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

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## Abstract

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_{cmax}), 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.

## 1. Introduction

_{cmax}, μmol CO

_{2}m

^{−2}s

^{−1}) of RuBisCO is one of the most important parameters in the photosynthesis submodel [42]. SIF is related to the electron transport rate in the process of photosynthesis [3], and there are complex and intrinsic linkages among the absorbed photosynthetically active radiation (APAR), fluorescence radiance, and the rate of photochemistry [16,43]. Declining V

_{cmax}reduces the saturated rate of photosynthesis and switches the light-limiting condition to the light-saturating condition at lower PAR, leading to the maximum photochemistry rate at which photochemical yield declines [43]. With the increase in PAR and nonphotochemical quenching, fluorescence yield first increases and then declines, while the fluorescence radiance (expected SIF) rises first nonlinearly and then linearly [43]. At a midday satellite overpass with high PAR, SIF tends to be higher for leaves with high V

_{cmax}than for leaves with low V

_{cmax}, leading to a positive relationship between SIF and V

_{cmax}[43]. Due to its intrinsic relationship with vegetation photosynthetic activity, SIF has also been used to estimate V

_{cmax}to better simulate GPP and SIF using the SCOPE model [41]. Several studies have evaluated the sensitivity of SIF and GPP to this parameter [39,41,44], and have shown that the SIF–V

_{cmax}relationship was not stable and instead varied between different versions of the SCOPE model [19,39,45,46]. Two recent studies showed that SIF was less sensitive to V

_{cmax}in SCOPE v1.6 [19,44]. However, the sensitivity of SIF to V

_{cmax}in the SCOPE v1.7 model across different biomes has not been examined yet. In addition, no studies have used satellite SIF observations and flux tower GPP data to jointly constrain the parameters of the SCOPE model for improving SIF and GPP simulations.

^{2}for GOME-2), leading to large scale mismatch between satellite grid cells and eddy covariance (EC) flux tower footprints [2]. The OCO-2 satellite, launched in 2014, has enabled the retrieval of finer-resolution (1.3 × 2.25 km

^{2}) SIF than the previous satellites/sensors [47]. The ground area of the OCO-2 SIF soundings is close to the typical footprint of EC flux towers, and therefore the OCO-2 SIF data can be better matched with EC observations to examine the relationship between SIF and GPP or to simultaneously constrain models [5]. To date, only a few studies have used the OCO-2 SIF products to evaluate or improve the SIF simulation of the SCOPE model (e.g., [44]). Here, we used SIF observations in the far red region from OCO-2 and GPP data from flux towers to improve the performance of the SCOPE model for simulating SIF and GPP by optimizing the key uncertain parameters in the model. We chose two temperate forests (Park Falls and Willow Creek in the USA) as our study sites. The objectives of this study were to (1) identify the key parameters of the SCOPE model in predicting SIF and GPP; and (2) examine the effects of parameter optimization on the simulation of SIF and GPP, and assess how well the SCOPE model simulations can be constrained by OCO-2 SIF and flux tower GPP, both separately and together. Our results are useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and revealing the potential of improving the SIF and GPP estimation by incorporating the SIF products and flux tower data.

## 2. Materials and Methods

#### 2.1. Site Description and Flux Tower Observations

#### 2.2. Solar-Induced Chlorophyll Fluorescence (SIF) Observations from Orbiting Carbon Observatory-2 (OCO-2)

_{2}A-band observation records [47]. Although the spatial resolution of OCO-2 is 1.3 km × 2.25 km in the nadir mode, the total swath width is only 10.3 km. With the sparse coverage of the instrument, temporally dense SIF data are not available for most locations over the globe. The Park Falls site is a very tall tower and a validation site for the OCO-2 mission [2], and OCO-2 has been collecting SIF data in the target mode, leading to temporally well-spaced retrievals of SIF measurements near the flux tower site (Figure 1).

#### 2.3. Moderate Resolution Imaging Spectroradiometer (MODIS) Data Products

#### 2.4. Model Description and Parameterization

_{best}, the fitness of the i-th chromosome (f

_{i}) in the ordered list is conducted using a linear function during the evaluation:

_{i}can be defined as follows:

_{i}

_{,sim}and Ref

_{i}

_{,obs}are the ith simulated and observed reflectance band, respectively, and n is the number of reflectance bands. The prior ranges of the parameters (e.g., Cab, LAI) of the PROSAIL model are given in Table 1.

#### 2.5. Parameter Sensitivity Analysis and Inverse Estimation of Key Parameters

_{cmax}of RuBisCO can be constrained by OCO-2 SIF and flux tower GPP, we conducted a parameter sensitivity analysis and estimated the key parameters. Three different objective functions were used to assess the sensitivity of physiological parameters: optimization using SIF as the constraint (F

_{SIF}, Equation (4)), optimization using GPP as the constraint (F

_{GPP}, Equation (5)), and optimization using both SIF and GPP as constraints (F

_{SIF_GPP}, Equation (6)). The performance of the model was evaluated using the coefficient of determination (R

^{2}) of the linear regression between the measured and estimated values of SIF and GPP, measuring how much variation in the observations was explained by the models. The root mean square error (RMSE) was also used to evaluate the performance of the model. We minimized the following objective functions, respectively.

_{i}

_{,sim}and SIF

_{i}

_{,obs}are the simulated and observed SIF at each time step, respectively; and GPP

_{i}

_{,sim}and GPP

_{i}

_{,obs}are the simulated and observed GPP at each time step, respectively; m is the number of timesteps; and std

_{GPP}and std

_{SIF}are the standard deviations of the observed GPP and SIF, respectively. The cost function for the optimization based on both SIF and GPP as constraints (Equation (6)) used the summed normalized errors of SIF and GPP to represent the total model error in simulating both SIF and GPP.

_{i}is the partial variance of the i-th parameter on output Y; V(Y) is the total unconditional output variance; S

_{ij}is the contribution to the total variance by the interactions between parameters i and j; and X

_{~i}denotes variation in all input parameters. Following previous studies [32,64], the sensitivity indices of the EFAST can be derived at the cost of nk model evaluations for robust results (n is the sample size and k is the number of input parameters). Aside from the GSA approach that is based on the SIMLAB, we also used a local SA approach, the “one-factor-at-a-time” (OAT) approach to analyze the sensitivity of SIF and GPP simulations to V

_{cmax}and light conditions. Since GOME-2 and OCO-2 SIF products are based on different wavelengths, we conducted a sensitivity analysis with different V

_{cmax}values at different wavelengths to identify the feasibility and sensitivity of satellite-derived SIF in constraining GPP simulations.

_{cmax}easily. To reduce the computational burden, we also used an adaptive surrogate modeling-based optimization method [38] to optimize the key parameters of the model in simulating GPP and SIF. Although the surrogate-based method might not be able to provide exactly the same optimal solutions as the original models, it has been proven to be effective and efficient in obtaining approximate optimal parameters for land surface and radiative transfer models [36,37,62]. The surrogate-based method can acquire acceptable optimized results with fewer simulations. Following a previous study [62], we applied an adaptive, nonlinear regression method, Gaussian processes regression (GPR), to approximate and replace the original computation model with the surrogate model [38]. Previous studies [37,62] showed that the GPR method outperformed other surrogate methods. According to the results of the parameter sensitivity, we selected the key physiological parameters (i.e., V

_{cmax}, Rd, and fqe) that are related to SIF and GPP estimation in the model for optimization. The SCE method [35] was used to find the optimal parameters. Similar to the parameter sensitivity analysis, the estimation of the parameters was also based on three different objective functions using SIF and GPP to optimize parameters separately and also using both SIF and GPP to constrain parameters at the same time. We used the data from 2015 to 2017 for calibration, and the remaining data for validation. We evaluated the performance of the SCOPE model for simulating SIF using both OCO-2 and GOME-2 SIF observations.

## 3. Results

#### 3.1. Parameter Sensitivity Analysis of the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) Model

_{cmax}was one of the least sensitive parameters (Figure 3a). The simulated SIF was sensitive to fqe, Cab, and LAI with the total-order impact ratio of 48.0%, 18.1%, and 16.7%, respectively. The other parameters had relatively low impact ratios (less than 5%). However, for the GPP simulation, the most sensitive parameters were V

_{cmax}, LAI, Rd, and Cab with the total-order impact ratio of 41.7%, 31.4%, 16.1%, and 5.4%, respectively, and other parameters had relatively low impact ratio (Figure 3b). For the objective function of both SIF and GPP, both fqe and V

_{cmax}were the most sensitive parameters (Figure 3c); both GPP and SIF were sensitive to V

_{cmax}, Cab, Rd, fqe, and LAI with the total-order impact ratio of 21.9%, 21.2%, 17.7%, 15.3%, and 13.2%, respectively, while other parameters had relatively low impact ratio. Both LAI and Cab were used as input variables for the simulation of GPP and SIF and were therefore not optimized in this study.

_{cmax}, we conducted a sensitivity analysis with different V

_{cmax}values ranging from 10 to 200 μmol m

^{−2}s

^{−1}(Figure 4). The SIF spectrum ranged from 640 to 850 nm with two peaks centered at 685 nm and 740 nm. The sensitivity of SIF to V

_{cmax}was higher at 757 nm than at 771 nm. The GOME-2 wavelength (740 nm) is right at one of the SIF emission peaks, which is also the V

_{cmax}sensitivity band for SIF prediction. The sensitivity of SIF to V

_{cmax}at the GOME-2 wavelength (740 nm) was higher than that of the OCO-2 wavelengths (757 nm and 771 nm).

_{cmax}and light conditions, we further performed a sensitivity analysis with varying V

_{cmax}values and light conditions (Figure 5). With varying V

_{cmax}at the constant irradiance, SIF did not significantly increase with increasing V

_{cmax}; for a given V

_{cmax}value, SIF increased with increasing solar irradiance (Figure 5a). In contrast, GPP increased with increasing V

_{cmax}, particularly at higher light conditions, and light saturation of GPP occurred under lower light conditions with lower V

_{cmax}values (Figure 5b).

#### 3.2. Parameter Estimation of the SCOPE Model

_{cmax}, Rd, and fqe) in SIF and GPP simulations are given in Table 2. These optimized values were used for the SCOPE model simulation at Park Falls. Figure 6 shows the performance of SCOPE for estimating GPP and SIF at Park Falls, which illustrates the seasonal trajectories of GPP and SIF simulated by SCOPE with the objective function of SIF and GPP, respectively. The simulated GPP and SIF were validated with the flux tower-based GPP and OCO-2 SIF data, respectively. We found that the simulated GPP was well correlated with flux tower GPP with R

^{2}values of 0.45 and 0.88 (RMSE = 2.11 and 1.20 gC m

^{2}day

^{−1}) at the daily time scale for the objective function of SIF and GPP, respectively. The simulated SIF was strongly correlated with OCO-2 SIF with R

^{2}values of 0.77 and 0.76 for the 757 nm wavelength (the RMSE were 0.14 and 0.54 W m

^{−2}μm

^{−1}sr

^{−1}), and 0.75 and 0.74 for the 771 nm wavelength (the RMSE were 0.06 and 0.27 W m

^{−2}μm

^{−1}sr

^{−1}) for the objective functions of SIF and GPP, respectively. Our results showed that the model performance varied with the objective function: GPP was better simulated with GPP as the objective function, and similarly, SIF was better simulated with SIF as the objective function.

^{2}= 0.90 and RMSE = 0.90 gC m

^{−2}day

^{−1}for daily GPP, R

^{2}= 0.77 and RMSE = 0.15 W m

^{−2}μm

^{−1}sr

^{−1}for SIF at the 757 nm wavelength, and R

^{2}= 0.74 and RMSE = 0.07 W m

^{−2}μm

^{−1}sr

^{−1}for SIF at the 771 nm wavelength.

^{2}= 0.88 and RMSE = 1.47 gC m

^{2}day

^{−1}for daily GPP, R

^{2}= 0.70 and RMSE = 0.15 W m

^{−2}μm

^{−1}sr

^{−1}for SIF at 757 nm, R

^{2}= 0.61 and RMSE = 0.09 W m

^{−2}μm

^{−1}sr

^{−1}for SIF at 771 nm). The simulated GPP and SIF based on the optimized parameters were more consistent with flux tower GPP and OCO-2 SIF in seasonal trajectories than the simulated GPP and SIF based on the default parameters (Figure 8).

#### 3.3. Predicting Long-Term SIF and GPP Using the Optimized Model

#### 3.4. Evaluating the Capability of SIF in Estimating GPP

## 4. Discussion

#### 4.1. Parameter Sensitivity of the SCOPE Model

_{cmax}, Rd, fqe). SIF is linked to the maximum carboxylation capacity, V

_{cmax}, an important parameter that determines the capacity of photosynthesis [15,42], while their complex relationship is still not well understood. It is debatable to what extent SIF is sensitive to V

_{cmax}across biomes [41,45]. A previous study [41] showed strong linear relationship between SIF and V

_{cmax}using the SCOPE model and estimated seasonally-varying V

_{cmax}based on the GOME-2 SIF product. In contrast, other studies have shown that the relationship between V

_{cmax}and SIF within the SCOPE model was not stable and varied between different versions [19,20,39,45]. In this study, we used Saltelli’s method and OAT approach to quantify the sensitivity of SIF and GPP to key parameters in SCOPE at two temperate forest sites. Both approaches showed that SIF had low sensitivity to V

_{cmax}, while GPP had high sensitivity to this parameter. Our results are inconsistent with the finding of a previous study [41] based on the old version of the SCOPE model that there is a strong linear relationship between SIF and V

_{cmax}. Our finding is instead consistent with the previous results of SIF simulation using SCOPE v1.7 [19,44,62], showing a relatively low sensitivity of SIF to photosynthesis in the global parameter space of the model.

_{2}concentration [65]. It is noticed that the V

_{cmax}cannot be constrained by the optical data alone, and the GPP simulation was improved by combined use of optical and thermal infrared data [66]. Previous studies have demonstrated that V

_{cmax}only partly affected SIF [16,43]; our study also confirmed that SIF was less sensitive to V

_{cmax}.

_{cmax}is the most important parameter for GPP simulation. The physiological parameters such as Cab and LAI are also sensitive parameters in SIF and GPP simulations, which is similar to the finding of a previous study [62]. Meanwhile, we also found that the SIF and GPP were constrained well by the concurrent use of OCO-2 SIF and flux tower GPP data. V

_{cmax}is a parameter that directly influences the photosynthetic capacity and indirectly influences the SIF simulation.

#### 4.2. Parameter Estimation and Model Evaluation

_{cmax}or using a module of the SCOPE model) to optimize the parameters of the SCOPE model (e.g., [19,41]). As above-mentioned, we found that the relationship between SIF and V

_{cmax}was not statistically significant in version 1.70 of the SCOPE model, while the simplified SCOPE module (i.e., only using a module of the SCOPE model) cannot reflect the linkages between SIF and photosynthesis processes. Therefore, we utilized the surrogate modeling-based approach to optimize the parameters of the model because this approach requires a limited number of model runs and is more computationally efficient. The surrogate approach has been proven to be efficient and effective in searching the approximate optimal parameters in previous studies (e.g., [37,40]). Our study showed that the surrogate approach is able to estimate parameters of a relatively complex model like SCOPE. However, it should be noted that the surrogate model is not the “real” model, and it is designed to use cheaper, approximate solutions to a simulation model. Its performance depends on multiple factors such as the surrogate method choice, optimization methods, and the initial sample size and complexity of the problems [37,40].

_{cmax}can effectively improve the estimation of photosynthetic capacity [19]. Previous studies have mainly utilized coarse-resolution SIF observations from GOME-2 [12,13,47], and few studies have used finer-resolution OCO-2 SIF to constrain parameters in the SCOPE model (e.g., [44]). We used both OCO-2 SIF and flux tower GPP data to constrain the SIF and GPP simulation by optimizing photosynthetic parameters and SIF related parameters. The optimized SCOPE model had satisfactory performance in simulating SIF and GPP. Similar to two previous studies [44,45], our study demonstrated that the OCO-2 SIF measurements constrained the SIF related parameters such as fqe well but not V

_{cmax}. Our study demonstrated that the OCO-2 SIF observations could improve the SIF simulation of the SCOPE model by optimizing uncertain parameters.

#### 4.3. Uncertainties and Future Perspectives

## 5. Conclusions

_{cmax}was greater than that of SIF simulation in SCOPE. The performances of SIF and GPP estimation were improved with the inverse estimation of key parameters in the model. Incorporating the OCO-2 SIF data constrained key uncertain parameters related to SIF simulation, but did not significantly improve the GPP simulation, suggesting that SIF observations have low capability in constraining photosynthetic capacity. The concurrent use of satellite SIF observations and flux tower GPP data improved the simulations of both SIF and GPP. The optimized model can be used to simulate SIF and GPP continuously for the past, present, and future. The simulated SIF can also potentially be used to fill the data gaps for satellite SIF products like OCO-2 to produce spatially and temporally continuous SIF products and to improve the diagnosis of carbon cycle at large scales.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Orbiting Carbon Observatory-2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) observations surrounding the Park Falls (US-PFa) and Willow Creek (US-WCr) sites on 1 July 2015. The base map is the MODIS land cover product (MCD12Q1) at 500 m spatial resolution.

**Figure 2.**Seasonality of (

**a**) leaf area index (LAI) and (

**b**) chlorophyll (Cab) at the Park Falls site retrieved by inverting the PROSAIL model with Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance (bands 1–7).

**Figure 3.**Global sensitivity analysis (GSA) total sensitivity index for SCOPE-simulated SIF Orbiting Carbon Observatory-2 solar-induced chlorophyll fluorescence (OCO-2 SIF) and gross primary production (GPP). Three different objective functions were used: (

**a**) SIF, (

**b**) GPP, and (

**c**) SIF and GPP together.

**Figure 4.**The sensitivity of simulated SIF to V

_{cmax}values in the SCOPE model. We used 20 different V

_{cmax}values ranging from 10 to 200 μmol m

^{−2}s

^{−1}.

**Figure 5.**Modeled SIF (

**a**) and GPP (

**b**) at the leaf level as a function of V

_{cmax}(μmol m

^{−2}s

^{−1}) at different levels of light (Rin): 100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 W m

^{−2}. V

_{cmax}ranged from 10 to 200 μmol m

^{−2}s

^{−1}.

**Figure 6.**Seasonal trajectories of GPP and SIF simulated by the SCOPE model at the Park Falls site. The black lines stand for the model results with the default parameters. The green lines and blue lines represent the simulations based on the parameters optimized with SIF and GPP, respectively (i.e., using the objective functions of F

_{SIF}and F

_{GPP}, respectively). The symbols stand for flux tower GPP (

**a**) and OCO-2 SIF (

**b**,

**c**).

**Figure 7.**Seasonal trajectories of GPP and SIF simulated by the SCOPE model at the Park Falls site. The black lines and blue lines represent the modeled GPP based on the default parameters and GPP based on the parameters optimized from both SIF and GPP (using the objective function of F

_{SIF_GPP}), respectively. The magenta symbols stand for flux tower GPP data (

**a**) and OCO-2 SIF observations (

**b**,

**c**).

**Figure 8.**Seasonal trajectories of GPP and SIF simulated by the SCOPE model at the Willow Creek site. The black lines and blue lines represent the modeled GPP based on the default parameters and modeled GPP based on parameters optimized with both SIF and GPP, respectively. The magenta symbols stand for flux tower GPP (

**a**) and OCO-2 SIF observations (

**b**,

**c**).

**Figure 9.**Seasonal trajectories of SIF and GPP simulated by the SCOPE model at Park Falls. The black lines and blue lines represent the modeled values with the default and optimized parameters, respectively. The magenta symbols stand for flux tower GPP (

**a**), OCO-2 SIF (

**b**,

**c**), and GOME-2 SIF (

**d**).

**Figure 10.**Comparison of flux tower GPP (GPP

_{EC}) against GPP simulated by the SCOPE model at Park Falls using (

**a**) simulated hourly GPP with default parameters; (

**b**) simulated hourly GPP with optimized parameters; (

**c**) simulated daily GPP with default parameters; (

**d**) simulated daily GPP with optimized parameters.

**Figure 11.**Comparison of simulated SIF (in GOME-2 SIF wavelength) against GOME-2 SIF observations from 2007 to 2017. The black points and blue points represent the SIF simulation with default parameters and that with the optimized parameters, respectively.

**Figure 12.**Relationships between SIF simulated by the SCOPE model and flux tower GPP at the Park Falls site at both instantaneous and daily timescales.

**Figure 13.**Relationships of flux tower GPP (GPP

_{EC}) with the SIF (by (

**a**) OCO-2 at the 757 nm; (

**b**) OCO-2 at the 771 nm; (

**c**) GOME-2 at the 740 nm), (

**d**) MODIS EVI, and GPP simulated by ((

**e**) SIF at 757 nm, (

**f**) SIF at 757 nm, (

**g**) SIF at 757 nm, and (

**h**) EVI) predicted by the optimized SCOPE model for the period 2003–2017.

**Table 1.**Physiological and structural parameters in the PROSAIL and Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model.

Parameters | Acronyms | Definition | Initial Values | Value Ranges | References |
---|---|---|---|---|---|

Chlorophyll content | Cab | μg cm^{−2} | 40 | 0~100 | [24] |

Carotenoid content | Cca | μg cm^{−2} | 10 | 0~30 | [24] |

Dry matter content | Cdm | mg cm^{−2} | 5 | 0~20 | [24] |

Equivalent water thickness | Cw | mg cm^{−2} | 20 | 0~100 | [62] |

Senescent material | Cs | - | 0.1 | 0~1.2 | [62] |

Anthocyanins | Cant | μg cm^{−2} | 0 | 0~40 | [59] |

Leaf structure parameters | N | - | 1.4 | 1~3 | [24] |

Maximum carboxylation capacity | V_{cmax} | μmol m^{−1} s^{−1} | 30 | 0~200 | [62] |

Stomatal conductance parameter | m | - | 8 | 2~20 | [62] |

Extinction coefficient for canopy | Kv | - | 0.64 | 0~0.8 | [62] |

Dark respiration parameter | Rd | - | 0.015 | 0.001~0.03 | [62] |

Fluorescence quantum efficiency | fqe | - | 0.01 | 0.001~0.03 | [63] |

Leaf area index | LAI | m^{2} m^{−2} | 3 | 0~7 | [24] |

Leaf angle distribution parameter a | LIDFa | - | −0.35 | −1~1 | [24] |

Leaf angle distribution parameter b | LIDFb | - | −0.15 | −1~1 | [24] |

**Table 2.**The key physiological parameters related to SIF and GPP estimation in the SCOPE model. The initial values and optimized values using three different objective functions were shown: (1) F

_{SIF}, optimized SIF; (2) F

_{GPP}, optimized GPP; (3) F

_{SIF_GPP}, optimized SIF and GPP. The units of V

_{cmax}are μmol m

^{−2}s

^{−1}, and the units of SIF and GPP are W m

^{−2}μm

^{−1}sr

^{−1}and gC m

^{−2}day

^{−1}, respectively.

Parameters | Initial Values | Optimized SIF (F_{SIF}) | Optimized GPP (F_{GPP}) | Optimized SIF and GPP (F_{SIF_GPP}) |
---|---|---|---|---|

V_{cmax} | 30 | 66.648 | 34.22 | 32.88 |

Rd | 0.015 | 0.021 | 0.002 | 0.006 |

fqe | 0.01 | 0.008 | 0.030 | 0.008 |

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**MDPI and ACS Style**

Wang, H.; Xiao, J.
Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers. *Remote Sens.* **2021**, *13*, 794.
https://doi.org/10.3390/rs13040794

**AMA Style**

Wang H, Xiao J.
Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers. *Remote Sensing*. 2021; 13(4):794.
https://doi.org/10.3390/rs13040794

**Chicago/Turabian Style**

Wang, Haibo, and Jingfeng Xiao.
2021. "Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers" *Remote Sensing* 13, no. 4: 794.
https://doi.org/10.3390/rs13040794