Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Verma, Manish; Friedl, M. A.; Richardson, Andrew D.; Kiely, Gerard; Cescatti, Alessandro; Law, B. E.; Wohlfahrt, Georg; Gielen, Bert; Roupsard, Olivier; Moors, E.J.; Toscano, Piero; Vaccari, Francesco Primo; Gianelle, Damiano; Bohrer, Gil; Varlagin, Andrej; Buchmann, Nina; Gorsel, Eva van; Montagnani, Leonardo; Propastin, Pavel

doi:10.5194/bg-11-2185-2014

Cited by 74 publications

(65 citation statements)

References 82 publications

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“…Models based on the LUE principle continue to be developed and compared, now most commonly in terms of their ability to reproduce GPP as derived from CO 2 flux measurements (see e.g. Cheng et al, 2014;McCallum et al, 2009McCallum et al, , 2013Verma et al, 2014;Horn and Schulz, 2011;Yuan et al, 2007Yuan et al, , 2013. Their popularity depends on the fact that green-vegetation cover in LUE models is directly provided from satellite observations, thus sidestepping one of the most serious limitations of current dynamic global vegetation models (DGVMs), namely their (in)ability to realistically predict spatial and temporal patterns of green-vegetation cover (Kelley et al, 2013).…”

Section: Implications For Modelling Strategymentioning

confidence: 99%

“…Meanwhile, the multiplicity of available LUE formulations, and the lack of agreement on, for example, the way temperature and CO 2 responses are built into LUE models (Verma et al, 2014), or whether or not these responses should be PFT-specific (Yuan et al, 2013), are causes for concern. These differences ultimately reflect the lack of a clear theoretical basis for LUE modelling.…”

Section: Implications For Modelling Strategymentioning

confidence: 99%

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Biophsyical constraints on gross primary production by the terrestrial biosphere

2014

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Abstract. Persistent divergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C 3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C 3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO 2 limitation. The ratio of leaf-internal to ambient CO 2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO 2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover -driven primarily by water shortage -represents the largest constraint on global GPP.

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Section: Implications For Modelling Strategymentioning

confidence: 99%

Section: Implications For Modelling Strategymentioning

confidence: 99%

Biophsyical constraints on gross primary production by the terrestrial biosphere

2014

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“…The scientific applications of these data are further advanced by combining the information from remotely sensed products, such as Moderate Resolution Imaging Spectroradiometer‐derived fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation indices. These applications have stimulated an increasing interest in empirical, semiempirical, and process‐oriented modeling approaches to estimate regional to global carbon, water, and energy budgets [e.g., Beer et al , , , ; Jung et al , , ; Verma et al , ; Xiao et al , ; Yang et al , ].…”

Section: Introductionmentioning

confidence: 99%

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

et al. 2015

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Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input-output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m−2 d−1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gC m−2 d−1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8–2.09 gC m−2 d−1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty

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“…The reason for the weak or no correlations between both GPP and PsnNet and the text-based records may lie in the formulation of the MOD17 product. Indeed, limitations of the product in capturing spatial and temporal variability in croplands have been reported (Verma et al, 2014;Zhang et al, 2012).…”

Section: Early Drought Detection With Remote Sensing Productsmentioning

confidence: 99%

The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

Linés

Werner

Bastiaanssen

2017

Hydrol. Earth Syst. Sci.

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Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

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Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Cited by 74 publications

References 82 publications

Biophsyical constraints on gross primary production by the terrestrial biosphere

Biophsyical constraints on gross primary production by the terrestrial biosphere

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

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