Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation

Papale, Dario; Reichstein, Markus; Aubinet, Marc; Canfora, E.; Bernhofer, Christian; Kutsch, Werner L.; Longdoz, Bernard; Rambal, Serge; Валентини, Р.; Vesala, Timo; Yakir, Dan

doi:10.5194/bg-3-571-2006

Cited by 1,404 publications

(1,088 citation statements)

References 36 publications

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“…We specifically use the estimated GPP product using the nighttime respiration partitioning method (Papale et al, 2017). Furthermore, data were filtered using procedures used in previous studies (Zhou et al, 2015).…”

Section: Datamentioning

confidence: 99%

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

Gentine

Alemohammad

2018

Geophysical Research Letters

108

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Solar‐induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The resulting product is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh). Multiplying this new product with a MODIS estimate of photosynthetically active radiation provides a new MODIS‐only reconstruction of SIF called Reconstructed SIF (RSIF). RSIF exhibits much higher seasonal and interannual correlation than the original SIF when compared with eddy covariance estimates of GPP and two reference global GPP products, especially in dry and cold regions. RSIF also reproduces intense productivity regions such as the U.S. Corn Belt contrary to typical vegetation indices and similarly to SIF.

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Section: Datamentioning

confidence: 99%

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

Gentine

Alemohammad

2018

Geophysical Research Letters

108

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“…Since the 1990s, the eddy covariance method has been used as an important tool to measure heat, water, and CO 2 exchanges as well as trace green-house gases (Baldocchi, 2014). The observed net ecosystem CO 2 exchange (NEE) at the ecosystem scale is partitioned into GPP and ecosystem respiration (R e , including both autotrophic and heterotrophic respiration components) (Desai et al, 2008;Papale et al, 2006;Reichstein et al, 2005). However, due to the limited number of flux tower sites and their footprints, estimation of GPP at the regional and global scales still relies on model simulation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

107

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Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

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“…However, its implementation is complex because the study site should be in a relatively flat terrain and be homogeneous in terms of vegetation and soil. The installation and operation of instruments, as well as processing and data analysis require special attention for correct results representing the ecosystem (Papale et al, 2006).…”

Section: Eddy Covariance (Ec) Techniquementioning

confidence: 99%

“…In order to scale these measurements in time and space, it is essential to: (1) generate a standardized protocol for inter-calibration of measuring instruments and measurement methods; and (2) develop quality assurance and control (QA/ QC) procedures of raw data and post-processing of computed fluxes (i.e., gap-filling series, partition NEE into GPP and RS). This QA/QC must comply with international standards (Papale et al, 2006) to generate useful and consistent datasets for regional and global synthesis studies.…”

Section: Mexflux Short-and Long-term Goalsmentioning

confidence: 99%

Progress and opportunities for monitoring greenhouse gases fluxes in Mexican ecosystems: the MexFlux network

Vargas¹,

Yépez²,

Andrade³

et al. 2013

Atmósfera

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RESUMENPara entender los procesos de los ecosistemas desde un punto de vista funcional es fundamental entender las relaciones entre la variabilidad climática, los ciclos biogeoquímicos y las interacciones superficie-atmósfera. En las últimas décadas se ha aplicado de manera creciente el método de covarianza de flujos turbulentos (EC, por sus siglas en inglés) en ecosistemas terrestres, marinos y urbanos para medir los flujos de gases de invernadero (p. ej., CO 2 , H 2 O ) y energía (p. ej., calor sensible y latente). En diversas regiones se han establecido redes de sistemas EC que han aportado información científica para el diseño de políticas ambientales y de adaptación. En este contexto, el presente trabajo delimita el marco conceptual y técnico para el establecimiento de una red regional de medición de flujos de gases de efecto invernadero en México, denominada MexFlux, cuyo objetivo principal es mejorar nuestra comprensión de la forma en que la variabilidad climática y la transformación ambiental influye en la dinámica de los ecosistemas mexicanos ante los factores de cambio ambiental global. En este documento se analiza primero la importancia del intercambio de CO 2 y vapor de agua entre los ecosistemas terrestres y la atmósfera. Después se describe brevemente la técnica de covarianza de flujos turbulentos para la medición de éstos, y se presentan ejemplos de mediciones en dos ecosistemas terrestres y uno urbano en México. Por último, se describen las bases conceptuales y operativas a corto, mediano y largo plazo para la continuidad de la red MexFlux. 325-336 (2013) Atmósfera 26(3), 326 R. Vargas et al. ABSTRACTUnderstanding ecosystem processes from a functional point of view is essential to study relationships among climate variability, biogeochemical cycles, and surface-atmosphere interactions. Increasingly during the last decades, the eddy covariance (EC) method has been applied in terrestrial, marine and urban ecosystems to quantify fluxes of greenhouse gases (e.g., CO 2 , H 2 O) and energy (e.g., sensible and latent heat). Networks of EC systems have been established in different regions and have provided scientific information that has been used for designing environmental and adaptation policies. In this context, this article outlines the conceptual and technical framework for the establishment of an EC regional network (i.e., MexFlux) to measure the surface-atmosphere exchange of heat and greenhouse gases in Mexico. The goal of the network is to improve our understanding of how climate variability and environmental change influence the dynamics of Mexican ecosystems. First, we discuss the relevance of CO 2 and water vapor exchange between terrestrial ecosystems and the atmosphere. Second, we briefly describe the EC basis and present examples of measurements in terrestrial and urban ecosystems of Mexico. Finally, we describe the conceptual and operational goals at short-, medium-, and long-term scales for continuity of the MexFlux network.

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Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation

Cited by 1,404 publications

References 36 publications

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Progress and opportunities for monitoring greenhouse gases fluxes in Mexican ecosystems: the MexFlux network

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