scholarly journals A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

2020 ◽  
Vol 12 (2) ◽  
pp. 258 ◽  
Author(s):  
Ruonan Qiu ◽  
Ge Han ◽  
Xin Ma ◽  
Hao Xu ◽  
Tianqi Shi ◽  
...  
Keyword(s):  
North America ◽  
Primary Production ◽  
Vegetation Type ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Seasonal Cycles ◽  
Vegetation Types ◽  
Growth Trend ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

scholarly journals Leaf Area Index identified as a major source of variability in modelled CO<sub>2</sub> fertilization

2018 ◽  
Author(s):  
Qianyu Li ◽  
Xingjie Lu ◽  
Yingping Wang ◽  
Xin Huang ◽  
Peter M. Cox ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Vegetation Types ◽  
Cable Model ◽  
Co2 Fertilization ◽  
Area Index ◽  
Leaf Level

Abstract. The concentration-carbon feedback factor (β), also called the CO2 fertilization effect, is a key unknown in climate-carbon cycle projections. A better understanding of model mechanisms that govern terrestrial ecosystem responses to elevated CO2 is urgently needed to enable a more accurate prediction of future terrestrial carbon sink. We calculated CO2 fertilization effects at various hierarchical levels from leaf biochemical reaction, leaf photosynthesis, canopy gross primary production (GPP), net primary production (NPP), to ecosystem carbon storage (cpool), for seven C3 vegetation types in response to increasing CO2 under RCP 8.5 scenario, using the Community Atmosphere Biosphere Land Exchange model (CABLE). Our results show that coefficient of variation (CV) for the CABLE model among the seven vegetation types is 0.15–0.13 for the biochemical level β, 0.13–0.16 for the leaf-level β, 0.48 for the βGPP, 0.45 for the βNPP, and 0.58 for the βcpool. The low variation of the leaf-level β is consistent with a theoretical analysis that leaf photosynthetic sensitivity to increasing CO2 concentration is almost an invariant function. In CABLE, the major jump in CV of β values from leaf- to canopy- and ecosystem-levels results from divergence in modelled leaf area index (LAI) within and among the vegetation types. The correlations of βGPP, βNPP, or βcpool with βLAI are very high in CABLE. Overall, our results indicate that modelled LAI is a key factor causing the divergence in β values in CABLE model. It is therefore urgent to constrain processes that regulate LAI dynamics in order to better represent the response of ecosystem productivity to increasing CO2 in Earth System Models.

scholarly journals Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

2014 ◽  
Vol 11 (13) ◽  
pp. 3437-3451 ◽  
Author(s):  
P. N. Foster ◽  
I. C. Prentice ◽  
C. Morfopoulos ◽  
M. Siddall ◽  
M. van Weele
Keyword(s):  
Primary Production ◽  
Air Temperature ◽  
Seasonal Cycle ◽  
Gross Primary Production ◽  
Canopy Temperature ◽  
Remotely Sensed ◽  
Formaldehyde Concentration ◽  
Isoprene Emission ◽  
The Tropics ◽  
Better Than

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover generalized relationships applicable across many biomes between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. We used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r= 0.56) and a "reference" isoprene model that combines GPP and an exponential function of temperature (r = 0.49), and far better than either remotely sensed green vegetation cover, fPAR (r = 0.25) or model-estimated GPP (r = 0.14). Gross primary production in tropical regions was anti-correlated with formaldehyde concentration (r = −0.30), which peaks during the dry season. Our results were most reliable in the tropics, where formaldehyde observational errors were the least. The tropics are of particular interest because they are the greatest source of isoprene emission as well as the region where previous modelling attempts have been least successful. We conjecture that positive correlations of isoprene emission with GPP and air temperature (as found in temperate forests) may arise simply because both covary with canopy temperature, peaking during the relatively short growing season. The lack of a general correlation between GPP and formaldehyde concentration in the seasonal cycle is consistent with experimental evidence that isoprene emission rates are largely decoupled from photosynthetic rates, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress.

scholarly journals Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

2015 ◽  
Vol XL-7/W3 ◽  
pp. 27-30 ◽  
Author(s):  
H. H. Jaafar ◽  
F. A. Ahmad
Keyword(s):  
Primary Production ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Early Assessment ◽  
Food Shortages ◽  
Semi Arid ◽  
Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

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 Remotely-sensed detection of effects of extreme droughts on gross primary production

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Sara Vicca ◽  
Manuela Balzarolo ◽  
Iolanda Filella ◽  
André Granier ◽  
Mathias Herbst ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Remotely Sensed

scholarly journals Estimation of gross primary production of the Amazon-Cerrado transitional forest by remote sensing techniques

2014 ◽  
Vol 29 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Maísa Caldas Souza ◽  
Marcelo Sacardi Biudes ◽  
Victor Hugo de Morais Danelichen ◽  
Nadja Gomes Machado ◽  
Carlo Ralph de Musis ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Type ◽  
Gross Primary Production ◽  
Circulation Model ◽  
Lookup Table ◽  
Sensor Data ◽  
Climatic Data ◽  
Light Use Efficiency ◽  
Transitional Forest ◽  
Use Efficiency

The gross primary production (GPP) of ecosystems is an important variable in the study of global climate change. Generally, the GPP has been estimated by micrometeorological techniques. However, these techniques have a high cost of implantation and maintenance, making the use of orbital sensor data an option to be evaluated. Thus, the objective of this study was to evaluate the potential of the MODIS (Moderate Resolution Imaging Spectroradiometer) MOD17A2 product and the vegetation photosynthesis model (VPM) to predict the GPP of the Amazon-Cerrado transitional forest. The GPP predicted by MOD17A2 (GPP MODIS) and VPM (GPP VPM) were validated with the GPP estimated by eddy covariance (GPP EC). The GPP MODIS, GPP VPM and GPP EC have similar seasonality, with higher values in the wet season and lower in the dry season. However, the VPM performed was better than the MOD17A2 to estimate the GPP, due to use local climatic data for predict the light use efficiency, while the MOD17A2 use a global circulation model and the lookup table of each vegetation type to estimate the light use efficiency.

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).

scholarly journals Are vegetation-specific model parameters required for estimating gross primary production?

2013 ◽  
Vol 6 (4) ◽  
pp. 5475-5488 ◽  
Author(s):  
W. Yuan ◽  
S. Liu ◽  
W. Cai ◽  
W. Dong ◽  
J. Chen ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Specific Model ◽  
Accurate Information ◽  
Model Parameters ◽  
Vegetation Types ◽  
Predictive Capacity ◽  
Ecosystem Carbon ◽  
Use Efficiency ◽  
Globally Distributed

Abstract. Models of gross primary production (GPP) are currently parameterized with vegetation-specific parameter sets and therefore require accurate information on the distribution of vegetation to drive them. Can this parameterization scheme be replaced with a vegetation-invariant set of parameter that can maintain or increase model applicability by reducing errors introduced from the uncertainty of land cover classification? Based on the measurements of ecosystem carbon fluxes from 150 globally distributed sites in a range of vegetation types, we examined the predictive capacity of seven light use efficiency (LUE) models. Two model experiments were conducted: (i) a constant set of parameters for various vegetation types and (ii) vegetation-specific parameters. The results showed no significant differences in model performances to simulate GPP while using both sets of parameters. These results indicate that a universal set of parameters, which is independent of vegetation cover type and characteristics can be adopted in prevalent LUE models. Availability of this well tested and universal set of parameters would help to improve the accuracy and applicability of LUE models in various biomes and geographic regions.

Sign in / Sign up

Export Citation Format

Share Document