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

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

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2011.06.007 ◽

2011 ◽

Vol 151 (12) ◽

pp. 1514-1528 ◽

Cited By ~ 74

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

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Study on Hydrological Simulation of Jin River Based on SWAT-X Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.84-85.238 ◽

2011 ◽

Vol 84-85 ◽

pp. 238-243

Author(s):

Yu Jie Fang ◽

Wen Bin Zhou ◽

Ding Gui Luo

Keyword(s):

River Basin ◽

Assessment Tool ◽

Swat Model ◽

Model Parameters ◽

Parameter Sensitivity Analysis ◽

Hydrological Simulation ◽

Resources Management ◽

Using Data ◽

Sensitive Parameters ◽

Water Assessment

Hydrological simulation is the basis of water resources management and utilization. In this study, Soil and Water Assessment Tool (SWAT) model was applied to Jin River Basin for hydrological simulation on ArcView3.3 platform. The basic database of Jin river Basin was built using ArcGis9.2. Based on the LH-OAT parameter sensitivity analysis, the sensitive parameters of runoff were identified, including CN2, Gwqmn, rchrg_dp, ESCO, sol_z, SLOPE, SOL_AWC, sol_k, Gwrevap, and then model parameters related to runoff were calibrated and validated using data observed in weifang, yifeng, shanggao and gaoan hydrological stations during 2001-2008. The simulation showed that the simulated values were reasonably comparable to the observed data (Re<20%, R2 >0.7 and Nash-suttcliffe > 0.7), suggesting the validity of SWAT model in Jin River Basin.

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

Journal of Geophysical Research Atmospheres ◽

10.1029/2011jg001723 ◽

2011 ◽

Vol 116 (G4) ◽

Cited By ~ 36

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

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

Biogeosciences ◽

10.5194/bg-13-1409-2016 ◽

2016 ◽

Vol 13 (5) ◽

pp. 1409-1422 ◽

Cited By ~ 10

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.

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Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products

Journal of Remote Sensing ◽

10.34133/2021/9795837 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Zhaoying Zhang ◽

Yongguang Zhang ◽

Jing M. Chen ◽

Weimin Ju ◽

Mirco Migliavacca ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Energy Balance ◽

Primary Production ◽

Gross Primary Production ◽

Structural Effects ◽

Canopy Interception ◽

Monitoring Instrument ◽

Uncertainty Level ◽

Scope Model

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.

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Reduction of Global Plant Production due to Droughts from 2001 to 2010: An Analysis with a Process-Based Global Terrestrial Ecosystem Model

Earth Interactions ◽

10.1175/ei-d-14-0030.1 ◽

2015 ◽

Vol 19 (16) ◽

pp. 1-21 ◽

Cited By ~ 4

Author(s):

Chang Liao ◽

Qianlai Zhuang

Keyword(s):

Primary Production ◽

Net Primary Production ◽

Gross Primary Production ◽

Terrestrial Ecosystem ◽

Terrestrial Ecosystems ◽

Ecosystem Model ◽

Plant Production ◽

Model Parameters ◽

Drought Impact ◽

Terrestrial Ecosystem Model

Abstract Droughts dramatically affect plant production of global terrestrial ecosystems. To date, quantification of this impact remains a challenge because of the complex plant physiological and biochemical processes associated with drought. Here, this study incorporates a drought index into an existing process-based terrestrial ecosystem model to estimate the drought impact on global plant production for the period 2001–10. Global Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data products are used to constrain model parameters and verify the model algorithms. The verified model is then applied to evaluate the drought impact. The study indicates that droughts will reduce GPP by 9.8 g C m−2 month−1 during the study period. On average, drought reduces GPP by 10% globally. As a result, the global GPP decreased from 106.4 to 95.9 Pg C yr−1 while the global net primary production (NPP) decreased from 54.9 to 49.9 Pg C yr−1. This study revises the estimation of the global NPP and suggests that the future quantification of the global carbon budget of terrestrial ecosystems should take the drought impact into account.

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Vegetation-specific model parameters are not required for estimating gross primary production

Ecological Modelling ◽

10.1016/j.ecolmodel.2014.08.017 ◽

2014 ◽

Vol 292 ◽

pp. 1-10 ◽

Cited By ~ 26

Author(s):

Wenping Yuan ◽

Wenwen Cai ◽

Shuguang Liu ◽

Wenjie Dong ◽

Jiquan Chen ◽

...

Keyword(s):

Primary Production ◽

Gross Primary Production ◽

Specific Model ◽

Model Parameters

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

Remote Sensing ◽

10.3390/rs10050708 ◽

2018 ◽

Vol 10 (5) ◽

pp. 708 ◽

Cited By ~ 6

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

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

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-5475-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 5475-5488 ◽

Cited By ~ 2

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.

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