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 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 Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops

2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Víctor Sánchez-Girón ◽  
Laura Recuero ◽  
César Sáenz ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Balance ◽  
Gross Primary Production ◽  
Meteorological Variables ◽  
Water Fluxes ◽  
Energy Fluxes ◽  
Global Carbon

Gross primary production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis and it is the largest flux of the global carbon balance. Our overall objective in this research is to identify and model GPP dynamics and its relationship with meteorological variables and energy fluxes based on time series analysis of eddy covariance (EC) data in two different agroecosystems, a Mediterranean rice crop in Spain and a rainfed cropland in Germany. Crops exerted an important influence on the energy and water fluxes dynamics existing a clear feedback between GPP, meteorological variables and energy fluxes in both type of crops.

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

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 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 Evaluating triple oxygen isotope estimates of gross primary production at the Hawaii Ocean Time-series and Bermuda Atlantic Time-series Study sites

2012 ◽  
Vol 117 (C5) ◽  
pp. n/a-n/a ◽  
Author(s):  
David P. Nicholson ◽  
Rachel H. R. Stanley ◽  
Eugeni Barkan ◽  
David M. Karl ◽  
Boaz Luz ◽  
...  
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Oxygen Isotope ◽  
Gross Primary Production ◽  
Time Series Study ◽  
Study Sites ◽  
Series Study

Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

2015 ◽  
Vol 89 (3) ◽  
pp. 491-510 ◽  
Author(s):  
Víctor Cicuéndez ◽  
Javier Litago ◽  
Margarita Huesca ◽  
Manuel Rodriguez-Rastrero ◽  
Laura Recuero ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Analysis ◽  
Primary Production ◽  
Gross Primary Production ◽  
Holm Oak ◽  
Oak Forest ◽  
Series Analysis ◽  
Production Dynamics ◽  
Sensing Time

scholarly journals Tune in on 11.57 µHz and listen to primary production

2017 ◽  
Vol 14 (22) ◽  
pp. 5271-5280
Author(s):  
Tom J. S. Cox ◽  
Justus E. E. van Beusekom ◽  
Karline Soetaert
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Frequency Domain ◽  
Water Column ◽  
Gross Primary Production ◽  
Time Varying ◽  
Carrier Wave ◽  
Theoretical Derivation ◽  
Complex Demodulation ◽  
Averaging Time

Abstract. In this paper we present an elegant approach to reconstruct slowly varying gross primary production (GPP) as a function of time, based on O2 time series. The approach, called complex demodulation, is based on a direct analogy with amplitude-modulated (AM) radio signals. The O2 concentrations oscillating at the diel frequency (or 11.57 µHz) can be seen as a carrier wave, while the time variation in the amplitude of this carrier wave is related to the time-varying GPP. The relation follows from an analysis in the frequency domain of the governing equations of O2 dynamics. After the theoretical derivation, we assess the performance of the approach by applying it to three artificial O2 time series, generated with models representative of a well-mixed vertical water column, a river and an estuary. These models are forced with hourly observed incident irradiance, resulting in a variability of GPP on scales from hours to months. The dynamic build-up of algal biomass further increases the seasonality. Complex demodulation allows for reconstruction, with great precision, of time-varying GPP of the vertical water column and the river model. Surprisingly, it is possible to derive daily averaged GPP – complex demodulation thus reconstructs the amplitude of every single diel cycle. Also, in estuaries time-varying GPP can be reconstructed to a great extent. But there, the influence of the tides prevent achieving the same temporal resolution. In particular, the combination of horizontal O2 gradients with quasi-diurnal harmonics in the tides interferes with the complex demodulation procedure and introduces spurious amplitude variation that can not be attributed to GPP. We demonstrate that these spurious effects also occur in real-world time series (Hörnum Tief, Germany). The spurious effects due to K1 and P1 quasi-diurnals can not be distinguished from GPP. However, the spurious fluctuations introduced by O1 and Q1 can be removed to a large extent by increasing the averaging time to 15 days. As such, we demonstrate that a good estimate of the running 15-day average of GPP can be obtained in tidal systems. Apart from the direct merits of estimating GPP from O2 time series, the analysis in the frequency domain enhances our insights into O2 dynamics in tidal systems in general, and into the performance of O2 methods to estimate GPP in particular.

scholarly journals A time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in eastern United States

2018 ◽  
Author(s):  
Qinchuan Xin ◽  
Yongjiu Dai ◽  
Xiaoping Liu
Keyword(s):  
United States ◽  
Time Series ◽  
Steady State ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Gross Primary Production ◽  
Eastern United States ◽  
Area Index ◽  
Time Stepping

Abstract. Terrestrial plants play a key role in regulating the exchange of energy and materials between the land surface and the atmosphere. Robust terrestrial biosphere models that simulate both time series of leaf dynamics and canopy photosynthesis are required to understand the vegetation-climate interactions. This study proposes a time stepping scheme to simulate leaf area index (LAI), phenology, and gross primary production (GPP) simultaneously via only climate variables based on an ecological assumption that plants allocate leaf biomass till an environment could sustain to maximize photosynthetic reproduction. The method establishes a linear function between the steady-state LAI and the corresponding GPP, which is used to track the suitability of environmental conditions for plant photosynthesis, and applies the MOD17 algorithm to form simultaneous equations together, which can be solved numerically. To account for the time lag in plant responses of leaf allocation to environment variation, a time stepping scheme is developed to simulate the LAI time series based on the solved steady-state LAI. The simulated LAI time series is then used to derive the timing of key phenophases and simulate canopy GPP with the MOD17 algorithm. The developed method is applied to deciduous broadleaf forests in eastern United States and has found to perform well on simulating canopy LAI and GPP at the site scale as evaluated using both flux tower and satellite data. The method could also capture the spatiotemporal variation of vegetation LAI and phenology across eastern United States as compared with satellite observations. The developed time-stepping scheme provides a simplified and improved version of our previous modeling approach and forms a potential basis for regional to global applications in future studies.

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