoring and estimating impacts of droughts on forest ecosystems; and (3) highlight research gaps and future challenges.Résumé : Avec le réchauffement global, on s'attend à ce que la fréquence, la sévérité et l'étendue des sécheresses augment dans un futur rapproché, conduisant vraisemblablement à des impacts significatifs sur la croissance, la production, la structure, la composition et les services des écosystèmes forestiers. Cependant, compte tenu des caractéristiques spatiales et temporelles, il est difficile de faire le suivi et d'évaluer les effets potentiels des sécheresses. La télédétection peut fournir un moyen efficace pour obtenir en temps réel les conditions des forêts affectées par la sécheresse et d'offrir un ensemble de perceptions spatiales et temporelles des changements induits par la sécheresse dans la structure, la fonction et les services des écosystèmes forestiers. La télédétection se développe rapidement avec le lancement de nouveaux satellites. Les techniques de fusion des données satellites et in situ ont connu un succès remarquable dans l'évaluation des dommages induits par la sécheresse sur les forêts et les cycles du carbone. Tout de même, il existe toujours des contraintes dans l'utilisation des données satellites. Les objectifs de cette revue consistaient à (1) explorer rapidement les sources de données existantes et les méthodes de la télédétection; (2) synthétiser les applications courantes et les contributions de la télédétection dans le suivi et l'estimation des impacts des sécheresses sur les écosystèmes forestiers; et (3) souligner les lacunes et les défis à venir. [Traduit par la Rédaction]

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Monitoring carbon assimilation in South America's tropical forests: Model specification and application to the Amazonian droughts of 2005 and 2010

Cited by 17 publications

References 35 publications

Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel

Assessment of MODIS, MERIS, GEOV1 FPAR Products over Northern China with Ground Measured Data and by Analyzing Residential Effect in Mixed Pixel

Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century

Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges

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