Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors

Veroustraete, Frank; Sabbe, Hendrik; Rasse, Daniel P.; Bertels, Luc

doi:10.1080/0143116031000115238

Cited by 49 publications

(40 citation statements)

References 18 publications

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“…Fourthly, although the CO 2 level increases each year, it is considered as a global constant. Finally, the AR is a linear function of daily mean temperature and is assumed biomass-independent [59]. The average value of the original AR fraction is around 0.7 for Europe and is an overestimation compared to other values found in the literature [1,42,[60][61][62].…”

Section: Introductionmentioning

confidence: 83%

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

et al. 2016

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Abstract:According to Monteith's theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO 2 fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith's theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999-2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R 2 values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84-0.61, RRMSE (%) reduced from 13.1-8.9, MBE (t/ha) decreased from 0.05-0.03 and the index of model performance (E 1 ) increased from 0.08-0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

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Section: Introductionmentioning

confidence: 83%

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

et al. 2016

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“…Similarly, the capacity of Modified C-Fix to correctly estimate forest production variations in space and time has been assessed in previous investigations [18,20,21,25]. The theoretical basis of this model has also been recently validated by Jung et al [34], who indicated that cumulative fAPAR of the growing season derived from space is directly linked to gross carbon uptake in European ecosystems.…”

Section: Discussionmentioning

confidence: 90%

“…The enhancements in photosynthetic activity simulated in the current study critically depend on this concentration and on the robustness of Equation 2. This equation was calibrated using the results of various FACE experiments, which should guarantee its applicability to a wide range of ecosystems and environmental situations [21,39].…”

Section: Discussionmentioning

confidence: 99%

“…C-Fix is a Monteith type parametric model driven by temperature, radiation and the fraction of absorbed photosynthetically active radiation (fAPAR), quantified through its generalized relationship with the normalized difference vegetation index (NDVI) [20,21]. C-Fix combines satellite-derived fAPAR with field based estimates of incoming solar radiation and air temperature, which are jointly used to simulate total photosynthesis.…”

Section: Application Of C-fix To Present Environmental Conditionsmentioning

confidence: 99%

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Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests

et al. 2009

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Abstract:A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO 2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO 2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes.

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“…The SPOT GPP dataset is produced at 10-day intervals, based on the C-Fix model of Veroustraete et al (2004). It differs from the other models by imposing limits of GPP when the temperature or vapor pressure is higher or lower, than an optimum value specified for a given climate (Mc Cree, 1972;Sabbe & Veroustraete, 2000).…”

Section: Modeling Gppmentioning

confidence: 99%

Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States

Coops

Ferster

Waring

et al. 2009

Remote Sensing of Environment

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Gross primary production (GPP), the photosynthetic uptake of carbon, is an important variable in the global carbon cycle. Although continuous measurements of GPP are being collected from a network of micrometeorological towers, each site represents a small area with records available for only a limited period. As a result, GPP is commonly modeled over forested landscapes as a function of climatic and soil variables, often supplemented with satellite-derived estimates of the vegetation's light-absorbing properties. Since the late 1990s, a number of models have been developed to provide seasonal and annual estimates of GPP across much of the Earth. Each model, however, contains different underlying assumptions and requires different amounts of data. As a result, predictions vary, sometimes significantly. In this paper we compare modeled estimates of GPP for forested areas across the U.S.A. derived from: NASA's MODIS Product (MOD17); the C-Fix model using SPOT-VGT satellite-derived vegetation data; and the Physiological Principles Predicting Growth from Satellites (3-PGS) model, a process-based model that requires information on both climate and soil properties. The models predicted average ecoregion values of forest GPP between 9.8 and 14.1 MgC ha − 1 y − 1 across the United States. 3-PGS predicted the lowest values while the C-Fix model, which included a CO 2 fertilization factor, produced the highest estimates. In the western part of the country, estimates of GPP within a given ecoregion varied by as much as 50%, whereas in the northeast, where topography and climate are less extreme, variation in GPP was less than 10%. Within ecoregions, 3PGS predicted the most variation, reflecting its sensitivity to variation in soil properties. We conclude that where model predictions disagree, an opportunity is presented to evaluate underlying assumptions through sensitivity analyses, additional data collection and where more detailed study is warranted.

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Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors

Cited by 49 publications

References 18 publications

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests

Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States

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