The potential of carbonyl sulfide as a proxy for gross primary production at flux tower sites

2011 ◽  
Vol 116 (G4) ◽  
Author(s):  
J. M. Blonquist ◽  
S. A. Montzka ◽  
J. W. Munger ◽  
D. Yakir ◽  
A. R. Desai ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Carbonyl Sulfide ◽  
Flux Tower

scholarly journals Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data

2011 ◽  
Vol 151 (12) ◽  
pp. 1514-1528 ◽  
Author(s):  
Joshua L. Kalfas ◽  
Xiangming Xiao ◽  
Diana X. Vanegas ◽  
Shashi B. Verma ◽  
Andrew E. Suyker
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
Flux Tower ◽  
Modis Imagery

scholarly journals Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

2016 ◽  
Vol 13 (5) ◽  
pp. 1409-1422 ◽  
Author(s):  
Rahul Raj ◽  
Nicholas Alexander Samuel Hamm ◽  
Christiaan van der Tol ◽  
Alfred Stein
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Empirical Relationship ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
Uncertainty Assessment ◽  
Forest Site ◽  
Posterior Distributions ◽  
Flux Tower ◽  
Mcmc Simulation

Abstract. Gross primary production (GPP) can be separated from flux tower measurements of net ecosystem exchange (NEE) of CO2. This is used increasingly to validate process-based simulators and remote-sensing-derived estimates of simulated GPP at various time steps. Proper validation includes the uncertainty associated with this separation. In this study, uncertainty assessment was done in a Bayesian framework. It was applied to data from the Speulderbos forest site, The Netherlands. We estimated the uncertainty in GPP at half-hourly time steps, using a non-rectangular hyperbola (NRH) model for its separation from the flux tower measurements. The NRH model provides a robust empirical relationship between radiation and GPP. It includes the degree of curvature of the light response curve, radiation and temperature. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. We defined the prior distribution of each NRH parameter and used Markov chain Monte Carlo (MCMC) simulation to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps. This time series also allowed us to estimate the uncertainty at daily time steps. We compared the informative with the non-informative prior distributions of the NRH parameters and found that both choices produced similar posterior distributions of GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

scholarly journals Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data

2018 ◽  
Vol 10 (5) ◽  
pp. 708 ◽  
Author(s):  
Xiaoming Kang ◽  
Liang Yan ◽  
Xiaodong Zhang ◽  
Yong Li ◽  
Dashuan Tian ◽  
...  
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Coastal Wetland ◽  
Gross Primary Production ◽  
Eddy Flux ◽  
Flux Tower

scholarly journals Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements

2003 ◽  
Vol 17 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tagir G. Gilmanov ◽  
Shashi B. Verma ◽  
Phillip L. Sims ◽  
Tilden P. Meyers ◽  
James A. Bradford ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Light Response ◽  
Co2 Flux ◽  
Southern Plains ◽  
Flux Tower

scholarly journals Comparison of Machine Learning Methods to Up-Scale Gross Primary Production

2021 ◽  
Vol 13 (13) ◽  
pp. 2448
Author(s):  
Tao Yu ◽  
Qiang Zhang ◽  
Rui Sun
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Flux ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Learning Methods ◽  
Flux Tower ◽  
Machine Learning Methods

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.

scholarly journals Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images

2017 ◽  
Vol 190 ◽  
pp. 42-55 ◽  
Author(s):  
Fengfei Xin ◽  
Xiangming Xiao ◽  
Bin Zhao ◽  
Akira Miyata ◽  
Dennis Baldocchi ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Paddy Rice ◽  
Eddy Flux ◽  
Flux Tower

scholarly journals Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

2020 ◽  
Vol 12 (14) ◽  
pp. 2186
Author(s):  
Fengfei Xin ◽  
Xiangming Xiao ◽  
Osvaldo M.R. Cabral ◽  
Paul M. White ◽  
Haiqiang Guo ◽  
...  
Keyword(s):  
Primary Production ◽  
Seasonal Dynamics ◽  
Land Surface ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Data Driven ◽  
Flux Tower ◽  
The Usa ◽  
The Relationship

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

scholarly journals Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

2015 ◽  
Vol 12 (16) ◽  
pp. 13967-14002
Author(s):  
R. Raj ◽  
N. A. S. Hamm ◽  
C. van der Tol ◽  
A. Stein
Keyword(s):  
Primary Production ◽  
Prior Distribution ◽  
Gross Primary Production ◽  
Empirical Relationship ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
Forest Site ◽  
Posterior Distributions ◽  
Flux Tower ◽  
Mcmc Simulation

Abstract. Gross primary production (GPP), separated from flux tower measurements of net ecosystem exchange (NEE) of CO2, is used increasingly to validate process-based simulators and remote sensing-derived estimates of simulated GPP at various time steps. Proper validation should include the uncertainty associated with this separation at different time steps. This can be achieved by using a Bayesian framework. In this study, we estimated the uncertainty in GPP at half hourly time steps. We used a non-rectangular hyperbola (NRH) model to separate GPP from flux tower measurements of NEE at the Speulderbos forest site, The Netherlands. The NRH model included the variables that influence GPP, in particular radiation, and temperature. In addition, the NRH model provided a robust empirical relationship between radiation and GPP by including the degree of curvature of the light response curve. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. Adopting a Bayesian approach, we defined the prior distribution of each NRH parameter. Markov chain Monte Carlo (MCMC) simulation was used to update the prior distribution of each NRH parameter. This allowed us to estimate the uncertainty in the separated GPP at half-hourly time steps. This yielded the posterior distribution of GPP at each half hour and allowed the quantification of uncertainty. The time series of posterior distributions thus obtained allowed us to estimate the uncertainty at daily time steps. We compared the informative with non-informative prior distributions of the NRH parameters. The results showed that both choices of prior produced similar posterior distributions GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

scholarly journals Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers

2021 ◽  
Vol 13 (4) ◽  
pp. 794
Author(s):  
Haibo Wang ◽  
Jingfeng Xiao
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Model Parameters ◽  
Flux Tower ◽  
Plant Photosynthesis ◽  
Model Understanding ◽  
Using Data ◽  
Sensitive Parameters ◽  
Shed Light ◽  
Scope Model

Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.

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