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Article

Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors

1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3
Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
4
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
5
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
6
University of Chinese Academy of Sciences, Beijing 100049, China
7
Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading RG6 7BE, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(3), 698; https://doi.org/10.3390/rs15030698
Submission received: 21 November 2022 / Revised: 19 January 2023 / Accepted: 20 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)

Abstract

:
The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures ( T opt eco ) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.

1. Introduction

The total atmospheric carbon dioxide (CO2) assimilated by vegetation is known as gross primary productivity (GPP) and is generally considered the most significant carbon flux in the carbon cycle of terrestrial ecosystems, which plays an important role in global carbon and the climate system [1,2]. Especially in the background of rapid global climate change, accurate estimation of GPP is particularly essential and urgent [3].
The flux observation network based on the eddy covariance technique supplies lots of observational data (such as FLUXNET, Ameriflux, and Chinaflux), which have been used by a large number of studies as standard ground data [4,5,6]. However, due to the finite number and non-uniform distribution of sites, it is still unable to meet the needs of vegetation-productivity monitoring and evaluation at the larger scales [7]. In recent years, many studies have developed multifarious models including meteorological-based models [8,9], process-based models [10,11,12,13], light use efficiency (LUE) models [3,14,15,16,17,18,19,20], and data-driven models [21,22,23,24] to quantify GPP at different scales based on remote sensing data. Among these models, the LUE model has wide utilization due to its high accuracy, simple form and easy access to the input data [3,25,26]. However, the interannual variability of GPP estimated by the LUE models still deviates considerably from in situ observations [26,27]. Stocker et al. [28] suggested that one of the main reasons for the uncertainty in the GPP estimation is that the effects of environmental factors on photosynthesis are not fully incorporated into the LUE model.
Typically, GPP can be calculated using the LUE model as described in Equation (1):
G P P = ε m a x × A P A R × f s t r e s s
where the εmax and APAR are the maximum LUE and the absorbed photosynthetically active radiation, respectively. The f (stress) represents the environmental factor used to adjust the εmax. Among all environmental factors, temperature has an important influence on enzyme activity and electron transport rate, and a sufficient water supply ensures stomatal openness and physiochemical reactions in plants, which are the most common environmental stress factors in the LUE models [25,29]. However, most LUE models set the temperature parameter as a constant for each vegetation type, which ignores the spatial differences in the influence of temperature [30,31,32].
In recent years, several studies have proposed methods to calculate optimal temperatures [30,33,34], and Huang et al. [34] have published the first global map of optimal ecosystem temperatures ( T o p t e c o ), which may help to reduce the large uncertainty in GPP estimates due to temperature parameters. In situ GPP allows the estimation of site optimum temperatures, while at the regional scale, satellite-retrieved solar-induced chlorophyll fluorescence (SIF) capable of observing vegetation photosynthesis may be a better option than the traditional vegetation indices used in previous studies. Moreover, atmospheric CO2 can diffuse through stomata into leaves and is a crucial driver of photosynthesis. Since the 1980s, global atmospheric CO2 concentrations have increased by approximately 23%, contributing to increased photosynthesis and GPP. Although many studies have recognized the importance of the CO2 fertilization effect, it has not been integrated into most LUE models, except for CFix [35], P-model [36], revised EC-LUE [26], PRELES model [37], and the Dry Matter Productivity (DMP) of the Copernicus global land service, which could result in the insensitivety of the GPP estimates to the increasing CO2 concentrations. In addition, sunlit leaves are more likely to reach light saturation due to the simultaneous reception of direct and diffuse radiation [3,14,38]. So far, the impact of the radiation component on LUE has been agreed in many studies [3,14,19,26,39].
Nitrogen (N) is one of the most important components of enzymes and pigments and plays a crucial role in photosynthesis, which has not been considered in almost all LUE models [40,41,42]. In LUE models, the εmax is assumed to be a constant value for each vegetation type, but it should be dynamic under various environmental conditions [43,44]. Many studies found that the εmax is linearly related to canopy N concentrations and that the εmax increases with increasing N deposition [45,46]. Therefore, introducing canopy N concentrations into the LUE model to calculate the εmax with seasonal variation may make the LUE model more suitable for the actual situation of leaves adapting to environmental conditions changes [47].
The currently available canopy or leaf N products were compressed to a static value that lacked temporal variation and cannot reflect changes in the εmax [41,48,49]. Furthermore, there may be high uncertainties and errors in canopy N content simulations based on radiative transfer models due to their complex parameters and calculation processes [50]. In recent years, several vegetation indices based on satellite or in situ measurements of reflectance have been developed to estimate canopy N concentrations at local scales due to their simple calculations and high accuracy [40,50,51,52,53]. Therefore, calculating the εmax using the vegetation index characterizing canopy N concentrations not only introduces N into the LUE model, but also enables the dynamic εmax, which may have implications on the GPP simulation and facilitate the evaluation of the long-term impacts of N deposition on GPP.
In this study, an LUE model (NI-LUE) that integrated temperature, water, radiation components, CO2 fertilization effects, and N was developed. In this model, a vegetation index capable of characterizing canopy N concentrations was selected to achieve dynamic εmax. In addition, global T o p t e c o distribution mapped based on satellite-retrieved SIF were introduced to calculate the temperature stress factor. Our primary aim is to estimate global GPP and produce a novel dataset (0.05°, 8 days). We validated and evaluated our product using in situ observations at daily and annual scales. Moreover, multiple existing global GPP products were collected to indicate the effectiveness of the interannual variation and spatial distribution of our products.

2. Data

2.1. Site Data

The FLUXNET2015 dataset (https://FLUXNET.fluxdata.org/ (accessed on 11 October 2022)) includes carbon flux and other meteorological variables over 200 sites globally. In this study, we obtained daily GPP based on the nighttime partitioning method (GPP_NT_VUT_REF) along with meteorological variables including incident shortwave radiation (SW), air temperature (Ta), vapor pressure deficit (VPD), latent heat flux (LE), and sensible heat flux (H) for parameterizing model and validating the product. We chose high-quality data with a quality flag above 0.8 and deleted the negative GPP values. Firstly, we aggregated the daily values to 8-day time steps to match the leaf area index (LAI) product. Then, we verified the homogeneity of the flux sites within 5 km using high spatial resolution images. Finally, we selected 104 sites where the land cover type is consistent with the moderate-resolution imaging spectroradiometer (MODIS) land cover product (MCD12Q1), including 12 vegetation types: evergreen needleleaf forest (ENF), evergreen broadleaf forest, deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrubland (CSH), open shrubland (OSH), woody savanna (WSAV), savanna (SAV), grassland (GRA), permanent wetland (WET), and cropland (CRO). The location information of these sites is shown in Table A4.
To corroborate the ability of the selected vegetation index to characterize canopy N concentrations, we collected canopy mean N concentrations measurements from previous studies that had time stamps (Table A2). The canopy N concentrations were determined by the mean of dry-mass-based foliar N concentrations for all species in multiple field plots within each site. For detailed measurements of the canopy N concentration, please refer to Ollinger et al. [53] and Ollinger and Smith [54].

2.2. Global Scale Data

Here, we used the Global land surface satellite (GLASS) Advanced Very High Resolution Radiometer (AVHRR) LAI product (0.05° and 8-day) from 2001 to 2018 as vegetation structure parameters to drive the models and used it to decompose the APAR into the APAR of shaded leaves (APARsh) and sunlit leaves (APARsu) [55] (http://www.glass.umd.edu/LAI/AVHRR/ (accessed on 11 October 2022)).
The NOAA Earth System Research Laboratory (ESRL) provided global three-hourly distributions of CO2 mole fraction with 3° × 2° spatial resolution [56]. This study obtained CO2 mole fraction data files for CT2019B from 2000 to 2018 and aggregated them into daily CO2 concentrations, which were used to calculate the CO2 fertilization effect in the model. This dataset is available at https://gml.noaa.gov/aftp/products/carbontracker/co2/molefractions/co2_total/. (accessed on 11 October 2022)
ERA-5 is a reanalysis dataset produced by the European Centre for Medium Range Weather Forecasts (ECMWF) with a 0.25° spatial resolution (http://data.ecmwf.int/data (accessed on 11 October 2022)). In this study, we selected daily 2 m dew point temperature, 2 m air temperature, surface net solar radiation, surface net thermal radiation, and surface solar radiation downwards from 2001 to 2018 as the meteorological data to drive the model, and the effect of altitude was also considered in the interpolation of temperature. All meteorological variables above were aggregated to 8-day time steps and resampled to 0.05° resolution using the bilinear interpolation method.
The MODIS land cover product MCD12C1 (https://e4ftl01.cr.usgs.gov/MOTA/MCD12C1.006/ (accessed on 11 October 2022)), from 2001 to 2018, was used to drive the model. Here, we selected the International Geosphere-Biosphere Programme (IGBP) classification product which consists of 17 land cover classifications with a spatial resolution of 0.05° and an annual interval.
To select vegetation indices that characterize canopy N concentrations for the εmax estimation, we collected MODIS reflectance products MCD43A4 and MYDOCGA, which contain MODIS reflectance bands 1 to 7 and bands 8 to 16, respectively. The reflectance data were controlled by quality data, and the original time series vegetation indices were smoothed with the Savitzky-Golay (S-G) filtering method [57].
The SIF data were used to calculate the global T opt eco . Zhang et al. [58] generated a global continuous SIF (CSIF) dataset using satellite-retrieved SIF from the Orbiting Carbon Observatory-2 (OCO-2) and MODIS surface reflectance based on neural networks. We selected the all-sky daily average CSIF dataset at moderate spatiotemporal (0.05°, 4-day), which has strong spatiotemporal dynamics, to characterize the photosynthetic state of vegetation.

2.3. GPP Products Derived from Different Models

We collected multiple global GPP products, including four LUE GPP products, two data-driven GPP products, and two process model products, to compare with the GPP simulations in this study, as shown in Table 1. Among them, the FLUXCOM GPP, LPJ-GUESS GPP, and SDGVM GPP were resampled to 0.05° × 0.05° using a bilinear interpolation method.

3. Methods

3.1. Calculation of the Optimum Temperature for Photosynthesis

We calculated the optimum temperature for photosynthesis at the site scale using in situ GPP and the global scale using satellite SIF data, respectively. Referring to Yang et al. [32], we first corresponded the long time series in situ GPP (or satellite SIF pixel values) to temperature to get the gray scatter plot in Figure 1. Then, these points were divided into multiple temperature bins at 1 °C intervals (as shown in Figure 1, the blue part was a temperature bin), and the 90% quantile of the in situ GPP (or satellite SIF pixel value) in each bin was extracted as the response value at that temperature (as shown in Figure 1, the red point in the blue bin was the response value to this temperature). Finally, the response values in all the temperature bins were connected together to obtain the temperature response curve, and the highest value of the curve was set as the optimum temperature (Figure 1). The optimum temperature calculated by the in situ GPP was used to verify the global optimum temperature based on satellite SIF data.

3.2. Product Algorithm

The NI-LUE model is based on the LUE model considering temperature and water, and further integrates the effect of CO2 fertilization, radiation components and canopy N concentrations. The schematic workflow of the NI-LUE model was shown in Figure 2 and GPP can be estimated as follows:
G P P = p × N I + q × f T × f W × f C O 2 × f P A R s u × A P A R s u + f P A R s h × A P A R s h
where p and q are parameters optimized for different vegetation types; [NI] represents the vegetation index selected to characterize the canopy N concentrations. This study collected the vegetation indices used in previous studies to estimate canopy N (Table A1). These indices were used separately to drive the model and to obtain the optimal vegetation index that would result in the best model performance.
f (T) and f (W) are the scalars of temperature and water, respectively. f (T) was calculated according to the Terrestrial Ecosystem Model (TEM) as the following formula [26]:
f T = T a T m i n T a T m a x T a T m i n T a T m a x T a T o p t 2  
where Ta represents the air temperature (°C). Topt, Tmin and Tmax are the optimum, minimum, and maximum air temperature for vegetation growth, respectively. In this model, Topt was the global T o p t e c o calculated in Section 3.1. Tmin and Tmax were set as shown in Table 2. For f (W), we described it using the actual evapotranspiration (E) and the potential evapotranspiration (Ep) as follows:
f W = 0.5 + 0.5 E E p  
where E was calculated based on the revised Penman-Monteith model. Since the Penman-Monteith model requires the canopy resistance of different vegetation types under sufficient water conditions [65,66], we used the Priestley-Taylor equation for Ep calculations [67,68,69,70,71]. The detailed calculation method can be found in Cui et al. [67].
The effect of CO2 fertilization (f (CO2)) was integrated into our model with reference to the revised EC-LUE model [26] as follows:
f C O 2 = C i φ C i + 2 φ  
C i = C O 2 × χ
where φ is the CO2 compensation point in the absence of dark respiration, which was set for different vegetation types according to Zheng et al. [26] as shown in Table 2; Ci indicates the internal leaf CO2 concentrations and was calculated using the product of the atmospheric CO2 concentrations ([CO2]) and χ, which represents the ratio of the Ci and [CO2] which can be calculated as follows:
χ = ξ ξ + V P D  
V P D = S V P × 1 R H
S V P = 0.6112 × e 17.67 × T a T a + 243.5
R H = e 17.625 × D T D T + 243.04 17.625 × T a T a + 243.04
ξ = 356.51 K 1.6 η *  
where parameter ξ represents the sensitivity of VPD to χ; K is the Michaelis-Menten coefficient of Rubisco; η* is the viscosity of water relative to its value at 25 °C.
K = K c 1 + P o K o
K c = 39.97 × e 79.43 × T K 298.15 298.15 × R × T K  
K o = 27480 × e 36.38 × T K 298.15 298.15 × R × T K
where Po is the partial pressure of O2, approximated as 21,278.25 Pa; Kc and Ko are the Michaelis-Menten constants of CO2 and O2, respectively; TK is the air temperature with unit K; and R is the molar gas constant and is set to 8.314 J/mol/K.
We further considered the effect of radiation components. Since the differences between the LUE of sunlit and shaded leaves are mainly influenced by light intensity, their εmax should be similar [3,72]. Considering the hyperbolic relationship between PAR and LUE, we used the PAR of the shaded (PARsh) and sunlit leaves (PARsu) to calculate the radiation constraints, and we decomposed the APAR into APAR of the shaded (APARsh) and sunlit leaves (APARsu). f (PARsu) and f (PARsh) are the radiation scalars for sunlit and shaded leaves, respectively, calculated as Equations (15) and (16).
f P A R s u = 1 a × P A R s u + 1    
f P A R s h = 1 a × P A R s h + 1
where a is optimized according to different vegetation types.
PARsu, PARsh, APARsu, and APARsh can be calculated based on the BEPS model [38] as follows:
P A R s h = P A R d i f P A R d i f , u L A I + C
P A R s u = P A R d i r × cos β cos θ + P A R s h
P A R d i f , u = P A R d i f × e 0.5 × Ω × L A I cos θ ¯
cos θ ¯ = 0.537 0.025 × L A I
C = 0.07 × Ω × P A R d i r × 1.1 0.1 × L A I × e cos θ
A P A R s u = 1 α × P A R s u × L A I s u  
A P A R s h = 1 α × P A R s h × L A I s h  
where PARdif and PARdir are the diffuse and direct PAR, respectively; PAR(MJ/m2) was calculated using 0.48 times the surface solar radiation downwards from ERA-5; the PARdif was calculated by parameter calibration using the clear sky index [38,73]; PARdir is the residual of PAR minus PARdif; C is the multiple scattering effects of direct radiation; β is set to 60° indicating the mean leaf-sun angle; θ is the solar zenith angle; θ ¯ is a representative zenith angle for diffuse radiation transmission; Ω and α are the clumping index and the canopy albedo, which were set for different vegetation types according to Tang et al. [74] and Zhang et al. [75], respectively (Table 2); LAIsu and LAIsh denote the LAI of sunlit and shaded leaves, respectively, and were calculated as follows:
L A I s u = 2 × cos θ × 1 e 0.5 × Ω × L A I cos θ
L A I s h = L A I L A I s u

3.3. Model Parameterization and Validation

The Shuffled Complex Evolution Procedure Developed at the University of Arizona (SCE-UA) is a global optimization algorithm which aims to find the optimal values of parameters in a particular range which makes sure that the cost function is minimized [76]. The expression of the cost function is as follows:
d = 1 R M S E 2 i = 1 n x i x ¯ 2 + i = 1 n y i x ¯ 2 + 2 × i = 1 n x i x ¯ y i y ¯ i = 1 n x i x ¯ 2   i = 1 n y i y ¯ 2 × i = 1 n x i x ¯ 2 × i = 1 n y i x ¯ 2        
where the RMSE is the root-mean-square error; n is the total number of data used for model parameters’ optimization; x i and y i represent in situ GPP and estimated GPP from the model, respectively; x ¯ and y ¯ are the mean values of in situ GPP and estimated GPP. In this study, p, q, and a in Equations (2), (15), and (16) were optimized using the SCE-UA algorithm. To ensure the robustness of the model, we used a 10-fold cross-validation method to optimize the parameters and validate the model. First, we randomly divided the in situ GPP of each vegetation type into 10 groups, using 9 groups of data to train and optimize the parameters and one group of data for validation, ensuring that all data were involved in training and validating the model. Then, we trained 10 models and obtained 10 sets of parameters, and the average of the these parameters was used for the generation of global GPP products. Finally, the standard deviation of the global GPP generated by the ten sets of parameters was used as the uncertainty of the product.
The coefficient of determination (R2), the RMSE, and the mean absolute error (MAE) were used to evaluate the performace of the GPP estimation. Furthermore, the index of agreement (IOA), which measures the degree of agreement between the estimates and observations, was selected [77]. The logical range of IOA is 0 to 1, where 1 indicates that the model estimates are consistent with the observations, and 0 represents complete disagreement.

3.4. Evaluation of Spatial Performance

To demonstrate the rationality of the spatial distribution of GPP products, we compared the spatial distribution of our product with other GPP products. In this study, the SPAtial Efficiency (SPAEF) was used as a metric to evaluate the consistency of two spatial pattern maps. SPAEF is a bias-insensitive spatial performance metric, which improved the structure of the Kling-Gupta efficiency [78].
SPAEF = 1 r 1 2 + β 1 2 + γ 1 2  
where r is the Pearson correlation coefficient between the two spatial pattern maps, β is the ratio of the coefficient of variations (CV) of the two spatial pattern maps, and γ is the percentage of histogram intersections of the two spatial pattern maps after normalization to a mean of 0 and standard deviation of 1 (z score).

3.5. Contribution of Each Variable to Long-Term Variations in GPP

To assess the contributions of multiple variables to GPP simulation, including the meteorological variables, CO2 concentrations, LAI, land use cover, and nitrogen index, we constructed two types of simulation experiments using our LUE model. The first experiment (SALL) was that the model was running normally, and each variable varied over time. The second experiment (Si0) was to fix one variable (i) in the initial state, and other variables varied with time. Meteorological variables, atmospheric CO2 concentrations, LAI, land cover, and N index were used as fixed variables in the second experiment, respectively, and the interannual changes of GPP simulation (NI-LUEi0) were obtained under the assumption that each variable was unchanged. The total of the differences in the GPP simulations of SALL and Si0 for each year from 2001 to 2018 (∑NI-LUE-NI-LUE i0) was the contribution of variable i to the long-term changes in GPP.

4. Results

4.1. Distribution of Global Ecosystem-Scale Optimum Temperature for Photosynthesis

The distribution of global T o p t e c o based on long-term CSIF was shown in Figure 3a. In general, the average T o p t e c o over the vegetated areas was 20.17 ± 5.57 °C and T o p t e c o varied from 15∼30 °C in approximately 82.47% of the vegetated area. The global T o p t e c o presented a decreasing trend from low latitudes to high latitudes, except that the average T o p t e c o in the vegetated area of the Qinghai-Tibet Plateau was 10.58 ± 3.55 °C, showing a significant spatial gradient. In this study, T o p t e c o calculated by the in situ GPP was used to verify the global T o p t e c o based on satellite SIF, and the relationship between them was shown in Figure 3b. Satellite SIF-derived T o p t e c o was comparable to that calculated by in situ GPP (R2 = 0.63, RMSE = 3.15 °C), which provided support for estimating global T o p t e c o using CSIF data. Among all vegetation types, EBF, CRO, DBF, SAV, and WSA had higher T o p t e c o , while the mean T o p t e c o of ENF, DNF, and GRA were lower (Figure 3c). When we calculated the global GPP, if there were too many invalid data and the temperature response curve was not statistically significant, we used the average optimal temperature of the vegetation type in Figure 3c to replace the optimal temperature of this pixel.

4.2. Canopy N Concentrations Index Selection and Model Parameter Optimization

Vegetation indices that have previously been used to estimate canopy N content were used to drive the model. The performance of each index was evaluated by comparing the accuracy of GPP estimated by the models driven by in situ meteorological data and these indices (Figure 4). Among all vegetation indices, the GPP simulation using CIgreen performed the best, followed by the chlorophyll/carotenoid index (CCI), the green normalized difference vegetation index (GNDVI) and the near-infrared reflectance (NIR).
Moreover, we collected some measured canopy N concentrations data to demonstrate the correlation between CIgreen and canopy N concentrations. As shown in Figure 5, there was a strong correlation between CIgreen and canopy N concentrations (r = 0.82). Similarly, many studies also suggested that CIgreen can be used to quantify canopy N concentrations. For example, He et al. [52] found that the R2 between the canopy N concentrations of winter wheat and CIgreen at different viewing zenith angles reached 0.6~0.75; Mutowo et al. [79] predicted the woodland leaf N concentrations using random forests and found that CIgreen had the highest importance among multiple vegetation indices; Clevers and Gitelson [80] found that CIgreen and canopy N of grass and potato were linearly correlated (R2 of 0.77 and 0.89, respectively). Therefore, we selected CIgreen as the nitrogen index (NI) in this study to characterize the canopy N concentrations and introduced it into the LUE model to calculate the εmax.
As shown in Figure 6, we verified the accuracy of the GPP simulation using CIgreen and the ERA5 meteorological data based on the 10-fold cross-validation. The model overall has a high estimation accuracy (R2 = 0.63 ± 0.01, RMSE = 2.27 ± 0.05 gC/m2/d, MAE = 1.48 ± 0.03 gC/m2/d, IOA = 0.88 ± 0.01). Parameters optimized according to different vegetation types were shown in Table A3. Among all vegetation types, the GPP estimates of DBF had the highest accuracy, followed by GRA, WSA, and WET. The GPP estimation accuracy of CRO, EBF, and MF was relatively poor, with R2 below 0.5.

4.3. Spatiotemporal Patterns in Global GPP

A global GPP product was produced based on an improved LUE model that simultaneously considered multiple environmental factors and the canopy N concentrations index. The global mean annual GPP, from 2001 to 2018, was 129.7 ± 3.02 PgC. The spatial pattern of global annual GPP was shown in Figure 7. The GPP was high in tropical regions where sufficient water and suitable temperature can satisfy the photosynthesis of vegetation. While the GPP gradually decreased with increasing latitude, cold or arid environmental conditions limited the growth of vegetation.
Figure 8 showed the interannual variation trend of global GPP estimated by the NI-LUE model from 2001 to 2018. In the past 18 years, GPP showed an increased trend in about 60.1% of terrestrial ecosystems. In addition, the GPP in Central Africa, Central South America, Western Europe, Eastern China, and western India showed a rapidly increasing trend. However, there was a significant decreasing trend in eastern South America and tropical Southeast Asia. In high latitudes, GPP varied less and showed an increasing trend.
Additionally, we used 10 sets of parameters obtained by a 10-fold cross-validation to simulate the global GPP separately to determine the uncertainty of the model. Globally, the mean uncertainty of the annual GPP simulation was 15.86 gC/m2/yr. The spatial pattern of the uncertainty in the global GPP simulation (Figure 9) showed that GPP uncertainty was low at middle and high latitudes, while GPP uncertainty was high in eastern South America and central Africa.

4.4. Comparison with Other Global GPP Products

In this study, we used FLUXNET GPP data accumulated over three years to evaluate the daily and yearly simulation accuracy of different global GPP products. Figure 10 showed comparisons between FLUXNET GPP and various GPP products at 8-day time scales. Among all products, the NI-LUE GPP generated in this study had a good performance (R2 = 0.65, RMSE = 2.43 gC/m2/d, MAE = 1.73 gC/m2/d, IOA = 0.89). MuSyQ, VPM, GOSIF, and FLUXCOM GPP products also have high accuracy, with R2 exceeding 0.6. FLUXCOM GPP had the highest R2 of all products, reaching 0.67. The MOD17 GPP product performed the worst, with an R2 of only 0.55 and a significant underestimation. It was worth noting that, compared with other products, the linearly fitted line of NI-LUE GPP was closer to the 1:1 line, and the underestimation phenomenon was significantly weakened (slope = 0.72). Figure 11 further showed the comparison of simulation accuracy of various GPP products for different vegetation types. Compared with different GPP products, the performances of NI-GPP improved in GPP simulations of CRO, DBF, EBF, and GRA. For SAV, WET, and WSA, although GOSIF GPP performed the best among all products, NI-LUE still had an advantage in all products based on LUE models. For ENF, MF, and OSH, the performance of NI-GPP was not outstanding, while MuSyQ GPP, FLUXCOM GPP, and GOSIF GPP performed better for these vegetation types, respectively.
Moreover, the annual GPP simulations of two process-based biophysical models in TRENDY, LPJ-GUESS, and SDGVM, were added to the comparison. As shown in Figure 12, the annual GPP simulations of LPJ-GUESS and SDGVM had poor performance, showing significant underestimation. Compared to the verification of daily GPP estimates, the accuracy of other products improved. Among them, the NI-LUE GPP produced in this study still had a good performance in the annual GPP verification, and the annual GPP of rEC-LUE, MuSyQ, GOSIF, and FLUXCOM also performed well, with an R2 exceeding 0.7. Across all products, the regression line between the annual gross GPP of NI-LUE, MuSyQ, and GOSIF and FLUXNET GPP is closer to the 1:1 line, with a slope exceeding 0.75.
Figure 13a showed the inter-annual variations in the annual total GPP of different products. There were great differences in the global annual total GPP of different products, ranging from 104.48 Pg C to 137.28 Pg C. Among all products, the global annual total GPP of the five LUE models ranged from 108.79 Pg C to 129.70 Pg C. The annual total GPP simulations of two process-based biophysical models in TRENDY differed considerably, with LPJ-GUESS GPP of 106.62 Pg C and SDGVM GPP of 133.65 Pg C. However, these two products had similar trends, and they can reflect the fluctuations around 2009 as well as the NI-LUE GPP produced in this study. For data-driven GPP products, the global annual total GPP of GOSIF GPP was the highest (137.28 Pg C), while that of FLUXCOM GPP was the lowest (104.48 Pg C). The global annual total NI-LUE GPP ranged from 125.09 Pg C to 133.75 Pg C from 2001 to 2018, placing at the middle of the various GPP products, which is in a reasonable range based on statistics for other products. Figure 13b showed the correlation coefficient matrix of the interannual variations of different products. The study showed that the interannual variation of NI-LUE GPP was significantly positively correlated with GOSIF GPP, FLUXCOM GPP, VPM GPP, LPJ-GUESS GPP, and SDGVM GPP, and the correlation coefficients with MOD17 GPP and MuSyQ GPP were only 0.36 and 0.33, respectively. However, since rEC-LUE GPP showed a decreasing trend from 2001 to 2018, it was negatively correlated with most of the GPP products.
To demonstrate that the spatial pattern of NI-LUE GPP products was reasonable, we separately calculated the SPAEF between the mean annual NI-LUE GPP and different GPP products. Figure 14 showed that the SPAEF of NI-LUE GPP and other products were all above 0.5, and the spatial consistency with rEC-LUE and MuSyQ was the highest, with an SPAEF of 0.86 and 0.83, respectively. Due to the lower spatial resolution of LPJ-GUESS and SDGVM, the spatial consistency with NI-LUE GPP was relatively poor, with an SPAEF of 0.51 and 0.55, respectively.

5. Discussion

5.1. Contributions of Multiple Variables to the GPP Simulation

To quantify the contribution of climate variables (including temperature, radiation, and dew point temperature), atmospheric CO2 concentrations, LAI, land cover, and vegetation indices that characterize canopy N concentrations to changes in global annual total GPP from 2001 to 2018, we fixed variable i (i = climate variables, CO2 concentrations, LAI, Land cover, NI) at the initial state to simulate the interannual variation of global GPP (Figure 15). Moreover, Figure 16 showed the spatial patterns of cumulative contributions of various variables to global annual GPP from 2001 to 2018. Under the normal situation (SAll), each variable changes normally over time, and the global annual GPP simulation (NI-LUE) increased at a rate of 0.53 Pg C/yr from 2001 to 2018. In contrast, the global GPP simulation (NI-LUEi0) under other situations showed different trends.
With the climate variables fixed at the initial state (2001), the global annual GPP simulation (NI-LUECli0) increased at a rate of 0.52 Pg C/yr from 2001 to 2018. Among all variables, climate variables may be more of a moderator. In 2009, for example, El Niño led to higher surface temperatures, and warmer and drier climate conditions [81]. The NI-LUE GPP under the normal situation dropped suddenly in 2009, consistent with the GPP trends simulated by the two process-based biophysical models in Figure 13a, while the NI-LUECli0 kept rising during the year. From 2001 to 2018, the cumulative contribution of climate variables to global GPP simulation was 12.39 Pg C. As shown in Figure 16a, the positive contributions of climate variables were mainly distributed in northern and southern South America, western North America, western Europe, central Africa, and southern China, while in eastern North America and the northern coast of South America regions, as well as the island of New Guinea in Southeast Asia, the contribution of climate variables was negative, reducing GPP in these regions.
Atmospheric CO2 concentrations significantly accelerated global GPP increase. The global annual GPP simulation (NI-LUECO20) increased at a rate of 0.33 Pg C/yr with the atmospheric CO2 concentrations fixed in 2001. Considering CO2 concentrations, the trend of global GPP interannual change increased by 60.6%. As the fuel of photosynthesis, a continued rise in atmospheric CO2 contributed positively to the increase in GPP [25,82]. In this study, atmospheric CO2 concentrations cumulatively contributed 30.77 Pg C to the global GPP simulation based on the NI-LUE model from 2001 to 2018. For the spatial pattern of the cumulative contribution of CO2 to GPP simulation from 2001 to 2018 (Figure 16b), it is similar to the global vegetation distribution, with higher contributions in tropical Southeast Asia, central Africa and South America, decreasing from low latitudes to high latitudes.
LAI was mainly used in the NI-LUE model to distinguish shaded leaves and sunlit leaves, and calculate their APAR. With the LAI fixed in 2001, the global NI-LUELAI0 GPP still showed a significant increasing trend with a rate of 0.53 Pg C/yr. Overall, the cumulative contribution of LAI to global GPP from 2001 to 2018 was -8.12 Pg C. It was obvious that, from 2006 to 2009 and 2018, LAI played an important role in the reduction of global GPP simulations based on the NI-LUE model (Figure 15). For the spatial pattern of cumulative contributions to LAI (Figure 16c), the positive contribution of LAI was mainly distributed in northeast China, central and southern North America, and central Brazil, while the negative contribution of LAI was obvious in southern Brazil, southeastern Africa, and Australia. It was worth noting that the contribution of LAI was most likely to depend on the LAI product driving the model, and its initial value in 2001 may determine the contribution to GPP.
Keeping the land cover in its initial state had little effect on the global GPP simulation, with the NI-LUELC0 GPP increasing at a rate of 0.54 Pg C/yr. The contribution of land cover changes to GPP from 2001 to 2018 was only −1.88 PgC. Figure 16d showed that the contribution of land cover was concentrated near the equator. In East South America, Central Africa, Central Europe, and Southeast Asia, land cover made a significant negative contribution.
CIgreen, an index to characterize the canopy N concentrations (NI) in the NI-LUE model, was used to calculate dynamic εmax, which had a significant impact on global GPP simulations. The seasonal variability of the εmax in the LUE model could make the model more suitable because the foliage responds adaptively to the seasonal fluctuations in the environmental conditions [47]. In this study, we used satellite reflectance to calculate NI, and although N itself may not be the only variable driving the observed pattern, leaf N concentrations influence leaf traits related to photosynthetic capacity that affect reflectance [53]. Canopy N concentrations may explain the temporal and spatial variation of the εmax [46]. In fact, the εmax is a large source of uncertainty. Even for the same vegetation, the εmax may be different [83]. Fixing NI at the initial state in 2001, the increasing rate of the global NI-LUENI0 GPP, from 2001 to 2018, was significantly lower than that of the original NI-LUE GPP, at 0.19 Pg C/yr. Moreover, NI contributed 62.55 Pg C cumulatively to global GPP simulations from 2001 to 2018. The cumulative contribution of NI was concentrated in northern South America, central Africa, southeastern China, and Southeast Asia.

5.2. Uncertainties Analysis

In general, the photosynthesis of the C4 and C3 plants under the same conditions is quite different [84]. In our model, the same coefficients of C3/C4 plants may cause uncertainties. In future research, we may need fine biotype products to optimize the parameters of C3/C4 vegetation separately, which would be beneficial in avoiding some uncertainties for cropland and grassland [84,85].
In this study, CIgreen was selected as the nitrogen index to characterize the canopy N concentrations, but there were still many vegetation indices that we did not consider. For example, regarding Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Double-peak Canopy Index (DCNI), previous studies demonstrated that these indices performed well in estimating canopy N concentrations [51,86]. However, the spectral resolution of the sensors and the time span of the available data prevented us from applying these indices to global long time-series studies. In addition, there are many vegetation parameters that have the potential to be applied to the model to adjust the εmax, such as LAI and leaf chlorophyll content. These parameters related to the photosynthetic capacity of vegetation are worthy of further exploration.
The spatial resolution of NI-LUE GPP products is 0.05°, which mainly depends on the input LAI and land cover data. However, land cover changes within a pixel cannot change the main land use type of the pixel, which may not fully reflect the impact of land cover on GPP. Moreover, the footprint of flux sites is around 500~2000 m [87]. Although we screened for flux sites with lower heterogeneity of the underlying surface, there was still a scale mismatch between the input data and flux sites, which may lead to uncertainties in parameter optimization and accuracy verification. Future research may need to develop a method for mixed pixels to optimize parameters.

5.3. Potential Benefits and Applications of the Product

This study showed that the NI-LUE GPP had a high consistency with the FLUXNET GPP. The comparison of NI-LUE GPP with other GPP products also reflected the rationality of the interannual variation and spatial pattern of NI-LUE GPP. The above results suggest that NI-LUE GPP has the potential to complement current global GPP products.
In terms of analyzing global or regional GPP spatial patterns, NI-LUE GPP may discover valuable insights that have not been found in other products due to the consideration of the spatial heterogeneity of optimal temperature and εmax.
Since the NI-LUE model simultaneously considered temperature, water, radiation components, atmospheric CO2 concentrations, and NI, NI-LUE GPP could be used in both long-term GPP trend analysis and change detection. It may have great potential and may help us to identify and quantify the drivers affecting the long-term GPP trends.
In addition, there are significant differences among the many current global GPP products (Figure 13a). Like these global GPP products, NI-LUE GPP cannot be regarded as the actual value of GPP, but it can be used as a new global GPP dataset for comparison with other products or models, which helps to understand the performance of the dataset comprehensively.

6. Conclusions

In this study, we produced a global GPP product at 8-day intervals with a spatial resolution of 0.05° from 2001 to 2018, based on an improved LUE model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiative composition, and canopy N concentrations. Moreover, the spatial heterogeneity of optimum temperatures was considered when calculating temperature stress. CIgreen was introduced into the model as a vegetation index characterizing the canopy N concentrations to achieve spatiotemporally dynamic εmax, which more agrees with the adaptation of leaf photosynthesis to the environment. Validated by FLUXNET GPP, our product performed well on both daily and yearly scales. Further comparisons with other state-of-the-art global GPP products indicated that our GPP product has a reasonable long-term interannual variation and spatial patterns. Overall, this product provides an effective and alternative dataset for capturing spatiotemporal dynamics of GPP at the regional or global scale, and has the potential to assess the response of vegetaion to changes in the climate.

7. Data Availability

The NI-LUE GPP with 0.05° spatial resolution and at 8-day intervals from 2001 to 2018 and its uncertainty data can be accessed at https://zenodo.org/record/7057843.

Author Contributions

H.Z. developed the model, generated the product and wrote the original draft. J.B., Y.W., and Y.P. provided help for writing and coding. P.C.M. and Z.X. assisted in completing the verification and comparison. R.S. provided help for improvement of the model and writing of the draft. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (42271330) and the National Key R&D Program of China (2017YFA0603002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

We thank NASA for providing the MODIS reflectance products (MCD43A4 & MYDOCGA) and all fluxnet communities of the eddy covariance data (FLUXNET2015, https://fluxnet.fluxdata.org/data/ (accessed on 11 October 2022)). We thank Martin Jung for providing FLUXCOM GPP products. We also thank Almut Arneth and Peter Anthony for providing LPJ-GUESS GPP products. We are grateful to Mikko Peltoniemi, Scott V. Ollinger, Takagi Kentaro, and Jeffrey M. Klopatek for the canopy nitrogen concentrations data. We also thank the anonymous reviewers for their helpful suggestions on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Published vegetation indices evaluated in this study.
Table A1. Published vegetation indices evaluated in this study.
IndexFormulationSelected MODIS BandsReferences
Normalized difference vegetation index (NDVI) R 800 R 680 / R 800 + R 680 B2 B1Rouse et al. [88]
Simple ratio (SR) R 800 / R 680 B2 B1Jordan [89]
Green NDVI (GNDVI) R 750 R 550 / R 750 + R 550 B2 B4Gitelson et al. [90]
Optimized soil-adjusted vegetation index (OSAVI) 1.16 × R 800 R 670 R 800 + R 670 + 0.16 B2 B1Rondeaux et al. [91]
Structure insensitive pigment index (SIPI) R 800 R 455 / R 800 + R 680 B2 B3 B1Penuelas et al. [92]
Greenness index (GI) R 554 / R 677 B4 B1Zarco-Tejada et al. [93]
Green chlorophyll index (CIgreen) R 780 / R 550 1 B2 B4Gitelson et al. [94]
Modified transformed Chlorophyll absorption in reflectance index (TCARI) 3 × R 800 R 680 0.2 × R 680 R 550 × R 800 R 680 B2 B1 B4Rondeaux et al. [91]
TCARI/OSAVI TCARI / OSAVI B2 B1 B4Rondeaux et al. [91]
Enhanced vegetation index 2 (EVI2) 2.5 × R 800 R 680 R 800 + 2.4 × R 680 + 1 B2 B1Jiang et al. [95]
Wide dynamic range vegetation index (WDRVI) 0.1 × R 800 R 670 / 0.1 × R 800 + R 670 B2 B1Gitelson [96]
Wide dynamic range vegetation index 3 (WDRVI3) 0.2 × R 800 R 670 0.2 × R 800 + R 670 + 0.667 B2 B1Peng and Gitelson [97]
Modified simple ratio (MSR) R 800 R 670 1 R 800 R 670 + 1 B2 B1Chen [98]
Plant pigment ratio (PPR) R 550 R 450 R 550 + R 450 B4 B3Metternicht [99]
NIRR800B2Ollinger et al. [53]
SPVI 0.4 × 3.7 × R 800 R 670 1.2 × R 550 R 670 B2 B1 B4Vincini et al. [100]
Lichtenthaler Index 2 (LIC2) R 440 / R 690 B3 B1Lichtenthaler et al. [101]
Enhanced vegetation index (EVI) 2.5 × R 800 R 680 R 800 + 6 × R 680 7.5 × R 460 + 1 B2 B1 B3Huete et al. [102]
Difference vegetation index (DVI) R 800 R 680 B2 B1Jordan [89]
Modified Triangular Vegetation Index (MTVI) 1.2 × 1.2 × R 800 R 550 2.5 × R 670 R 550 B2 B4 B1Haboudane et al. [103]
NIRv NDVI × NIR B1 B2Badgley et al. [104]
kNDVI tanh NDVI 2 B1 B2Camps-Valls et al. [105]
Chlorophyll/Carotenoid index (CCI) B 11 B 1 / B 11 + B 1 B11 B1Gamon et al. [106]
Table A2. Canopy N concentrations collected from previous studies.
Table A2. Canopy N concentrations collected from previous studies.
SiteLatitude (°)Longitude (°)Canopy N, % by MassVegetation TypeDateReferences
Bartlett Experimental Forest, NH44.05 −70.72 1.66Mixed northern hardwoodGrowing season between 2000 and 2006Ollinger et al. [53]
Duke Forest Deciduous, NC35.97 −78.90 1.85Oak–hickor
Duke Forest Pine, NC35.97 −78.92 1.47Loblolly pine
Harvard Forest, MA42.53 −71.83 1.95Mixed deciduous
Howland, ME45.20 −67.27 1.16Boreal evergreen
Hubbard Brook, NH43.95 −70.27 2.24Northern hardwoods
Morgan Monroe State Forest, IN39.32 −85.60 2.06Mixed deciduous
Niwot Ridge, CO40.02 −104.47 0.93Subalpine evergreen
Tremper Mount, NY42.08 −73.73 2.35Mixed deciduous
Willow Creek, WI45.80 −89.93 1.79Temperate deciduous
Wind River Experimental Forest, WA45.82 −120.05 0.75Temperate evergreen
Hyytiälä, Finland, HY61.85 24.30 1.2ConiferousSpring 2003Peltoniemi et al. [46]
Abisko, Sweden, AB68.35 18.78 1.79DeciduousJul., Aug. 2003Peltoniemi et al. [46]
Sorø, Denmark, SO55.48 11.63 2.3MixedSummer 2007Peltoniemi et al. [46]
Teshio, Japan, TE45.05 142.10 1.63MixedAug. 2001, Aug. 2002, Aug. 2003Peltoniemi et al. [46]; Takagi et al. [107]
Wind River, USA, WR45.82 −120.05 1.11ConiferousSep. 2003Peltoniemi et al. [46];Klopatek et al. [108]
Table A3. Optimized parameters of the NI-LUE model for different vegetation types.
Table A3. Optimized parameters of the NI-LUE model for different vegetation types.
CRODBFEBFENFMFGRAWSASAVCSHOSHWET
p0.31335 0.07883 0.29767 0.00002 0.00019 0.52879 0.18511 0.31125 0.32329 0.24062 0.78899
q0.57816 1.26957 0.13659 1.73640 1.61985 0.00011 0.91392 1.03839 0.48866 0.33551 0.00002
a0.00002 0.00092 0.00001 0.07253 0.00016 0.01613 0.01487 0.00814 0.00513 0.05444 0.92145
Table A4. FLUXNET sites used in this study.
Table A4. FLUXNET sites used in this study.
SiteIDSiteNameLatitudeLongitudeIGBPStudy Period
AR−SLuSan Luis−33.46 −66.46 MF2009−2011
AU−ASMAlice Springs−22.28 133.25 SAV2010−2014
AU−CprCalperum−34.00 140.59 SAV2010−2014
AU−DaSDaly River Cleared−14.16 131.39 SAV2008−2014
AU−DryDry River−15.26 132.37 SAV2008−2014
AU−GinGingin−31.38 115.71 WSA2011−2014
AU−GWWGreat Western Woodlands, Western Australia, Australia−30.19 120.65 SAV2013−2014
AU−HowHoward Springs−12.49 131.15 WSA2001−2014
AU−RigRiggs Creek−36.65 145.58 GRA2011−2014
AU−StpSturt Plains−17.15 133.35 GRA2008−2014
AU−TumTumbarumba−35.66 148.15 EBF2001−2014
AU−WacWallaby Creek−37.43 145.19 EBF2005−2008
AU−YncJaxa−34.99 146.29 GRA2012−2014
BE−LonLonzee50.55 4.75 CRO2004−2014
BE−VieVielsalm50.30 6.00 MF2001−2014
BR−Sa1Santarem−Km67−Primary Forest−2.86 −54.96 EBF2002−2011
CA−GroOntario—Groundhog River, Boreal Mixedwood Forest48.22 −82.16 MF2003−2014
CA−ManManitoba—Northern Old Black Spruce55.88 −98.48 ENF2001−2008
CA−NS1UCI−1850 burn site55.88 −98.48 ENF2001−2005
CA−NS2UCI−1930 burn site55.91 −98.52 ENF2001−2005
CA−NS3UCI−1964 burn site55.91 −98.38 ENF2001−2005
CA−NS4UCI−1964 burn site wet55.91 −98.38 ENF2002−2005
CA−NS5UCI−1981 burn site55.86 −98.49 ENF2001−2005
CA−NS6UCI−1989 burn site55.92 −98.96 OSH2001−2005
CA−NS7UCI−1998 burn site56.64 −99.95 OSH2002−2005
CA−OasSaskatchewan—Western Boreal, Mature Aspen53.63 −106.20 DBF2001−2010
CA−ObsSaskatchewan—Western Boreal, Mature Black Spruce53.99 −105.12 ENF2001−2010
CA−QfoQuebec—Eastern Boreal, Mature Black Spruce49.69 −74.34 ENF2003−2010
CA−SF1Saskatchewan—Western Boreal, forest burned in 197754.49 −105.82 ENF2003−2006
CA−SF2Saskatchewan—Western Boreal, forest burned in 198954.25 −105.88 ENF2001−2005
CA−SF3Saskatchewan—Western Boreal, forest burned in 199854.09 −106.01 OSH2001−2006
CH−DavDavos46.82 9.86 ENF2001−2014
CN−ChaChangbaishan42.40 128.10 MF2003−2005
CN−DanDangxiong30.50 91.07 GRA2004−2005
CN−Du2Duolun_grassland (D01)42.05 116.28 GRA2006−2008
CN−Du3Duolun Degraded Meadow42.06 116.28 GRA2009−2010
CN−Ha2Haibei Shrubland37.61 101.33 WET2003−2005
CN−HaMHaibei Alpine Tibet site37.37 101.18 GRA2002−2004
DE−GebGebesee51.10 10.91 CRO2001−2014
DE−HaiHainich51.08 10.45 DBF2001−2012
DE−KliKlingenberg50.89 13.52 CRO2004−2014
DE−RuSSelhausen Juelich50.87 6.45 CRO2011−2014
DE−SfNSchechenfilz Nord47.81 11.33 WET2012−2014
DE−SpwSpreewald51.89 14.03 WET2010−2014
DE−ZrkZarnekow53.88 12.89 WET2013−2014
DK−FouFoulum56.48 9.59 CRO2005
ES−AmoAmoladeras36.83 −2.25 OSH2007−2012
ES−LgSLaguna Seca37.10 −2.97 OSH2007−2009
ES−LJuLlano de los Juanes36.93 −2.75 OSH2004−2013
ES−Ln2Lanjaron−Salvage logging36.97 −3.48 OSH2009−2009
FR−GriGrignon48.84 1.95 CRO2004−2014
FR−PuePuechabon43.74 3.60 EBF2001−2014
IT−BCiBorgo Cioffi40.52 14.96 CRO2004−2014
IT−ColCollelongo41.85 13.59 DBF2001−2014
IT−NoeArca di Noe—Le Prigionette40.61 8.15 CSH2004−2014
IT−RenRenon46.59 11.43 ENF2001−2013
JP−MBFMoshiri Birch Forest Site44.39 142.32 DBF2003−2005
JP−SMFSeto Mixed Forest Site35.26 137.08 MF2002−2006
MY−PSOPasoh Forest Reserve (PSO)2.97 102.31 EBF2003−2009
NL−HorHorstermeer52.24 5.07 GRA2004−2011
RU−CheCherski68.61 161.34 WET2002−2005
RU−Ha1Hakasia steppe54.73 90.00 GRA2002−2004
RU−SkPYakutsk Spasskaya Pad larch62.26 129.17 DNF2012−2014
RU−TksTiksi71.59 128.89 GRA2010−2014
RU−VrkSeida/Vorkuta67.05 62.94 CSH2008−2008
SD−DemDemokeya13.28 30.48 SAV2005−2009
SN−DhrDahra15.40 −15.43 SAV2010−2013
US−AR1ARM USDA UNL OSU Woodward Switchgrass 136.43 −99.42 GRA2009−2012
US−ARbARM Southern Great Plains burn site− Lamont35.55 −98.04 GRA2005−2006
US−ARMARM Southern Great Plains site− Lamont36.61 −97.49 CRO2003−2012
US−AtqAtqasuk70.47 −157.41 WET2003−2008
US−BloBlodgett Forest38.90 −120.63 ENF2001−2007
US−CopCorral Pocket38.09 −109.39 GRA2001−2007
US−CRTCurtice Walter−Berger cropland41.63 −83.35 CRO2011−2013
US−Ha1Harvard Forest EMS Tower (HFR1)42.54 −72.17 DBF2001−2012
US−IvoIvotuk68.49 −155.75 WET2004−2007
US−KS2Kennedy Space Center (scrub oak)28.61 −80.67 CSH2003−2006
US−LinLindcove Orange Orchard36.36 −119.09 CRO2009−2010
US−LosLost Creek46.08 −89.98 WET2001−2014
US−Me1Metolius—Eyerly burn44.58 −121.50 ENF2004−2005
US−Me2Metolius mature ponderosa pine44.45 −121.56 ENF2002−2014
US−Me3Metolius−second young aged pine44.32 −121.61 ENF2004−2009
US−Me6Metolius Young Pine Burn44.32 −121.61 ENF2010−2014
US−MMSMorgan Monroe State Forest39.32 −86.41 DBF2001−2014
US−Ne1Mead—irrigated continuous maize site41.17 −96.48 CRO2001−2013
US−Ne2Mead—irrigated maize−soybean rotation site41.16 −96.47 CRO2001−2013
US−Ne3Mead—rainfed maize−soybean rotation site41.18 −96.44 CRO2001−2013
US−NR1Niwot Ridge Forest (LTER NWT1)40.03 −105.55 ENF2001−2014
US−PFaPark Falls/WLEF45.95 −90.27 MF2001−2014
US−SRCSanta Rita Creosote31.91 −110.84 OSH2008−2014
US−SRMSanta Rita Mesquite31.82 −110.87 WSA2004−2014
US−StaSaratoga41.40 −106.80 OSH2005−2009
US−TonTonzi Ranch38.43 −120.97 WSA2001−2014
US−UMBUniv. of Mich. Biological Station45.56 −84.71 DBF2001−2014
US−UMdUMBS Disturbance45.56 −84.70 DBF2007−2014
US−WCrWillow Creek45.81 −90.08 DBF2001−2014
US−WhsWalnut Gulch Lucky Hills Shrub31.74 −110.05 OSH2007−2014
US−Wi4Mature red pine (MRP)46.74 −91.17 ENF2002−2005
US−Wi9Young Jack pine (YJP)46.74 −91.07 ENF2002
US−WkgWalnut Gulch Kendall Grasslands31.74 −109.94 GRA2002
ZA−KruSkukuza−25.02 31.50 SAV2002
ZM−MonMongu−15.44 23.25 DBF2002

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Figure 1. Optimum temperature calculation based on a satellite SIF pixel.
Figure 1. Optimum temperature calculation based on a satellite SIF pixel.
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Figure 2. Datasets and workflow of NI-LUE model to calculate GPP.
Figure 2. Datasets and workflow of NI-LUE model to calculate GPP.
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Figure 3. (a) Global ecosystem-scale optimum temperature for photosynthesis ( T opt eco ); (b) Relationship between in situ GPP and satellite SIF-derived T opt eco ; (c) T opt eco for different vegetation types. The vegetation types include evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrubland (CSH), open shrubland (OSH), woody savanna (WSAV), savanna (SAV), grassland (GRA), permanent wetland (WET), and cropland (CRO).
Figure 3. (a) Global ecosystem-scale optimum temperature for photosynthesis ( T opt eco ); (b) Relationship between in situ GPP and satellite SIF-derived T opt eco ; (c) T opt eco for different vegetation types. The vegetation types include evergreen needleleaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needleleaf forest (DNF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrubland (CSH), open shrubland (OSH), woody savanna (WSAV), savanna (SAV), grassland (GRA), permanent wetland (WET), and cropland (CRO).
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Figure 4. (a) The coefficient of determination (R2), (b) the root-mean-square error (RMSE) (gC/m2/d), (c) the mean absolute error (MAE) (gC/m2/d), and (d) the index of agreement (IOA) of GPP simulations based on different vegetation indices.
Figure 4. (a) The coefficient of determination (R2), (b) the root-mean-square error (RMSE) (gC/m2/d), (c) the mean absolute error (MAE) (gC/m2/d), and (d) the index of agreement (IOA) of GPP simulations based on different vegetation indices.
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Figure 5. Relationship between CIgreen and canopy N concentrations (%). The grey area indicates the 95% confidence interval.
Figure 5. Relationship between CIgreen and canopy N concentrations (%). The grey area indicates the 95% confidence interval.
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Figure 6. 10-fold cross validation of the model driven by ERA5 meteorological data for different vegetation types, using (a) the coefficient of determination (R2), (b) the root-mean-square error (RMSE) (gC/m2/d), (c) the mean absolute error (MAE) (gC/m2/d), and (d) the index of agreement (IOA) as the accuracy evaluation indices. The vegetation types include cropland (CRO), closed shrubland (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savanna (SAV), permanent wetland (WET), and woody savanna (WSAV).
Figure 6. 10-fold cross validation of the model driven by ERA5 meteorological data for different vegetation types, using (a) the coefficient of determination (R2), (b) the root-mean-square error (RMSE) (gC/m2/d), (c) the mean absolute error (MAE) (gC/m2/d), and (d) the index of agreement (IOA) as the accuracy evaluation indices. The vegetation types include cropland (CRO), closed shrubland (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savanna (SAV), permanent wetland (WET), and woody savanna (WSAV).
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Figure 7. Spatial pattern of global GPP estimated by the NI-LUE model during 2001–2018.
Figure 7. Spatial pattern of global GPP estimated by the NI-LUE model during 2001–2018.
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Figure 8. Spatial pattern of interannual variation trend of global GPP estimated by the NI-LUE model from 2001 to 2018.
Figure 8. Spatial pattern of interannual variation trend of global GPP estimated by the NI-LUE model from 2001 to 2018.
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Figure 9. Spatial pattern of the uncertainty in global GPP estimated by the NI-LUE model.
Figure 9. Spatial pattern of the uncertainty in global GPP estimated by the NI-LUE model.
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Figure 10. Comparisons between FLUXNET GPP and various GPP products at 8-day time scales. Dashed lines are 1:1 lines. Red solid lines are linearly-fitted lines.
Figure 10. Comparisons between FLUXNET GPP and various GPP products at 8-day time scales. Dashed lines are 1:1 lines. Red solid lines are linearly-fitted lines.
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Figure 11. Comparison of simulation accuracy of various GPP products for different vegetation types at 8-day time scales.
Figure 11. Comparison of simulation accuracy of various GPP products for different vegetation types at 8-day time scales.
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Figure 12. Comparisons between FLUXNET GPP and various GPP products at yearly time scales. Dashed lines are 1:1 lines. Red solid lines are linearly fitted lines.
Figure 12. Comparisons between FLUXNET GPP and various GPP products at yearly time scales. Dashed lines are 1:1 lines. Red solid lines are linearly fitted lines.
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Figure 13. (a) Comparisons of global annual total GPP from different products; (b) correlation coefficient matrix of inter-annual variations of total GPP of different products.
Figure 13. (a) Comparisons of global annual total GPP from different products; (b) correlation coefficient matrix of inter-annual variations of total GPP of different products.
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Figure 14. SPAtial Efficiency (SPAEF) between NI-LUE GPP and different GPP products.
Figure 14. SPAtial Efficiency (SPAEF) between NI-LUE GPP and different GPP products.
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Figure 15. Interannual variation of the global annual total GPP (NI-LUEi0) with variable i fixed at the initial state.
Figure 15. Interannual variation of the global annual total GPP (NI-LUEi0) with variable i fixed at the initial state.
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Figure 16. Spatial patterns of cumulative contributions of various variables to global annual GPP from 2001 to 2018. (a) Climate variables; (b) atmospheric CO2 concentrations; (c) Leaf area index (LAI); (d) Land cover (LC); (e) Nitrogen index (NI).
Figure 16. Spatial patterns of cumulative contributions of various variables to global annual GPP from 2001 to 2018. (a) Climate variables; (b) atmospheric CO2 concentrations; (c) Leaf area index (LAI); (d) Land cover (LC); (e) Nitrogen index (NI).
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Table 1. Multiple GPP products selected for comparison in this study.
Table 1. Multiple GPP products selected for comparison in this study.
NameModel Type Spatial ResolutionTemporal ResolutionReferences
MOD17 GPPLUE model0.05° × 0.05°8 daysZhao et al. [59]
rEC-LUE GPP0.05° × 0.05°8 daysZheng et al. [26]
VPM GPP0.05° × 0.05°8 daysZhang et al. [60]
MuSyQ GPP0.05° × 0.05°8 daysWang et al. [19]
GOSIF GPPData-driven model0.05° × 0.05°8 daysLi and Xiao [61]
FLUXCOM GPP0.083° × 0.083°8 daysJung et al. [62]
LPJ-GUESS GPPProcess-based model0.5° × 0.5°monthlySmith et al. [63]
SDGVM GPP0.5° × 0.5°monthlyWalker et al. [64]
Table 2. Parameters Tmax, Tmin, clumping index (Ω), and albedo(α) of different vegetation types. The vegetation types include cropland (CRO), closed shrubland (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savanna (SAV), permanent wetland (WET), and woody savanna (WSAV).
Table 2. Parameters Tmax, Tmin, clumping index (Ω), and albedo(α) of different vegetation types. The vegetation types include cropland (CRO), closed shrubland (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grassland (GRA), mixed forest (MF), open shrubland (OSH), savanna (SAV), permanent wetland (WET), and woody savanna (WSAV).
IGBPCROCSHDBFDNFEBFENFGRAMFOSHSAVWETWSA
Tmax484840404840484848484048
Tmin−1−3−1−12.5−11−2−3−10−1
φ a 453432322025574934543654
Ωb0.90.80.80.60.80.60.90.70.80.80.90.8
α c0.1530.1320.1340.1120.1370.1020.1820.1220.1850.1530.1050.134
a Zheng et al. [26]; b Tang et al. [74]; c Zhang et al. [75].
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Zhang, H.; Bai, J.; Sun, R.; Wang, Y.; Pan, Y.; McGuire, P.C.; Xiao, Z. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors. Remote Sens. 2023, 15, 698. https://doi.org/10.3390/rs15030698

AMA Style

Zhang H, Bai J, Sun R, Wang Y, Pan Y, McGuire PC, Xiao Z. Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors. Remote Sensing. 2023; 15(3):698. https://doi.org/10.3390/rs15030698

Chicago/Turabian Style

Zhang, Helin, Jia Bai, Rui Sun, Yan Wang, Yuhao Pan, Patrick C. McGuire, and Zhiqiang Xiao. 2023. "Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors" Remote Sensing 15, no. 3: 698. https://doi.org/10.3390/rs15030698

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