scholarly journals A Modeling Application for GHG Fluxes Estimates in Betel Nuts Plantations in Taiwan

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 895
Author(s):  
Adriano Palma ◽  
Chen-Yeon Chu ◽  
Francesco Petracchini ◽  
Mei-Ling Yeh ◽  
Cheng-Ting Wu ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Positive Impact ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Evergreen Forest ◽  
Light Use Efficiency ◽  
Large Area ◽  
Tropical Evergreen Forest ◽  
Use Efficiency

Perennial woody crops could have a positive impact on carbon balance, absorbing carbon during growing season and storing it for several years, whereas annual crops do not have this particular effect. Usually, techniques for GHG (greenhouse gases) flux measurements have limited spatial representativeness, with some difficulties to extend leaf measurements to field scale. Models, especially if supported by remote sensing data, allow for upscaling the monitoring of these fluxes. The aim of this work was to evaluate the carbon fluxes (gross primary production, GPP; net ecosystem production, NEP) of the betel nut cultivars in Taiwan by a vegetation photosynthesis model (VPM). The model permitted estimating seasonal dynamics of GPP in a moist tropical evergreen forest. These plantations are very common in Taiwan and their role could be significant in environmental and development policies even though, until now, the consumption of the fruit of this tree is at the center of controversy due to their use and effects on the population. To obtain estimates of carbon fluxes on a large area that would appreciate its spatial variability, a model based on physiological processes was used. This model incorporated a series of procedures and monthly mean meteorological data, light use efficiency, and satellite enhanced vegetation index (EVI) were used as inputs. An additional purpose of this work was to compare the carbon uptake of different cultivars in Taiwan and Italy. Using a different model, always based on light use efficiency, a similar project was carried on Italian vineyards, with other climate conditions and different agricultural practices.

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

2020 ◽  
Vol 13 (3) ◽  
pp. 1545-1581 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Quantum Yield ◽  
Primary Production ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland

2020 ◽  
Vol 288-289 ◽  
pp. 107983 ◽  
Author(s):  
Hesong Wang ◽  
Gensuo Jia ◽  
Howard E. Epstein ◽  
Huichen Zhao ◽  
Anzhi Zhang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Light Use Efficiency ◽  
Use Efficiency ◽  
Semi Arid ◽  
Light Use Efficiency Model

scholarly journals Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production

2021 ◽  
Vol 13 (5) ◽  
pp. 1015
Author(s):  
Fengji Zhang ◽  
Zhijiang Zhang ◽  
Yi Long ◽  
Ling Zhang
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Large Scale ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Reanalysis Data ◽  
Light Use Efficiency ◽  
The Us ◽  
Use Efficiency

Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPPMODIS-GPP and GPPEC-LUE, respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPPGR and GPPVI, respectively). Lastly, GPPMODIS-GPP, GPPEC-LUE, GPPGR, and GPPVI were evaluated against the eddy covariance flux data (GPPEC) at the site scale and MODIS GPP products (GPPMOD17) at the regional scale. The results showed that, at the site scale, GPPMODIS-GPP and GPPEC-LUE agreed well with GPPEC for the US-Ton site, with R2 = 0.73 and 0.74, respectively. The performance of GPPGR and GPPVI varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPPMODIS-GPP and GPPEC-LUE agreed well with GPPMOD17 in 2017 and 2018, with R2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.

Biogeographical Patterns of Light Use efficiency?

2021 ◽  
Author(s):  
David Sandoval ◽  
Iain Colin Prentice
Keyword(s):  
Maximum Rate ◽  
Spatial Organization ◽  
Gross Primary Production ◽  
Incident Light ◽  
Direct Consequence ◽  
Theoretical Explanation ◽  
Light Use Efficiency ◽  
Mountain Regions ◽  
Use Efficiency ◽  
Photosynthetic Traits

<p>The emergent spatial organization of ecosystems in elevational gradients suggest that some ecosystem processes, important enough to shape morphological traits, must show similar patterns.</p><p>The most important of these processes, gross primary production (GPP), usually (albeit with some exceptions) decreases with elevation. This was previously thought to be a direct consequence either of the decrease in temperature, or the decrease of incident light due to cloud cover. However, some recent developments in photosynthetic theory, plus the unprecedented availability of ecophysiological data, support the hypothesis that plants acclimate (optimize) their photosynthetic traits to the environment. In this new theoretical context, the temperature is no longer considered as a major constraining factor, except when either freezing or excessively high temperatures inhibit plant function generally.</p><p>Two of the most important photosynthetic traits, the maximum rate of carboxylation (V<sub>CMAX</sub>) and the intrinsic quantum efficiency (φ<sub>o</sub>), vary in opposite directions with increasing elevation. Plants tend to increase V<sub>CMAX</sub> to compensate for a decrease in the ratio leaf-internal to ambient partial pressures of CO<sub>2</sub>, while φ<sub>o</sub> increases with temperature up to a plateau. To explore how these different responses, documented at leaf level, converge in emergent spatial patterns at ecosystem scale we considered how elevation shape light use efficiency (defined as the ratio of CO<sub>2</sub> assimilated over light absorbed) over mountain regions worldwide. We used data from eddy-covariance flux towers, from different networks, located in mountain regions around the world, adding up to 618 station-years of record. To complement our analysis, we included theoretical predictions using an optimality model (P-model) and evaluated changes in the spatial pattern with simulation experiments.</p><p>Empirically we found an asymptotic response of LUE to the average daytime temperature during the growing season with increasing elevation, and a small, but globally consistent effect of elevation on LUE. We propose a theoretical explanation for the observation that temperature differences have little impact on the biogeographical pattern of LUE, but we also find that different assumptions on the acclimation of the maximum rate of electron transport (J<sub>MAX</sub>) and φ<sub>o</sub> change this pattern.</p>

scholarly journals Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237351
Author(s):  
Koffi Dodji Noumonvi ◽  
Mitja Ferlan
Keyword(s):  
Carbon Fluxes ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals Remote sensing of ecosystem light use efficiency with MODIS-based PRI

2011 ◽  
Vol 8 (1) ◽  
pp. 189-202 ◽  
Author(s):  
A. Goerner ◽  
M. Reichstein ◽  
E. Tomelleri ◽  
N. Hanan ◽  
S. Rambal ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Vapour Pressure Deficit ◽  
Ground Truth ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Evaluation Exercise ◽  
Use Efficiency ◽  
Study Sites ◽  
Absorbed Photosynthetically Active Radiation

Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.

scholarly journals Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

scholarly journals Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

2017 ◽  
Vol 14 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Caitlin E. Moore ◽  
Jason Beringer ◽  
Bradley Evans ◽  
Lindsay B. Hutley ◽  
Nigel J. Tapper
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Seasonal Pattern ◽  
Gross Primary Productivity ◽  
Perennial Grasses ◽  
Light Use Efficiency ◽  
Tropical Savanna ◽  
Flux Tower ◽  
Use Efficiency

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

scholarly journals Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance

2015 ◽  
Vol 163 ◽  
pp. 206-216 ◽  
Author(s):  
Marta Yebra ◽  
Albert I.J.M. Van Dijk ◽  
Ray Leuning ◽  
Juan Pablo Guerschman
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Canopy Conductance ◽  
Light Use Efficiency ◽  
Use Efficiency ◽  
Global Vegetation
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