Improved global maps of the optimum growth temperature, maximum light use efficiency and gross primary production for vegetation

2021 ◽  
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu ◽  
Xutong Wu ◽  
Zhen Gao
Keyword(s):  
Primary Production ◽  
Growth Temperature ◽  
Gross Primary Production ◽  
Light Use Efficiency ◽  
Optimum Growth ◽  
Maximum Light ◽  
Optimum Growth Temperature ◽  
Use Efficiency

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 Global cropland monthly Gross Primary Production in the year 2000

2014 ◽  
Vol 11 (2) ◽  
pp. 3465-3488
Author(s):  
T. Chen ◽  
G. R. van der Werf ◽  
N. Gobron ◽  
E. J. Moors ◽  
A. J. Dolman
Keyword(s):  
Primary Production ◽  
Survey Data ◽  
Land Surface ◽  
Gross Primary Production ◽  
Co2 Exchange ◽  
Light Use Efficiency ◽  
Natural Ecosystems ◽  
Use Efficiency ◽  
Crop Types ◽  
Year 2000

Abstract. Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ−1 to 2.96 g CMJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

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 Direct effect of aerosols on solar radiation and gross primary production in boreal and hemiboreal forests

2018 ◽  
Vol 18 (24) ◽  
pp. 17863-17881 ◽  
Author(s):  
Ekaterina Ezhova ◽  
Ilona Ylivinkka ◽  
Joel Kuusk ◽  
Kaupo Komsaare ◽  
Marko Vana ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Primary Production ◽  
Direct Effect ◽  
Gross Primary Production ◽  
Global Radiation ◽  
Diffuse Radiation ◽  
Light Use Efficiency ◽  
Diffuse Fraction ◽  
Aerosol Loading ◽  
Use Efficiency

Abstract. The effect of aerosol loading on solar radiation and the subsequent effect on photosynthesis is a relevant question for estimating climate feedback mechanisms. This effect is quantified in the present study using ground-based measurements from five remote sites in boreal and hemiboreal (coniferous and mixed) forests of Eurasia. The diffuse fraction of global radiation associated with the direct effect of aerosols, i.e. excluding the effect of clouds, increases with an increase in the aerosol loading. The increase in the diffuse fraction of global radiation from approximately 0.11 on days characterized by low aerosol loading to 0.2–0.27 on days with relatively high aerosol loading leads to an increase in gross primary production (GPP) between 6 % and 14 % at all sites. The largest increase in GPP (relative to days with low aerosol loading) is observed for two types of ecosystems: a coniferous forest at high latitudes and a mixed forest at the middle latitudes. For the former ecosystem the change in GPP due to the relatively large increase in the diffuse radiation is compensated for by the moderate increase in the light use efficiency. For the latter ecosystem, the increase in the diffuse radiation is smaller for the same aerosol loading, but the smaller change in GPP due to this relationship between radiation and aerosol loading is compensated for by the higher increase in the light use efficiency. The dependence of GPP on the diffuse fraction of solar radiation has a weakly pronounced maximum related to clouds.

scholarly journals Identification of a general light use efficiency model for gross primary production

2011 ◽  
Vol 8 (4) ◽  
pp. 999-1021 ◽  
Author(s):  
J. E. Horn ◽  
K. Schulz
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Gross Primary Production ◽  
Daily Basis ◽  
Climatic Conditions ◽  
Environmental Drivers ◽  
Light Use Efficiency ◽  
Meteorological Forcing ◽  
Use Efficiency ◽  
Light Use Efficiency Model

Abstract. Non-stationary and non-linear dynamic time series analysis tools are applied to multi-annual eddy covariance and micrometeorological data from 44 FLUXNET sites to derive a light use efficiency model for gross primary production on a daily basis. The extracted typical behaviour of the canopies in response to meteorological forcing leads to a model formulation allowing for a variable influence of the environmental drivers temperature and moisture availability modulating the light use efficiency. Thereby, the model is applicable to a broad range of vegetation types and climatic conditions. The proposed model explains large proportions of the variation of the gross carbon uptake at the study sites while the optimized set of six parameters is well defined. With the parameters showing explainable and meaningful relations to site-specific environmental conditions, the model has the potential to serve as basis for general regionalization strategies for large scale carbon flux predictions.

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