Open AccessArticle

Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales

Xiaocui Wu

^1,2,

Weimin Ju

^1,2,*,

Yanlian Zhou

³,

Mingzhu He

⁴,

Beverly E. Law

⁵

T. Andrew Black

⁶,

Hank A. Margolis

⁷,

Alessandro Cescatti

⁸,

Lianhong Gu

⁹,

Leonardo Montagnani

^10,11,

Asko Noormets

¹²,

Timothy J. Griffis

¹³,

Kim Pilegaard

¹⁴,

Andrej Varlagin

¹⁵,

Riccardo Valentini

¹⁶,

Peter D. Blanken

¹⁷,

Shaoqiang Wang

¹⁸,

Huimin Wang

¹⁸,

Shijie Han

¹⁹,

Junhua Yan

²⁰,

Yingnian Li

²¹,

Bingbing Zhou

³ and

Yibo Liu

²² Show full author list Hide full author list

International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China

School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China

⁴

Numerical Terradynamic Simulation Group, the University of Montana, Missoula, MT 59812, USA

⁵

College of Forestry, Oregon State University, Corvallis, OR 97331, USA

⁶

Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

⁷

Center ďÉtude de la Forêt, Laval University, Quebec City, QC G1V 0A6, Canada

⁸

Institute for Environment and Sustainability, Joint Research Center, European Commission, 20127 Ispra, Italy

⁹

Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

¹⁰

Forest Services, Autonomous Province of Bolzano, Via Brennero 6, 39100 Bolzano, Italy

¹¹

Faculty of Science and Technology, Free University of Bolzano, Piazza Università 5, 39100 Bolzano, Italy

¹²

Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA

¹³

Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USA

¹⁴

Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark

¹⁵

Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Lenisky pr.33, Moscow 119071, Russia

¹⁶

Department for Innovation in Biological, Aro-food and Forest Systems, University of Tuscia, 01100 Viterbo, Italy

¹⁷

Department of Geography, University of Colorado, CO 80309, USA

¹⁸

Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China

¹⁹

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China

²⁰

South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China

²¹

Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China

²²

Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China

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Remote Sens. 2015, 7(3), 2238-2278; https://doi.org/10.3390/rs70302238

Submission received: 20 August 2014 / Revised: 6 February 2015 / Accepted: 14 February 2015 / Published: 25 February 2015

(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)

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Graphical abstract
"> Figure 1
Distribution of all sites with data used for parameter optimization and validation in this study. The background is the MODIS global land cover product (MCD12C1) in 2003. "> Figure 2
The number of site-years within different root mean square error (RMSE) and R2 classes (left) and the averages of RMSE and R2 (right) of GPP simulated using the TL-LUEn, TL-LUE, and MOD17 models in 85 calibration site-years at half-hourly (a,b), daily (c,d), and 8-day (e,f) temporal scales, respectively. "> Figure 3
Average RMSE and R2 of GPP simulated using calibrated TL-LUEn, TL-LUE and MOD17 in the calibration site-years at half-hourly (the first and second columns), daily (the third and fourth columns), and 8-day (the last two columns) scales for individual vegetation types. Note: Broadleaf forest (BF); Mixed forest (MF); Needleleaf forest (NF); Crop (CROP); Grass (GRASS); Shrub (SHRUB). Solid black circles are the means and horizontal error bars denote standard deviations. "> Figure 4
The number of site-years within different RMSE and R2 classes (left) and the averages of RMSE and R2 (right) of gross primary productivity (GPP) simulated using the TL-LUEn, TL-LUE, and MOD17 models in 58 validation site-years at half-hourly (a,b), daily (c,d), and 8-day (e,f) temporal scales, respectively. "> Figure 5
Average RMSE and R2 of GPP simulated using calibrated TL-LUEn, TL-LUE and MOD17 in the validation site-years at half-hourly (the first and second columns), daily (the third and fourth columns), and 8-day (the last two columns) scales for individual vegetation types. Note: Broadleaf forest (BF); Mixed forest (MF); Needleleaf forest (NF); Crop (CROP); Grass (GRASS); Shrub (SHRUB). Solid black circles are the means and horizontal error bars denote standard deviations. "> Figure 6
The RMSE of modeled GPP against tower-derived GPP within different photosynthetically active radiation (PAR) classes for six different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the half-hourly scale. "> Figure 7
The RMSE of modeled GPP against tower-derived GPP within different PAR classes for 6 different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the daily scale. "> Figure 8
The RMSE of modeled GPP against tower-derived GPP within different PAR classes for 6 different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the 8-day scale. "> Figure 9
The average differences of modeled daily GPPs with observations for different ranges of clearness index Q. ΔGPP means the difference between the simulated and tower-derived daily GPP for certain biome. (a–f) denoteΔGPP for 6 different vegetation types (BF, CROP, GRASS, MF, NF and SHRUB), respectively. "> Figure 10
Histograms of the 20000 samples for each parameter generated by the Metropolis-Hasting Algorithm. Note: Only the best and worst cases are shown for each temporal scale owing to space limitation. ">

Versions Notes

Abstract

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale.

Keywords:

gross primary productivity (GPP); light use efficiency model; sunlit and shaded leaves; vegetation types; temporal scales

Graphical Abstract

1. Introduction

Efforts to mitigate climate change require the stabilization of atmospheric CO₂ concentrations [1], which is significantly regulated by exchanges of carbon between terrestrial ecosystems and the atmosphere. Terrestrial gross primary productivity (GPP) is the largest component of the global carbon flux [2] and about 120 Pg C year⁻¹ globally, considerably larger than the carbon annually emitted by human activities (about 9 Pg C year⁻¹) [3]. Consequently, even a small change in GPP is likely to have a significant impact on atmospheric CO₂ concentration. Thus, accurately simulating terrestrial GPP is of great significance to quantifying the global carbon cycle and predicting the future trajectories of the atmospheric CO₂ concentration.

Two approaches have been widely employed to investigate the spatial and temporal variability in GPP using remotely sensed data: (i) remote sensing driven process-based models, and (ii) light use efficiency (LUE) models [4]. The former is based on the mechanistic description of the photosynthetic biochemical processes and scales the Farquhar instantaneous leaf-level biochemical model [5] to the canopy level using big-leaf, two-leaf, and multilayer scaling approaches. A number of process-based models have been successfully applied to quantify spatial-temporal variations of GPP at regional and global scales using remotely sensed vegetation parameters, such as leaf area index (LAI) and land cover types, as inputs. However, the application of these process-based models is limited by the complexity and uncertainty of their parameterization [6].

In contrast, LUE models, such as CASA [7], MOD17 [8], EC-LUE [9], and VPM [10], were developed according to the LUE argument of Monteith [11,12] that productivity is linearly related to the amount of absorbed photosynthetically active radiation (APAR). A fundamental assumption underlying LUE models is that plant canopies behave like a big single-leaf and their LUE is independent of the directional nature of solar radiation and vegetation structure [13].

Many studies have indicated that both GPP and LUE vary with both quantity and quality of incoming solar radiation. Gu et al. [14] detected a 20% increase in Harvard Forest photosynthesis after the 1991 Pinatubo eruption owing to the increase of diffuse radiation caused by volcanic aerosols. Flux site data indicated that canopy LUE was enhanced under diffuse sunlight in comparison with that under direct radiation [13,15,16]. Choudhury et al. [17] estimated an increase of 110% in crop LUE under diffuse radiation. Alton et al. [18] conducted a study for three forest sites (two broadleaf and one needleleaf) and found that the canopy LUE was enhanced by 6–33% under diffuse radiation. Alton [19] indicated that the enhancement of canopy LUE due to diffuse radiation varied with vegetation types, most significantly for tundra shrubs. Cai et al. [20] found that a single-leaf LUE model performed very well for a 56-year-old Douglas-fir stand when, instead of using total incident photosynthetically active radiation (PAR), they used the sum of incident diffuse PAR and a relatively small fixed fraction (22%) of incident direct PAR. Recently, Zhang et al. [21] reported that canopy LUE generally decreased with increasing sky clearness index, which is the ratio of solar radiation observed on the ground to radiation received at the top of the atmosphere, over 5 ChinaFLUX sites, including a temperate forest, a subtropical forest, a tropical rain forest, and two grassland sites. Therefore, the assumption that LUE is independent of the quality of radiation and GPP linearly increases with absorbed photosynthetic radiation would induce underestimation/overestimation of GPP in cloudy/clear skies [22,23].

Conceptually, a canopy is composed of clumps of sunlit and shaded leaves exposed to different levels of irradiance and showing variable LUE. Sunlit leaves receive both direct and diffuse radiation while shaded leaves mainly interact with diffuse beams. Under clear skies, solar irradiance is high and dominated by direct beams. Sunlit leaves are easily light saturated, and photosynthesis can even decrease with increasing radiation because of elevated temperature and enhanced photorespiration [13]. Consequently, the overall LUE of sunlit leaves is normally low [23]. In contrast, a large number of shaded leaves are only exposed to diffuse radiation, which is normally much lower than the radiation saturation point. Therefore, the photosynthesis of shaded leaves is typically light-limited. Under cloudy conditions, solar irradiance is dominated by diffuse sunlight, allowing shaded leaves to capture a large fraction of the solar irradiance. Even though the total incident radiation may be lower than that on clear days, the apparent improvement of the LUE for shaded leaves could lead to the enhanced LUE for the whole canopy [15,19].

More and more process models now calculate photosynthesis for sunlit and shade leaves separately [24,25,26]. However, this strategy has not been adopted by LUE models. To remedy this limitation, He et al. [23] recently developed a two-leaf LUE model on the basis of the MOD17 model. This new model considered differences in radiation absorption and in LUE of sunlit and shaded leaves. Validation at 6 ChinaFLUX sites demonstrated the improvement of the two-leaf LUE models over the MOD17 model in simulating GPP, especially at forest sites, with a R² value increasing about 0.1 and a root mean square error (RMSE) value decreasing about 0.64 g C m⁻² day⁻¹ on average.

In the two-leaf LUE model developed by He et al. [23], GPP of sunlit and shaded leaves increases linearly with APAR. However, many studies have shown a nonlinear increase of photosynthesis of sunlit leaves with increasing APAR because of light saturation of photosynthesis, especially at short temporal scales (minutes to hours) [15,27,28,29,30,31]. Recently, Wang et al. [6] developed a two-leaf temperature and vegetation type dependent rectangular hyperbolic model, which links quantum yield (α) and maximum photosynthetic rate (P_m) with the maximum carboxylation rate at 25 °C. The model is able to simulate GPP as accurately as a process-based model.

Previously, linear LUE models have been mostly used to calculate GPP at daily, 8-day, and even longer temporal scales [7,8,9,10,32,33]. Recently, this type of models is used to calculate GPP at short temporal scales. For example, Carbon Tracker, a system to optimize terrestrial carbon flux, uses the Carnegie-Ames Stanford Approach (CASA) biogeochemical model to calculate GPP every three hours as the prior carbon flux [34]. Such application of linear LUE models might induce biases in simulated GPP since observations have indicated the nonlinear response of canopy GPP to incoming PAR at short temporal scales [15,27,28,29,30,31]. Therefore, the assessment of applicability of different types of LUE models in simulating GPP at different temporal scales is of great significance to improving the simulation of GPP using remote sensing data.

In this study, the ability of nonlinear two-leaf LUE, linear two-leaf LUE, and MOD17 to simulate GPP at half-hourly, daily, and 8-day temporal scales were verified using GPP derived from net ecosystem productivity (NEP) measured at globally distributed 58 sites as benchmarks. The main goals of this study are: (1) to compare the performance of the MOD17, linear and nonlinear two-leaf LUE models at three different temporal scales (half-hourly, daily and 8-day); (2) to analyze the possible causes for the different performances of three LUE models. For simplicity, the MOD17, linear two-leaf LUE, and nonlinear two-leaf LUE models will be referred to hereinafter as MOD17, TL-LUE, and TL-LUEn, respectively.

2. Data and Methods

2.1. Data

In this study, we used meteorological data and ecosystem fluxes measured with the eddy covariance (EC) technique at 58 sites pertaining to the FLUXNET network and the processed MODIS leaf area index (LAI) product (MOD15A2) to simulate GPP at half-hourly, daily and 8-day temporal scales. The meteorological and flux data belongs to the LaThuile FLUXNET dataset and can be freely downloaded [35]. The sites were selected on the basis of the availability of key datasets, such as LAI, meteorology, and land surface C fluxes. GPP derived from tower measured NEP was used as benchmarks for model parameter optimization and model evaluation. All flux data were processed in the manner proposed within the Fluxnet project [36,37] as described by [38,39,40,41]. The 58 sites included 21 needle-leaf-forest (NF) sites, with 1 deciduous needle-leaf-forest (DNF) sites and 20 evergreen needle-leaf-forest (ENF) sites, 11 broadleaf-forest (BF) sites, with 2 evergreen broadleaf-forest (EBF) sites and 9 deciduous broadleaf-forest (DBF) sites, 4 mixed-forest (MF) sites, 7 crop (CROP) sites, 7 grassland (GRASS) sites, and 8 shrub (SHRUB) sites, located in Asia, Europe, and North America (Figure 1). The observations covered the period from January 2001 to December 2007 with at least two years of data for each site. In total, 143 site-years of data were used, of which 85 site-years of data were selected for parameter optimization (17 BF, 11 CROP, 10 GRASS, 6 MF, 29 NF, and 12 SHRUB site-years). The remaining 58 site-years of data were used for model evaluation, with one year of data for each site. Detailed information about each site is given in Table 1.

Figure 1. Distribution of all sites with data used for parameter optimization and validation in this study. The background is the MODIS global land cover product (MCD12C1) in 2003.

Table 1. Name, location, vegetation type, and period of data used for each site.

**Table 1.** Name, location, vegetation type, and period of data used for each site.
Site Name	Country	Lat. (°)	Long. (°)	Veg. Type	Opti. Years	Vali. Years	Reference
Austin Cary (ACA)	USA	29.74	−82.22	NF	2003	2005	Gholz and Clark (2002) [42]
ARM_SGP_Main (ASM)	USA	36.61	−97.49	CROP	2003	2004	Fischer et al. (2007) [43]
Audubon (AUD)	USA	31.59	−110.51	GRASS	2003	2004	Wilson and Meyers (2007) [44]
BC-DFir1949 (BD49)	Canada	49.87	−125.33	NF	2003	2004	Humphreys et al. (2006) [45]
Bartlett Experimental (BEP)	USA	44.06	−71.29	BF	2005	2006	Jenkins et al. (2007) [46]
BC-Harvest Dfir2000 (DF00)	Canada	49.87	−125.29	NF	2003	2004	Humphreys et al. (2006) [45]
BC-Harvest Dir1988 (DF88)	Canada	49.53	−124.9	NF	2004	2005	Humphreys et al. (2006) [45]
Bondville (BON)	USA	40.01	−88.29	CROP	2004,2005	2006	Wilson and Meyers (2007) [44]
Changbaishan (CBS)	China	42.40	128.10	MF	2003	2004	Zhang et al. (2006a,b) [47,48]
Dinghushan(DHS)	China	23.17	112.53	BF	2003	2004	Zhang et al. (2000) [49]
Donaldson (DON)	USA	29.75	−82.16	NF	2003	2004	Gholz and Clark (2002) [42]
El Saler (ES)	Spain	39.35	−0.32	NF	2001,2002	2003	Reichstein et al. (2006) [50]
Fort Peck (FPE)	USA	48.31	−105.1	GRASS	2003	2004	Wilson and Meyers (2007) [44]
Fyodorovskoye (FY)	Russia	56.46	32.92	NF	2001	2003	Milyukova et al. (2002) [51]
Goodwin Creek (GCR)	USA	34.25	−89.87	GRASS	2004,2005	2006	Wilson and Meyers (2007) [44]
Hainich (HA)	Germany	51.08	10.45	BF	2001,2002	2003	Mund et al. (2010) [52]
Harvard Forest (HAF)	USA	42.54	−72.17	BF	2005	2006	Urbanski et al. (2007) [53]
Haibei (HB)	China	37.67	101.33	GRASS	2003	2004	He et al. (2013) [23]
Hesse (HES)	France	48.67	7.07	BF	2001,2002	2003	Granier et al. (2002) [54]
Howland Forest (HOF)	USA	45.2	−68.74	MF	2003	2004	Hollinger et al. (1999, 2004) [55,56]
Hyytiala (HY)	Finland	61.85	24.29	NF	2001	2002	Kramer et al. (2002) [57]
Kendall (KED)	USA	31.74	−109.94	GRASS	2006	2007	Scott (2010) [58]
Kennedy (KEN)	USA	28.61	−80.67	SHRUB	2004	2005	Powell et al. (2006) [59]
Loobos (LOB)	Netherlands	52.17	5.74	NF	2001,2002	2003	Dolman et al. (2002) [60]
Mead Irrigated (MEI)	USA	41.17	−96.48	CROP	2003,2004	2005	Verma et al. (2005) [61]
Mead Rainfed (MER)	USA	41.18	−96.44	CROP	2004	2005	Verma et al. (2005) [61]
Metolius Intermediate (MIN)	USA	44.45	−121.56	NF	2005	2007	Law et al. (2003) [62] and Thomas et al. (2009) [63]
Mead Irrigated Rotation (MIR)	USA	41.16	−96.47	CROP	2004	2005	Verma et al. (2005) [61]
Mize (MIZ)	USA	29.76	−82.24	SHRUB	2003	2004	Brocha et al. (2012) [64]
Morgan Monroe State (MMS)	USA	39.32	−86.41	BF	2003,2005	2006	Schmid et al. (2000) [65]
Metolius New Young Pine (MNY)	USA	44.32	−121.6	NF	2004	2005	Ruehr et al. (2012) [66] and Vickers et al. (2012) [67]
Missouri Ozark (MOZ)	USA	38.74	−92.2	BF	2005,2006	2007	Gu et al. (2006) [68]
North Carolina Loblolly Pine (NCL)	USA	35.8	−76.67	NF	2005	2006	Noormets et al. (2009) [69]
Neustift (NEU)	Austria	47.12	11.32	GRASS	2002	2003	Wohlfahrt et al. (2008) [70]
Niwot Ridge (NR)	USA	40.03	−105.55	NF	2003,2006	2007	Monson et al. (2002) [71]
ON EpeatlandMerBleue (OEM)	Canada	45.41	−75.52	SHRUB	2001	2004	Lafleur et al. (2003) [72]
Puechabon (PUE)	France	43.74	3.6	BF	2001,2002	2003	Allard et al. (2008) [73]
QC-Black Spruce (QMB)	Canada	49.69	−74.34	NF	2004	2005	Bergeron et al. (2007) [74]
Qianyanzhou(QYZ)	China	26.73	115.07	NF	2003	2004	Yu et al. (2006) [75]
Renon (REN)	Italy	46.59	11.43	NF	2002	2003	Montagnani et al. (2009) [76]
Rosemount G19 (RG19)	USA	44.72	−93.09	CROP	2004,2005	2006	Griffis et al. (2008) [77]
Rosemount G21 (RG21)	USA	44.71	−93.09	CROP	2004,2005	2006	Bavin et al. (2009) [78]
Roccarespampani1 (ROC)	Italy	42.39	11.92	BF	2002	2003	Keenan et al. (2009) [79]
Sky Oaks New (SON)	USA	33.38	−116.64	SHRUB	2004,2005	2006	Luo et al. (2007) [80]
Soroe (SOR)	Denmark	55.48	11.65	MF	2001,2002	2003	Pilegaard et al. (2001) [81]
Santa Rita Mesquite (SRM)	USA	31.82	−110.87	SHRUB	2004,2005	2006	Scott (2010) [58]
San Rossore (SRO)	Italy	43.73	10.29	NF	2001,2002	2003	Migliavacca et al. (2011) [82]
Tharandt (THA)	Germany	50.96	13.57	NF	2001,2002	2003	Grünwald and Bernhofer (2007) [83]
Tomakomai (TMK)	Japan	42.74	141.52	NF	2001,2002	2003	Hirano et al. (2003) [84]
Tonzi Ranch (TRA)	USA	38.43	−120.97	SHRUB	2004,2005,2006	2007	Baldocchi et al. (2004) [85]
UCI 1850 (U50)	Canada	55.88	−98.48	NF	2003	2004	Goulden et al. (2011) [86]
UCI 1989 (U89)	Canada	55.92	−98.96	SHRUB	2003	2004	Goulden et al. (2011) [86]
UCI 1998 (U98)	Canada	56.64	−99.95	SHRUB	2003	2004	Goulden et al. (2011) [86]
UMBS (UMBS)	USA	45.56	−84.71	BF	2003,2004	2006	Curtis et al. (2005) [87]
Vaira Ranch (VRA)	USA	38.41	−120.95	GRASS	2003,2004	2007	Baldocchi et al. (2004) [85]
Vielsalm (VSA)	Belgium	50.31	6.00	MF	2001,2002	2003	Aubinet et al. (2001) [88]
Willow Creek (WCR)	USA	45.81	−90.08	BF	2003	2005	Bolstad et al. (2004) [89]
Wetzstein (WET)	Germany	50.45	11.46	NF	2002	2003	Rebmann et al. (2009) [90]

2.2. Methods

2.2.1. Models Used

TL-LUEn, TL-LUE, and MOD17 models were used in this study. The MOD17 algorithm is described in detail in Running et al. [8,91]. It relies on the assumption that GPP is linearly related to APAR [11,12,92]. The TL-LUE model stems from the MOD17 algorithm and discriminates the differences of upper and bottom canopy in receiving direct radiation and diffuse radiation and in their LUE. As a consequence, canopy GPP simulated by TL-LUE nonlinearly changes with incoming PAR. The TL-LUEn adopts the same methodology as the TL-LUE model to separate sunlit and shaded leaves and calculate their APAR. However, it takes the rectangular hyperbolic model to calculate GPP for sunlit and shaded leaves. The MOD17, TL-LUE, and TL-LUEn models are described in Equations (1–3), respectively, i.e.,

G P P = ε_{max} \times PAR \times f P A R \times f (V P D) \times g (T_{a min})

(1)

G P P = (ε_{m s u} \times A P A R_{m s u} \times L A I_{m s u} + ε_{m s h} \times A P A R_{m s h} \times L A I_{m s h}) \times f (V P D) \times g (T_{a min})

(2)

G P P = (\frac{ε_{m} \times A P A R_{m s u} \times β}{ε_{m} \times A P A R_{m s u} + β} \times L A I_{m s u} + \frac{ε_{m} \times A P A R_{m s h} \times β}{ε_{m} \times A P A R_{m s h} + β} \times L A I_{m s h}) \times f (V P D) \times g (T_{a min})

(3)

where ε_max is the maximum LUE in MOD17; fPAR is the fraction of PAR absorbed by vegetation and calculated from LAI using the Beer’s Law (fPAR=1-e^−k*LAI, where k is the light extinction coefficient and set as 0.5 as [23]); ε_msu and ε_msh are the maximum LUE of sunlit and shaded leaves in TL-LUE, respectively; ε_m is the quantum yield when incident PAR approaches zero and β is the maximum canopy photosynthetic flux density at light saturation in TL-LUEn [13]; f(VPD) and g(T_amin) denote the constrains imposed by atmospheric vapor pressure deficit (VPD) and minimum air temperature, respectively, and are used to downscale the maximum LUE values to real ones. APAR_msu and APAR_msh are the PAR absorbed by sunlit and shaded leaves per unit LAI; LAI_msu and LAI_msh are the leaf area index for sunlit and shaded leaves.

In above equations, the two attenuation scalars, f(VPD) and g(T_amin), range from 0 (total inhibition) to 1 (no inhibition) and are calculated using the same formulas for MOD17, TL-LUE, and TL-LUEn. Parameters VPD_max, VPD_min, T_{amin_min}, and T_{amin_}_max used to calculate f(VPD) and g(T_amin) depend on vegetation types [91] and are listed in Table 2.

In Equations (2) and (3), APAR_msu and APAR_msh are calculated as:

A P A R_{m s u} = (1 - α) \times [P A R_{d i r} \times \frac{cos (φ)}{cos (θ)} + \frac{P A R_{d i f} - P A R_{d i f, u}}{L A I} + C]

(4)

A P A R_{m s h} = (1 - α) \times [\frac{P A R_{d i f} - P A R_{d i f, u}}{L A I} + C]

(5)

where α is the albedo varying with vegetation types (Table 2), PAR_dif and PAR_dir are the diffuse and direct components of incoming PAR, respectively, and they are empirically calculated (see Equation (6)); PAR_dif,u is the diffuse PAR under the canopy and calculated following [24]; (PAR_dif – PAR_dif,u)/LAI denotes the absorbed diffuse PAR per unit leaf area within the canopy; C indicates multiple scattering of total PAR within the canopy [24]; φ is the mean leaf-sun angle and set as 60º[24]; θ is the mean solar zenith angles of half an hour, a day, and 8 days. The average solar zenith angle in each half an hour is calculated according to latitude, Julian day, and local time [93]. The average solar zenith angle in a given day is calculated according to latitude and Julian day [24]. The 8-day average solar zenith angle is the mean of the daily average solar zenith angles during the 8 days period.

Diffuse and direct PAR are empirically partitioned as [24]:

P A R_{d i f} = (0.943 + 0.734 R - 4.9 R^{2} + 1.796 R^{3} + 2.058 R^{4}) \times P A R

(6)

where PAR_dif is the estimated diffuse PAR; R is the sky clearness index (R=S/(S₀cosθ)), S and S₀ are the incoming solar radiation on the ground surface and solar constant (1367 Wm⁻²), respectively. In the conversion of incoming solar radiation into PAR, a constant of 0.5 is used [23] (PAR=0.5S).

LAI_msu and LAI_msh in Equations (2) and (3) are calculated as [24]:

L A I_{m s u} = 2 \times cos (θ) \times (1 - exp (- 0.5 \times Ω \times \frac{L A I}{cos (θ)}))

(7)

L A I_{m s h} = L A I - L A I_{m s u}

(8)

where Ω is the clumping index, which changes with land cover types, season and solar zenith angle. It was only assigned according to vegetation types here (Table 2) since spatially and temporally variant data are not available for this parameter.

Table 2. Model parameters used for different vegetation types.

**Table 2.** Model parameters used for different vegetation types.
Vegetation Type*	DBF	ENF	EBF	MF	GRASS	CROP	savannas	OS	WS
ε_max (g C/MJ)**	1.044	1.008	1.259	1.116	0.604	0.604	0.888	0.774	0.768
T_{amin_max} (°C)	7.94	8.31	9.09	8.5	12.02	12.02	8.61	8.8	11.39
T_{amin_min} (°C)	−8	−8	−8	−8	−8	−8	−8	−8	−8
VPD_max (kpa)	4.1	4.1	4.1	4.1	4.1	4.1	4.1	4.1	4.1
VPD_min (kpa)	0.93	0.93	0.93	0.93	0.93	0.93	0.93	0.93	9.3
Albedo	0.18	0.15	0.18	0.17	0.23^a	0.23^b	0.16	0.16	0.23
Clumping index (Ω^c)	0.8	0.6	0.8	0.7	0.9	0.9	0.8	0.8	0.8

*DBF: deciduous broadleaf forest; ENF: evergreen needleleaf forest; EBF: evergreen broadleaf forest; MF: mixed forest; GRASS: grassland; CROP: cropland; savannas: savannas; OS: open shrublands; WS: woody savannas; ^a Tang et al. [94]. ^b Grant et al. [95]. ^c Singarayer et al. [96]; ** Running et al. [91].

2.2.2. Parameter Optimization

The derived GPP in 85 calibration site-years was used to optimize parameters in MOD17, TL-LUEn, and TL-LUE models. The optimization was implemented using the Markov chain Monte Carlo (MCMC) method [97,98]. In the optimization, three models were driven using the same locally measured meteorological data and smoothed MODIS LAI. The MCMC simulation, as a stochastic simulation method, is based on Bayesian Theory, in which parameters are random variables instead of deterministic, but unknown constants in the classic thoughts. The fundamental formula of Bayesian Theory is:

π (θ | x) = \frac{p (x | θ) π (θ)}{\int_{- \infty}^{\infty} p (x | θ) π (θ) d θ}

(9)

where π(θ | x) is the posterior density of parameter

θ

(a term distribution under the condition of given sample x); π(θ) is the prior distribution of parameter

θ

(the knowledge possessed before measurement); and p(x | θ) is a likelihood function.

To determine the posterior density π(θ|x), the prior density and the likelihood function should be given in advance. We specified the prior density function as a uniform distribution over the following ranges:

\begin{array}{l} 0 < ε_{m} < 20 \\ 0 < β < 1500 \\ 0 < ε_{m s u}, ε_{m s h}, ε_{max} < 10 \end{array}

(10)

The lower and upper limits of ε_m (unit: g C MJ⁻¹) and

β

(unit: µg C m⁻² s⁻¹) were set according to the values compiled from 100 published datasets by Ruimy et al. [99]. The upper limits of ε_msu, ε_msh and ε_max (unit: g C MJ⁻¹) were assigned on the basis of previous findings [32,100]. In the optimization, these parameters were assumed uniformly distributed in the given limits with equal probability for all possible values.

The likelihood function was specified according to the distribution of simulation errors, which were assumed following a multivariate Gaussian distribution with a zero mean. This assumption is commonly made in many studies [101,102,103]. With this assumption, the likelihood function can be written as:

p (x | θ) = \prod_{i = 1}^{n} \frac{1}{\sqrt{2 π σ_{i}}} e^{- {(P_{i} - O_{i})}^{2} / 2 σ_{i}^{2}}

(11)

where O_i and P_i are the tower-derived GPP and simulated GPP, respectively;

σ_{i}

are the standard error of tower-derived GPP.

The sampling of parameters was implemented using the Metropolis-Hastings (M-H) algorithm. To find an effective proposal distribution, we first made a test run of the algorithm with 50,000 simulations. Based on the test run, a Gaussian distribution N(0, cov⁰(θ)) was constructed (cov⁰(θ) is a diagonal matrix with its diagonal elements equal to the estimated variances of parameters θ). Then, the following proposal distribution was adopted to execute the consecutive MCMC simulations formally for 30,000 times:

θ^{k} = θ^{k - 1} + N (0, {cov}^{0} (θ))

(12)

where θ^k is the new parameters generated from its predecessor θ^k−1.

The running means and standard deviations of parameter samples need time to approach stable. For bettering the statistical analysis of parameters, we discarded the initial 10,000 samples in the burn-in period and only used the remaining 20,000 samples for further analysis of each parameter. The histograms of the samples for each parameter indicate these parameters were well constrained in most situations because the posterior density functions were near the normal distribution (see Figure A1 in Appendix). Uncertainties of estimated parameters were quantified with a 95% highest-probability density interval. Means of parameter θ_i were estimated as followings and used for model validation:

E (θ_{i}) = \frac{1}{N} \sum_{k = 1}^{N} θ_{i}^{(k)}

(13)

where N is the number of samples in the M-H algorithm.

2.2.3. Parameter Sensitivity Analysis

Sensitivity of simulated GPP to parameters in three LUE models was analyzed using the factorial approach [104,105,106]. This method facilitates the statistically based representation of combinations of errors in several parameter sets. For a two-level complete factorial design, each of the model parameters is assigned upper and lower values based on specified perturbations of the magnitudes of the parameters, and the model is run using all combinations of parameter values. For n different parameters, this would require 2ⁿ simulation runs. Each parameter here was perturbed by an arbitrary magnitude ±10% [105].

The main effect of a parameter, which is also referred to the parameter sensitivity, is calculated as the average difference between a run in which the parameter is at its upper level (+10%) and a run in which the parameter at its lower level (−10%), but other parameters remain unchanged. For example, there are 4 simulation runs for TL-LUEn when considering the parameters ε_m and β. They are both ε_m and β at their lower levels (simulation #1), ε_m at its upper level and β at its lower level (simulation #2), ε_m at its lower level and β at its upper level (simulation #3), and both ε_m and β at their upper levels (simulation #4). The main effect of ε_m in TL-LUEn are the average of the difference between simulation #2 and simulation #1, and the difference between simulation #4 and simulation #3. A larger value of main effect indicates higher sensitivity of simulated GPP.

2.2.4. Model Performance Assessment

The performance of TL-LUEn, TL-LUE, and MOD17 was assessed using root mean square error (RMSE) and determination coefficient (R²). The paired t test was then conducted to evaluate the significance regarding the differences in R², RMSE between TL-LUEn and TL-LUE, TL-LUEn and MOD17, and TL-LUE and MOD17 when all vegetation types lumped together at three temporal scales for model evaluation, respectively [13].

3. Results

3.1. Optimized Model Parameters

Table 3 shows the averages of optimized ε_m, ε_max, ε_msu and ε_msh for 6 different vegetation types at half-hourly, daily and 8-day temporal scales, respectively. ε_m was generally larger than ε_msh, ε_msu, and ε_max. It increased sizably when the temporal scales increasing from half-hourly to 8-day, especially for CROP. CROP always had the highest ε_m in all the three temporal scales. At the half-hourly scale, three forest types, including BF, MF and NF, had lower ε_m values than CROP and SHRUB, but higher than GRASS. Through metadata analysis for more than 100 published datasets, Ruimy et al. [99] reported that CROP has the highest ε_m (about 5.17 g C MJ⁻¹) at the half-hourly scale, followed by forests (about 4.37 g C MJ⁻¹, mainly BF sites), and GRASS has the smallest one (about 2.71 g C MJ⁻¹), similar to the identified changes of ε_m with vegetation types here.

Optimized ε_max was in between ε_msh and ε_msu for all vegetation types and at all temporal scales. ε_msh is larger than ε_msu and ε_max due to the fact that shaded leaves are only exposed to diffuse radiation, which enters a canopy from all directions and distributes more evenly than direct radiation within the canopy [107,108]. The intensity of light absorbed by shaded leaves is normally lower than light saturation point. Thus, they have higher light use efficiency than sunlit leaves. The values of ε_max, ε_msu and ε_msh showed smaller variations than ε_m across three temporal scales (Table 3). As expected, CROP had the highest ε_max, ε_msu, and ε_msh values, which are 1.78, 1.21 and 5.23 g C MJ⁻¹ at the half-hourly scale, 1.80, 0.95 and 4.67 g C MJ⁻¹ at the daily temporal scale, and 1.80, 0.96 and 4.26 g C MJ⁻¹ at the 8-day scale, respectively. MF had the second largest ones, followed by BF and NF. GRASS had the lowest ε_max, ε_msu and ε_msh values among all 6 vegetation types. The ε_max, ε_msu and ε_msh of GRASS were lower than half of the corresponding values of CROP at the three temporal scales, respectively. In general, the average optimized ε_max was close to the default values used in the MOD17 algorithm (Table 2) for all vegetation types except CROP, which had much higher optimized ε_max than the default (0.604 g C MJ⁻¹). Many studies have indicated that the underestimation of CROP GPP by the MOD17 algorithm is mainly due to the low value of ε_max used [109]. It has been reported that the mean LUE of croplands can approach 2.80 g C MJ⁻¹ [110,111,112], slightly higher than the average value of about 2.0 g C MJ⁻¹ optimized in this study.

The parameter β in the TL-LUEn model showed complex changes with temporal scales and vegetation types. At the half-hourly scale, non-forest types had a relatively higher β value than forests, which is consistent with the findings reported by Ruimy et al. [99] and Wang et al. [6]. At the daily temporal scale, GRASS had the highest β (286.28 µg C m⁻² s⁻¹), followed by CROP, NF, MF, SHRUB and BF. At the 8-day temporal scale, the β values of BF, CROP, GRASS, MF, NF and SHRUB were 163.58, 214.64, 483.41, 267.39, 335.02, and 369.31 µg C m⁻² s⁻¹, respectively

Table 3. Average, standard deviation, variation of coefficient (CV), and uncertainties range of optimized ε_m, β, ε_msu, ε_msh and ε_max for 6 different vegetation types at half-hourly, daily and 8-day temporal scales (ε_m, β in TL-LUEn, ε_msu, ε_msh in TL-LUE, and ε_max in MOD17). The uncertainty was quantified with a 95% highest-probability density interval and averaged over each biome.

**Table 3.** Average, standard deviation, variation of coefficient (CV), and uncertainties range of optimized ε_m, β, ε_msu, ε_msh and ε_max for 6 different vegetation types at half-hourly, daily and 8-day temporal scales (ε_m, β in TL-LUEn, ε_msu, ε_msh in TL-LUE, and ε_max in MOD17). The uncertainty was quantified with a 95% highest-probability density interval and averaged over each biome.
	ε_m (g C MJ⁻¹)				β (µg C m⁻² s⁻¹)				ε_msu (g C MJ⁻¹)				ε_msh (g C MJ⁻¹)				ε_max (g C MJ⁻¹)
	Mean	STD	CV (%)	Uncertainty	Mean	STD	CV(%)	Uncertainty	Mean	STD	CV(%)	Uncertainty	Mean	STD	CV(%)	Uncertainty	Mean	STD	CV(%)	Uncertainty
Half-hourly
BF	3.52	1.72	48.93	±1.32	147.84	112.19	75.88	±77.50	0.58	0.15	25.20	±0.10	2.37	0.68	28.94	±0.39	0.88	0.24	27.39	±0.09
CROP	4.34	1.07	24.64	±1.46	470.48	235.03	49.96	±177.09	1.21	0.39	32.06	±0.16	5.23	1.90	36.34	±0.91	1.78	0.62	35.07	±0.16
GRASS	2.14	1.35	63.22	±1.13	273.55	333.25	121.83	±143.44	0.48	0.23	48.42	±0.16	1.69	1.06	62.74	±0.70	0.64	0.37	58.17	±0.14
MF	3.59	0.97	27.14	±1.27	214.63	83.46	38.89	±91.48	0.78	0.18	22.77	±0.13	3.33	0.83	24.91	±0.45	1.26	0.24	19.20	±0.11
NF	2.79	2.19	78.67	±0.92	308.14	272.11	88.31	±161.84	0.66	0.22	33.20	±0.16	2.35	0.79	33.57	±0.56	0.88	0.29	33.24	±0.11
SHRUB	2.41	3.48	144.64	±0.94	540.16	481.89	89.21	±242.72	0.53	0.17	32.29	±0.20	1.70	0.63	37.08	±0.95	0.65	0.24	36.44	±0.21
Daily
BF	4.39	3.10	70.56	±1.05	99.88	93.92	94.04	±16.16	0.47	0.16	34.76	±0.02	2.06	0.63	30.47	±0.06	0.95	0.29	30.23	±0.02
CROP	12.02	5.05	42.06	±2.85	189.06	79.53	42.06	±23.26	0.95	0.30	31.47	±0.02	4.67	1.55	33.27	±0.12	1.80	0.58	32.19	±0.03
GRASS	6.06	5.94	98.14	±3.46	286.28	399.60	139.58	±143.94	0.44	0.26	58.18	±0.05	1.56	0.98	62.91	±0.17	0.69	0.44	63.51	±0.04
MF	3.41	0.73	21.48	±0.32	147.65	53.43	36.19	±14.80	0.61	0.14	22.19	±0.02	2.97	0.65	21.98	±0.06	1.40	0.26	18.76	±0.02
NF	2.69	1.79	66.53	±0.59	152.09	77.61	51.03	±36.93	0.54	0.15	28.38	±0.05	2.21	0.74	33.31	±0.11	0.98	0.32	32.24	±0.02
SHRUB	5.60	3.86	68.87	±5.13	105.57	51.30	48.59	±25.56	0.44	0.15	33.76	±0.05	1.84	0.64	34.68	±0.26	0.75	0.21	28.66	±0.05
8-day
BF	4.64	2.78	59.92	±1.37	163.58	271.85	166.19	±84.65	0.53	0.18	34.42	±0.11	1.83	0.59	32.27	±0.19	0.97	0.30	31.04	±0.05
CROP	14.79	5.21	35.26	±2.28	214.64	197.90	92.20	±82.78	0.96	0.27	27.95	±0.09	4.26	1.59	37.44	±0.31	1.80	0.58	32.00	±0.09
GRASS	3.19	3.93	123.31	±1.20	483.41	489.70	101.30	±192.63	0.48	0.29	59.81	±0.14	1.33	0.79	58.88	±0.28	0.70	0.45	64.58	±0.10
MF	2.39	0.38	15.97	±0.71	267.39	172.78	64.62	±150.71	0.79	0.18	22.48	±0.23	2.51	0.63	24.95	±0.31	1.45	0.27	18.64	±0.07
NF	2.31	1.66	71.68	±0.79	335.02	341.62	101.97	±206.24	0.68	0.25	36.09	±0.17	1.81	0.63	34.79	±0.28	1.01	0.34	33.65	±0.07
SHRUB	2.08	1.61	77.58	±1.19	369.31	399.01	108.04	±242.47	0.47	0.14	30.39	±0.14	1.62	0.77	47.12	±0.33	0.74	0.22	29.68	±0.13

3.2. Model Performance in Calibration Site-Years

At the half-hourly scale, TL-LUEn showed slightly better performance than TL-LUE when data in all 85 calibration site-years were lumped together (Figure 2). With the increase of temporal scales from half an hour to 8 days, the difference of TL-LUEn and TL-LUE became almost indistinguishable. The improvement of both TL-LUE and TL-LUEn over MOD17 was obvious at all three temporal scales. At the half-hourly scale, the average RMSE of MOD17 were larger than that of TL-LUEn by 8.1 mg C m⁻² (30min)⁻¹, and the corresponding average R² was lower than that of TL-LUEn by 0.050 (Figure 2a,b). At the daily scale, MOD17 output an average RMSE higher by 0.3 g C m⁻² day⁻¹ and R² lower by 0.051 than TL-LUEn (Figure 2c,d). As to the 8-day scale, the average RMSE of MOD17 was 1.0 g C m⁻² 8days⁻¹ larger than that of TL-LUEn, and the corresponding average R² was 0.025 lower than that of TL-LUEn (Figure 2e,f).

Figure 2. The number of site-years within different root mean square error (RMSE) and R² classes (left) and the averages of RMSE and R² (right) of GPP simulated using the TL-LUEn, TL-LUE, and MOD17 models in 85 calibration site-years at half-hourly (a,b), daily (c,d), and 8-day (e,f) temporal scales, respectively.

At three different temporal scales, the number of site-years in the same RMSE and R² classes was similar for TL-LUEn and TL-LUE, confirming their similar ability to simulate GPP (see Figure 2). MOD17 performed poorer than TL-LUEn and TL-LUE in most site-years, indicated by larger RMSE and smaller R². For example, at the half-hourly temporal scale, the number of site-years with small RMSE values (below 60 mg C m⁻² (30min)⁻¹), was 29 for MOD17, 40 for TL-LUEn, and 41 for TL-LUE, respectively. The R² of GPP simulated by MOD17 was mostly in the range of 0.5–0.8 (in 58 site-years) while the R² of GPP simulated by the TL-LUEn and TL-LUE mostly ranged from 0.7 to 0.9 (58 site-years for TL-LUEn and 59 site-years for TL-LUE).

TL-LUEn performed better than TL-LUE for most vegetation types except CROP at the half-hourly and daily scale. However, it performed no better than TL-LUE for MF, NF and SHRUB at the 8-day scale (see Figure 3). Both TL-LUEn and TL-LUE outperformed MOD17 for most vegetation types at three temporal scales except for CROP at the half-hourly scale. The superiority of TL-LUEn and TL-LUE over MOD17 was most significant at forest sites.

Figure 3. Average RMSE and R² of GPP simulated using calibrated TL-LUEn, TL-LUE and MOD17 in the calibration site-years at half-hourly (the first and second columns), daily (the third and fourth columns), and 8-day (the last two columns) scales for individual vegetation types. Note: Broadleaf forest (BF); Mixed forest (MF); Needleleaf forest (NF); Crop (CROP); Grass (GRASS); Shrub (SHRUB). Solid black circles are the means and horizontal error bars denote standard deviations.

3.3. Model Performance in Evaluation Site–years

3.3.1. Model Performance at the Half-hourly Scale

Model evaluation shows that TL-LUEn performed slightly better than TL-LUE in simulating half-hourly GPP when data in all 58 validation site-years was lumped together (Figure 4a,b). The RMSE and R² of GPP simulated using TL-LUE against measurements averaged 64.3 mg C m⁻² (30min)⁻¹ and 0.732, respectively, while the corresponding values of TL-LUEn were 63.9 mg C m⁻² (30min)⁻¹ and 0.735, respectively. However, the differences in RMSE value between TL-LUEn and TL-LUE were not significant (p = 0.45) as well as the differences in R² between the two models (p = 0.27) (Table 4). The performance of MOD17 was the poorest, with average RMSE and R² values equaled to 70.1 mg C m⁻² (30min)⁻¹ and 0.690, respectively. In addition, the differences in both the two statistics (RMSE, R²) between TL-LUEn and MOD17, and TL-LUE and MOD17 were significant, with p values smaller than 0.0001 (Table 4).

Figure 4. The number of site-years within different RMSE and R² classes (left) and the averages of RMSE and R² (right) of gross primary productivity (GPP) simulated using the TL-LUEn, TL-LUE, and MOD17 models in 58 validation site-years at half-hourly (a,b), daily (c,d), and 8-day (e,f) temporal scales, respectively.

In 58 evaluation site-years, the RMSE of GPP simulated using TL-LUEn, TL-LUE, and MOD17 was larger than 80 mg C m⁻² (30min)⁻¹ at 10, 11 and 17 sites, respectively. The R² of GPP simulated using MOD17 mostly ranged from 0.5 to 0.8 (at 42 sites) while the R² of GPP simulated using TL-LUEn and TL-LUE was in the range from 0.7 to 0.9 at 39 and 40 sites, respectively (Figure 4a,b). TL-LUEn performed better than TL-LUE at 31 sites. The poorer performance of TL-LUEn relative to TL-LUE occurred at CROP, GRASS, SHRUB and NF sites. MOD17 only performed better than TL-LUE and TL-LUEn at 9 sites (Table A1 in the Appendix).

Table 4. Statistics of the Paired t Tests on the differences between TL-LUEn and TL-LUE, TL-LUEn and MOD17, and TL-LUE and MOD17 in RMSE and R² for model validation when all vegetation types lumped together at half-hourly, daily and 8-day scale.

**Table 4.** Statistics of the Paired t Tests on the differences between TL-LUEn and TL-LUE, TL-LUEn and MOD17, and TL-LUE and MOD17 in RMSE and R² for model validation when all vegetation types lumped together at half-hourly, daily and 8-day scale.
		RMSE				R²
		TL-LUEn − TL-LUE	TL-LUEn − MOD17	TL-LUE − MOD17		TL-LUEn − TL-LUE	TL-LUEn − MOD17	TL-LUE − MOD17
Half-hourly	t stat	−0.75	−4.33	−5.09	t stat	1.12	6.10	7.13
Half-hourly	p	0.45	0.00	0.00	p	0.27	0.00	0.00
Daily	t stat	0.33	−4.88	−7.63	t stat	0.53	7.61	9.30
Daily	p	0.75	0.00	0.00	p	0.60	0.00	0.00
8-day	t stat	2.24	−1.35	−5.96	t stat	0.98	4.70	0.98
8-day	p	0.03	0.18	0.00	p	0.33	0.00	0.33

Overall, TL-LUEn performed better than TL-LUE for BF, GRASS, MF and SHRUB, but poorer than TL-LUE for CROP and NF (see Figure 5). TL-LUEn and TL-LUE outperformed MOD17 for all vegetation types but CROP. The improvement of TL-LUE and TL-LUEn over MOD17 was most significant for forests (BF, MF and NF). Averaged over all forest sites, the RMSE of MOD17 was larger than those of TL-LUEn and TL-LUE by 10.1 mg C m⁻² (30min)⁻¹ and 8.8mg C m⁻² (30min)⁻¹, respectively. The corresponding average R² of MOD17 was 0.063 and 0.060 lower than those of TL-LUEn and TL-LUE, respectively.

3.3.2. Model Performance at the Daily Scale

TL-LUEn showed better performance than TL-LUE at the daily scale when 58 validation site-year data was lumped together (Figure 4c,d). The average RMSE of GPP simulated by TL-LUEn and TL-LUE was both 1.7 g C m⁻² day⁻¹. The average R² of GPP simulated by TL-LUEn was slightly larger than that of TL-LUE. Results of the paired t test on the differences in average RMSE value between TL-LUEn and TL-LUE were not significant (p = 0.75), as well as the differences in average R² value between the two models (p = 0.60) (Table 4). The average RMSE value of MOD17 was 1.9 g C m⁻² day⁻¹. The average R² value of MOD17 was smaller than the corresponding values of TL-LUEn and TL-LUE by 0.046 and by 0.045, respectively. In addition, the differences in average RMSE value and R² value between TL-LUEn and MOD17, and TL-LUE and MOD17 were significant, with p values were all smaller than 0.0001.

In 58 validation site-years, MOD17 produced larger RMSE and lower R² than TL-LUE and TL-LUEn at most sites. The RMSE of GPP simulated using MOD17, TL-LUE, and TL-LUEn was larger than 2.0 g C m⁻² day⁻¹ at 28, 15, and 15 sites, respectively. The values of R² above 0.9 occurred at only 3 sites for MOD17, at 13 sites for TL-LUE, at 16 sites for TL-LUEn, respectively (Figure 4c,d). TL-LUEn showed better ability to simulate GPP than TL-LUE at 29 sites. MOD17 only outperformed TL-LUEn and TL-LUE at 12 and 8 sites, respectively, mainly CROP, SHRUB and GRASS sites (Table A2 in the Appendix).

As to MF, NF and SHRUB, TL-LUEn performed with a higher RMSE and R² than TL-LUE. TL-LUEn only outperformed TL-LUE for BF, but performed poorer for CROP and GRASS (see Figure 5). Overall, TL-LUEn and TL-LUE outperformed MOD17 for forests and SHRUB. For three forest types, both TL-LUEn and TL-LUE outperformed MOD17. The average RMSE of both TL-LUEn and TL-LUE was 0.4 g C m⁻² day⁻¹ smaller than that of MOD17 while the average R² of TL-LUEn and TL-LUE was 0.063 and 0.057 higher than that of MOD17, respectively. As to CROP and GRASS, both average RMSE and R² of GPP simulated by TL-LUEn were higher than those of MOD17, while TL-LUE outperformed MOD17 with a smaller RMSE and a higher R² (see Figure 5).

Figure 5. Average RMSE and R² of GPP simulated using calibrated TL-LUEn, TL-LUE and MOD17 in the validation site-years at half-hourly (the first and second columns), daily (the third and fourth columns), and 8-day (the last two columns) scales for individual vegetation types. Note: Broadleaf forest (BF); Mixed forest (MF); Needleleaf forest (NF); Crop (CROP); Grass (GRASS); Shrub (SHRUB). Solid black circles are the means and horizontal error bars denote standard deviations.

3.3.3. Model Performance at the 8-day Scale

When data in all 58 validation site-years was lumped together, TL-LUEn performed similarly with TL-LUE at the 8-day scale (Figure 4e,f). The differences between the two models were significant (p < 0.05) in average RMSE value but was not significant (p = 0.33) in average R² value (Table 4). TL-LUEn outperformed MOD17 with significant differences (p < 0.05) in their average R² value but not significant differences in their RMSE value (p = 0.18), while TL-LUE outperformed MOD17 with significant differences (p < 0.0001) in their average RMSE value but not significant differences in their average R² value (p = 0.33) (Figure 4e,f, Table 4). However, the improvement of both TL-LUE and TL-LUEn over MOD17 was smaller in comparison with the improvement at half-hourly and daily temporal scales. MOD17 produced an average RMSE value of 13.0 g C m⁻² (8days)⁻¹ and an average R² value of 0.755. The average RMSE and R² of TL-LUE were 12.0 g C m⁻² (8days)⁻¹ and 0.775, respectively.

The RMSE of GPP simulated by TL-LUEn, TL-LUE and MOD17 were smaller than 12 g C m⁻² (8days)⁻¹ at 35, 34 and 27 sites. The three models had similar numbers of sites in each R² class. TL-LUEn performed poorer than TL-LUE at 32 sites, while MOD17 outperformed TL-LUEn and TL-LUE at 20 and 13 sites, respectively, which were mainly non-forest and NF sites (Table A3 in the Appendix).

TL-LUEn only performed better than TL-LUE for BF. As to non-forest types (CROP, GRASS and SHRUB), TL-LUEn performed similarly with MOD17. The average RMSE and R² of the former were larger than those of the latter by 1.0 g C m⁻² (8days)⁻¹ and 0.012, respectively. TL-LUE outperformed MOD17 in all the three non-forest types. As to forests, both TL-LUEn and TL-LUE showed a better performance than MOD17 with an average RMSE smaller than that of MOD17 by 1.5 g C m⁻² (8days)⁻¹ and 1.6 g C m⁻² (8days)⁻¹ and corresponding average R² larger than that of MOD17 by 0.030 and 0.022, respectively.

4. Discussion

4.1. The Ability of the Three LUE Models to Simulate GPP

At the half-hourly temporal scale, TL-LUEn and TL-LUE performed better than MOD17 for three types of forests (MF, BF, and NF), GRASS and SHRUB and their differences between these vegetation types are significant. With the increase of temporal scales, the improvement of TL-LUEn and TL-LUE over MOD17 gradually became less distinct. The changes in the ability of TL-LUEn, TL-LUE, and MOD17 to simulate GPP with vegetation types and temporal scales are, at least in part, related to the different structure of various vegetation types and the different response of canopy GPP to incident PAR described by three models. It was found that scaling up in time tended to linearize the relationship between CO₂ flux and PAR [99]. Observations also showed that changes in canopy GPP with incident PAR are nonlinear at the half-hourly scale and become approximately linear at the daily and 8-day scales [29]. GPP simulated by MOD17 always linearly increase with incident PAR while the increase of GPP with incident PAR is nonlinear in both TL-LUEn and TL-LUE. The non-linearity of TL-LUEn and TL-LUE can be modified through changing parameters ε_m, β, ε_msu, and ε_msh. For example, if ε_msu equals ε_msh in the TL-LUE model, the response of simulated canopy GPP to incident PAR would be close to linear. This is the reason why the improvement of TL-LUEn and TL-LUE over MOD17 is much smaller at the 8-day scale than at the half-hourly temporal scale.

The better performance of TL-LUEn and TL-LUE models over MOD17 changed with vegetation types, most significantly for forests, then for SHRUB, GRASS. Leuning et al. [113] pointed out that the canopy CO₂ exchange rates of crops is a quasi-linear function of absorbed PAR. In contrast, forests and sparse vegetation often show a markedly nonlinear response of canopy CO₂ exchange rates to absorbed PAR [99,113,114]. TL-LUEn and TL-LUE are able to capture both the nonlinear and linear responses of GPP to incident PAR while MOD17 is only able to describe the linear change of canopy GPP with incident PAR. Therefore, TL-LUEn and TL-LUE outperform MOD17 for forests, shrub and grass sites. The higher performance is the most significant for forests at the half-hourly scale.

The linear response of GPP to PAR in MOD17 led to the underestimation/overestimation of GPP under conditions of low/high incident PAR, which has been confirmed by Propastin et al. [22] and He et al. [23]. TL-LUEn and TL-LUE were, at least partially, able to correct this weakness. Figure 6, Figure 7 and Figure 8 show the RMSE of simulated GPP as a function of incident PAR at three different temporal scales. Under medium PAR conditions, TL-LUEn, TL-LUE, and MOD17 performed similarly. The improvement of TL-LUEn and TL-LUE over MOD17 mainly occurred under low or high incident PAR conditions.

Figure 6. The RMSE of modeled GPP against tower-derived GPP within different photosynthetically active radiation (PAR) classes for six different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the half-hourly scale.

Figure 7. The RMSE of modeled GPP against tower-derived GPP within different PAR classes for 6 different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the daily scale.

The ratio of diffuse to direct PAR changed with clearness index. Under conditions of low clearness index, canopy LUE is high [23] owing to more diffuse PAR being absorbed by shaded leaves with high LUE. MOD17 did not differentiate the different effects of diffuse and direct PAR on GPP and tended to underestimate/overestimate GPP under low/high clearness index conditions (Figure 9). In TL-LUEn and TL-LUE, incident PAR is first decomposed into diffuse and direct components according to clearness index. Under conditions of low clearness index, increased diffuse PAR will be mainly absorbed by shaded leaves, which have high LUE. Thus, GPP simulated by TL-LUEn and TL-LUE is higher than that simulated by MOD17 (Figure 9). In contrast, when clearness index is high, increased direct PAR will be mostly absorbed by sunlit leaves, which have low LUE. Consequently, GPP simulated by TL-LUEn and TL-LUE is lower than that simulated by MOD17. Therefore, the systematic biases of GPP simulated by MOD17 model under low and high clearness index can be alleviated by TL-LUE and TL-LUEn.

Figure 8. The RMSE of modeled GPP against tower-derived GPP within different PAR classes for 6 different vegetation types: (a) BF, (b) CROP, (c) GRASS, (d) MF, (e) NF and (f) SHRUB, at the 8-day scale.

4.2. The Applicability of Different Models

The different performances of three LUE models at different temporal scales and for different vegetation types suggest that it should be selective when using them. Prior to regional simulations of GPP using these models, we must be careful with the applicability of the optimized parameters.

In this study, optimized parameters changed significantly among different vegetation types. The across-site variability of these parameters is also sizeable even for a specific vegetation type (Table 3). The sensitivity of simulated GPP to ε_m and β in TL-LUEn, to ε_msu and ε_msh in TL-LUE, and to ε_max in MOD17 was assessed using the factorial approach described in section 2.2.3. The calculated main effect of ε_max, ε_m, β, ε_msu and ε_msh are 20%, 11.50%, 8.50%, 8.09% and 11.91% at the half-hourly scale, and 20%, 10.72%, 9.28%, 6.84% and 13.16% at the daily scale, and 20%, 11.26%, 8.74%, 7.52% and 12.48% at the 8-day scale, respectively (Table 5). The sensitivity of simulated GPP to ε_max in MOD17 is higher than the sensitivity of simulated GPP to individual parameters in TL-LUEn and TL-LUE. The sensitivity of simulated GPP to the simultaneous uncertainties of ε_m and β in TL-LUEn and to the simultaneous uncertainties of ε_msu and ε_msh in TL-LUE is the same as the sensitivity of simulated GPP to ε_max in MOD17. In addition, parameters in TL-LUE and MOD17 showed similar variations within each vegetation type, indicated by the similar CV in Table 3. However, as the two parameters in TL-LUEn vary not only with biomes but also with temperature [6], optimized ε_m and β exhibited larger variations and uncertainties than ε_max, ε_msu, and ε_msh for all vegetation types (Table 3).

Above analyses on the performance of three models at different temporal scales and their sensitivity to parameter uncertainties indicate that TL-LUEn is more applicable in individual site at the half-hourly scale. TL-LUE can be used regionally at the half-hourly, daily, and 8-day scales. MOD17 is also a good option for simulating regional GPP at the 8-day temporal scale and it is able to simulate GPP with accuracy close to TL-LUE.

Figure 9. The average differences of modeled daily GPPs with observations for different ranges of clearness index Q. ΔGPP means the difference between the simulated and tower-derived daily GPP for certain biome. (a–f) denoteΔGPP for 6 different vegetation types (BF, CROP, GRASS, MF, NF and SHRUB), respectively.

Table 5. Design of the 2² complete factorial sensitivity analyses for parameters of TL-LUEn, TL-LUE with all the vegetation types lumped together at half-hourly, daily, and 8-day scales.

**Table 5.** Design of the 2² complete factorial sensitivity analyses for parameters of TL-LUEn, TL-LUE with all the vegetation types lumped together at half-hourly, daily, and 8-day scales.
	Simulations	TL-LUEn			TL-LUE			MOD17
	Simulations	ε_m	β	ΔGPP_rel(%)	ε_msu	ε_msh	ΔGPP_rel(%)	ε_max	ΔGPP_rel(%)
Half-hourly	1	-	-	−10.00	-	-	−10.00	-	−10.00
	2	+	-	0.78	+	-	−1.91
	3	-	+	−2.23	-	+	1.91
	4	+	+	10.00	+	+	10.00	+	10.00
	Main effect(%)	11.50	8.50		8.09	11.91		20.00
Daily	1	-	-	−10.00	-	-	−10.00	-	−10.00
	2	+	-	0.72	+	-	−3.16
	3	-	+	−0.73	-	+	3.16
	4	+	+	10.00	+	+	10.00	+	10.00
	Main effect (%)	10.72	9.28		6.84	13.16		20.00
8-day	1	-	-	−10.00	-	-	−10.00	-	−10.00
	2	+	-	0.60	+	-	−2.48
	3	-	+	−1.92	-	+	2.48
	4	+	+	10.00	+	+	10.00	+	10.00
	Main effect (%)	11.26	8.74		7.52	12.48		20.00

Note: Columns three-four and six-seven show contrast coefficients for ε_m, β in TL-LUEn, and ε_msu, ε_msh in TL-LUE, respectively. A plus symbol indicates that the parameter was set at 110% of the estimate while a minus symbol indicates 90% of the estimate. ΔGPP_rel is the relative differences between the simulated GPP calculated by introducing a perturbation to a certain parameter and the simulated GPP calculated using optimized parameters.

4.3. Uncertainties and Remaining Issues

Both TL-LUEn and TL-LUE separate a canopy into sunlit and shaded leaves, and TL-LUEn further describe nonlinear response of their respective photosynthesis to APAR. These two models demonstrated powerful ability to simulate GPP. However, there are still some uncertainties remained. As indicated by Gebremichael and Barros [105], uncertainties in meteorological and LAI data, parameters, and model structure all might induce errors of simulated GPP. The change of linear response of GPP to VPD and PAR to nonlinear one and the inclusion of a soil moisture scalar might improve GPP simulation.

Similar to MOD17, TL-LUEn and TL-LUE only use VPD and minimum air temperature as environmental constraints on GPP. VPD represents the effect of atmospheric dryness on vegetation photosynthesis as a result of stomatal conductance. Soil moisture also plays an important role in regulating GPP via effects on leaf cell turgor pressure directly affecting photosynthesis or by stomatal conductance [10,115,116]. Because VPD and soil water availability did not co-vary, it would be most appropriate to have soil water availability as a constraint on photosynthesis in addition to VPD [117]. In MOD17, soil drought stress was approximated through the increase in the sensitivity of GPP to VPD [118]. Photosynthesis is considered to be totally shut off during periods of very high VPD, but in fact soil moisture and other environmental conditions might be still favorable to maintain photosynthetic activity at a certain level even if atmosphere is very dry [105]. Thus, the lack of soil water availability as a photosynthetic constraint in all three LUE models surely increases uncertainties in simulated GPP, especially for crops and grasses with shorter roots and high dependence on shallow soil moisture.

In this study, parameters in three LUE models are assumed invariant seasonally, which could induce some uncertainties in optimized parameters and calculated GPP. Many studies have shown that these parameters vary with both temperature and vegetation types [14,119,120]. Wang et al. [6] recently reported that with the consideration of seasonal changes of two parameters associated with temperature, the two-leaf nonlinear hyperbolic model (i.e., TL-LUEn) could simulate GPP as well as a process-based model. Chen et al. [121] indicated that the exclusion of seasonality of parameters in one-leaf and two-leaf LUE models is one of major drivers responsible for the failure of these models to capture the seasonality of GPP well. Thus, the proper representation of seasonal variations of these parameters needs further investigation.

Simulated GPP is also affected by the quality of meteorological and LAI inputs. In the calibration and validation periods, the models were driven by tower-measured meteorological data and processed MODIS LAI. The errors caused by inaccuracies of meteorological inputs are likely relative small [41]. However, the MODIS LAI contained considerable uncertainties, especially for crops [122], mainly caused by uncertainties in land cover and surface reflectance inputs and in the LAI inversion algorithm, and by the prevalence of persistent cloud cover [105]. The four variables in both TL-LUEn and TL-LUE (APAR_msh, APAR_msu, LAI_msh and LAI_msu), are all linked to LAI [23]. Our analysis showed that GPP simulated by TL-LUEn and TL-LUE is slightly more sensitive to LAI than that simulated by MOD17 (not shown here), which could be one of possible explainers for the poorer performance of TL-LUEn and TL-LUE relative to MOD17 at crop sites. Of course, this speculation is still worth of deep study.

The LUE of crops changes with species. At the BON, MIR, MER, RG19, and RG21 crop sites, the corn or soybeans were cultivated every other year. At the ASM site, the dominant species was wheat in 2003–2004 and 2006, while it was changed to corn in 2005. Corn is a C4 plant while soybeans and wheat are C3 plants. The LUE of corn is much higher than that of soybeans and wheat. The application of optimized parameters of C3/C4 plants for C4/C3 plants in the validation years might result in large uncertainties in simulated GPP. This is a possible cause for the poorer performance of models for crops in the validation period than in the calibration period. In addition, the uneven numbers of flux sites for different vegetation types could also result in uncertainties in the identified overall robustness of individual models.

5. Conclusions

In this study, the ability of three different types of LUE models (MOD17, TL-LUE and TL-LUEn) to simulate GPP at various temporal scales for different vegetation types was assessed using measurements at 58 flux sites in Asia, Europe and North America. The main conclusions that were drawn as follows:

(1): Optimized model parameters vary distinctly not only among different vegetation types, but also among different sites for the same vegetation type, especially for TL-LUEn. The parameters in TL-LUEn change sizably with temporal scales while the parameters in TL-LUE and MOD17 are almost invariant with temporal scales.
(2): The overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at 8-day scale.
(3): At the half-hourly temporal scale, TL-LUEn and TL-LUE outperformed MOD17 for all vegetation types but CROP. The outperformance of TL-LUEn and TL-LUE over MOD17 was more distinct for forests than for GRASS and SHRUB vegetation types. With the increase of temporal scales, the improvement of both TL-LUEn and TL-LUE over MOD17 decreased. At the daily temporal scale, both TL-LUEn and TL-LUE performed better than MOD17 for forests and SHRUB. TL-LUE also outperformed MOD17 slightly for other non-forest types (CROP and GRASS). TL-LUEn only performed better than TL-LUE for BF. At the 8-day temporal scale, TL-LUEn only outperformed MOD17 for forests while TL-LUE performed better than MOD17 for all vegetation types. TL-LUEn only slightly outperformed TL-LUE for BF.
(4): The improvement of TL-LUEn and TL-LUE over the MOD17 for forests was mainly achieved by the correction of the underestimation of GPP under low incident PAR and the overestimation of GPP under high incident PAR occurring in the MOD17.
(5): TL-LUEn is more applicable at individual sites at the half-hourly scale. TL-LUE could be regionally used at half-hourly, daily and 8-day scales, owing to its excellent performance and small parameter variations at different temporal scales and for most vegetation types. MOD17 is also an applicable option at 8-day scale.

Acknowledgements

This work was supported by Chinese Academy of Sciences for Strategic Priority Research Program (No. XDA05050602-1), National Basic Research Program of China (2010CB950702), National Natural Science Foundation of China (41371070), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We honestly thank all PIs of flux sites for providing the data for us. We appreciate the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Bekeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California - Berkeley, University of Virginia.

Author Contributions

Weimin Ju, Xiaocui Wu, Yanlian Zhou and Mingzhu He designed the research. Xiaocui Wu, Mingzhu He, and Yanlian Zhou processed data preparation, and ran the models. Xiaocui Wu and Weimin Ju mainly analyzed data and prepared the manuscript and figures. Beverly E. Law, T. Andrew Black, Hank A. Margolis, Alessandro Cescatti, Lianhong Gu, Leonardo Montagnani, Asko Noormets, Tim Griffis, Kim Pilegaard, Andrej Varlagin, Riccardo Valentini, Peter Blanken, Shaoqiang Wang, Huiming Wang, Shijie Han, Junhua Yan, Yingnian Li provided data, and reviewed and polished the manuscript. Bingbing Zhou and Yibo Liu helped to prepare the figures and polished the manuscript. All the authors contributed to the data analysis and paper writing and shared equally in the editing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Appendix

Figure A1. Histograms of the 20000 samples for each parameter generated by the Metropolis-Hasting Algorithm. Note: Only the best and worst cases are shown for each temporal scale owing to space limitation.

Table A1. Assessment of half-hourly GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.

**Table A1.** Assessment of half-hourly GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.
	ID	RMSE (mg C m ⁻² (30min)⁻¹)				R²
	ID	TL-LUEn	TL-LUE	MOD17		TL-LUEn	TL-LUE	MOD17
BF	BEP	63.381	66.958	76.423		0.819	0.783	0.689
	DHS	60.435	63.478	71.283		0.724	0.703	0.633
	HA	59.667	64.036	71.053		0.910	0.888	0.807
	HAF	71.021	72.580	85.082		0.862	0.853	0.801
	HES	76.049	85.382	99.336		0.807	0.774	0.671
	MMS	73.369	75.175	82.751		0.770	0.759	0.709
	MOZ	68.622	68.328	73.618		0.770	0.758	0.711
	PUE	49.406	49.861	54.236		0.806	0.801	0.770
	ROC	79.484	80.577	84.508		0.636	0.640	0.635
	UMBS	49.415	66.195	79.444		0.900	0.895	0.851
	WCR	56.459	59.721	75.943		0.899	0.887	0.822
	Average RMSE	64.301	68.390	77.607	Average R²	0.809	0.795	0.736
CROP	ASM	48.448	50.368	46.187		0.599	0.578	0.642
	BON	112.395	108.419	102.831		0.639	0.650	0.635
	MEI	142.000	139.843	134.365		0.678	0.687	0.711
	MER	161.440	158.377	158.585		0.688	0.700	0.728
	MIR	178.555	175.665	174.733		0.724	0.736	0.771
	RG19	70.821	69.495	72.853		0.737	0.744	0.709
	RG21	99.387	85.314	83.437		0.582	0.600	0.579
	Average RMSE	116.149	112.497	110.427	Average R²	0.664	0.671	0.682
GRASS	AUD	21.885	21.481	22.053		0.200	0.212	0.276
	FPE	34.699	34.544	35.134		0.541	0.531	0.496
	GCR	55.747	56.305	58.141		0.831	0.823	0.815
	HB	11.792	14.484	17.945		0.906	0.853	0.795
	KED	33.405	33.165	32.512		0.498	0.502	0.518
	NEU	80.230	81.569	93.599		0.821	0.821	0.767
	VRA	85.744	84.997	82.802		0.310	0.315	0.352
	Average RMSE	46.215	46.649	48.884	Average R²	0.587	0.580	0.574
MF	CBS	65.447	67.410	78.617		0.819	0.813	0.750
	HOF	55.397	58.873	70.669		0.881	0.862	0.776
	SOR	62.126	60.641	78.508		0.915	0.909	0.832
	VSA	56.961	55.160	62.589		0.861	0.862	0.819
	Average RMSE	59.983	60.521	72.596	Average R²	0.813	0.805	0.750
NF	ACA	77.015	76.300	75.549		0.413	0.413	0.402
	BD49	74.576	78.571	106.579		0.847	0.834	0.720
	DH00	56.199	63.847	63.684		0.605	0.602	0.561
	DH88	50.135	52.940	76.208		0.881	0.877	0.782
	DON	87.427	87.770	84.239		0.682	0.680	0.685
	ES	64.787	57.208	60.443		0.770	0.769	0.726
	FY	65.602	61.214	67.128		0.844	0.855	0.770
	HY	31.505	30.950	38.836		0.951	0.947	0.885
	LOB	75.394	73.753	80.112		0.782	0.781	0.739
	MIN	62.065	59.961	64.929		0.826	0.828	0.788
	MNY	42.416	45.296	44.548		0.738	0.721	0.663
	NCL	93.203	91.163	116.335		0.838	0.842	0.793
	NR	38.822	36.581	43.257		0.792	0.805	0.739
	QMB	26.751	25.044	29.041		0.633	0.673	0.597
	QYZ	78.560	78.520	80.415		0.756	0.758	0.747
	REN	77.531	79.238	79.692		0.663	0.657	0.617
	SRO	81.280	78.843	77.470		0.725	0.719	0.682
	THA	67.968	68.762	76.497		0.850	0.848	0.765
	TMK	59.161	64.370	105.229		0.935	0.919	0.799
	U50	34.898	30.674	36.441		0.633	0.710	0.606
	WET	70.679	72.746	75.443		0.862	0.862	0.803
	Average RMSE	62.665	62.560	70.575	Average R²	0.763	0.767	0.708
SHRUB	KEN	59.876	58.965	62.697		0.811	0.811	0.789
	MIZ	73.406	73.674	73.614		0.840	0.844	0.831
	OEM	16.690	19.447	23.973		0.818	0.785	0.713
	SON	28.248	29.451	25.293		0.381	0.400	0.391
	SRM	30.431	29.824	30.056		0.450	0.457	0.462
	TRA	55.896	52.920	51.908		0.639	0.663	0.680
	U89	23.456	21.613	27.365		0.779	0.781	0.679
	U98	20.229	23.686	27.612		0.746	0.648	0.543
	Average RMSE	38.529	38.698	40.315	Average R²	0.683	0.674	0.636

Table A2. Assessment of daily GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.

**Table A2.** Assessment of daily GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.
	ID	RMSE (g C m ⁻² day ⁻¹)				R²
	ID	TL-LUEn	TL-LUE	MOD17		TL-LUEn	TL-LUE	MOD17
BF	BEP	1.291	1.401	1.818		0.914	0.883	0.794
	DHS	1.328	1.418	1.695		0.379	0.362	0.321
	HA	1.590	1.646	2.165		0.929	0.909	0.829
	HAF	1.495	1.650	2.155		0.940	0.923	0.851
	HES	1.522	1.905	2.503		0.914	0.880	0.781
	MMS	1.810	1.792	1.895		0.849	0.854	0.829
	MOZ	1.824	1.782	2.055		0.780	0.770	0.706
	PUE	1.477	1.624	1.888		0.549	0.524	0.450
	ROC	2.006	2.045	2.063		0.689	0.670	0.682
	UMBS	1.318	2.147	2.330		0.924	0.944	0.917
	WCR	1.343	1.402	1.848		0.949	0.941	0.891
	Average RMSE	1.546	1.710	2.038	Average R²	0.801	0.787	0.732
CROP	ASM	1.095	1.061	1.165		0.608	0.630	0.595
	BON	3.166	2.821	2.793		0.727	0.737	0.696
	MEI	3.054	2.911	3.076		0.852	0.867	0.845
	MER	4.742	4.396	4.350		0.855	0.879	0.863
	MIR	5.375	5.075	4.996		0.835	0.876	0.875
	RG19	2.223	2.202	2.345		0.741	0.740	0.710
	RG21	2.476	2.480	2.570		0.691	0.691	0.664
	Average RMSE	3.162	2.992	3.042	Average R²	0.758	0.774	0.750
GRASS	AUD	0.609	0.611	0.618		0.412	0.435	0.427
	FPE	1.147	1.093	1.091		0.493	0.507	0.511
	GCR	2.312	1.493	1.608		0.803	0.806	0.790
	HB	0.328	0.349	0.357		0.912	0.917	0.913
	KED	0.949	0.948	0.958		0.574	0.578	0.560
	NEU	2.785	2.755	2.860		0.740	0.746	0.733
	VRA	2.907	2.938	2.991		0.025	0.020	0.013
	Average RMSE	1.577	1.455	1.498	Average R²	0.566	0.573	0.564
MF	CBS	1.550	1.603	1.868		0.899	0.893	0.842
	HOF	1.039	1.123	1.661		0.932	0.922	0.840
	SOR	1.386	1.361	2.024		0.951	0.948	0.887
	VSA	1.606	1.542	1.966		0.853	0.862	0.811
	Average RMSE	1.395	1.407	1.880	Average R²	0.909	0.906	0.845
NF	ACA	1.417	1.406	1.473		0.338	0.337	0.319
	BD49	1.567	1.714	2.756		0.922	0.908	0.755
	DH00	1.495	1.483	1.619		0.805	0.809	0.746
	DH88	1.357	1.518	2.270		0.908	0.912	0.780
	DON	2.057	2.032	2.073		0.287	0.279	0.318
	ES	1.358	1.416	1.712		0.440	0.432	0.397
	FY	1.747	1.703	2.062		0.877	0.874	0.795
	HY	0.851	0.849	1.111		0.955	0.955	0.915
	LOB	1.849	1.824	2.175		0.876	0.878	0.839
	MIN	2.284	2.294	2.407		0.726	0.726	0.716
	MNY	1.191	1.096	1.155		0.800	0.796	0.742
	NCL	1.826	1.917	2.612		0.862	0.858	0.801
	NR	1.030	1.053	1.194		0.796	0.799	0.751
	QMB	0.618	0.638	0.727		0.789	0.779	0.726
	QYZ	1.217	1.260	1.530		0.903	0.909	0.863
	REN	1.906	1.871	2.016		0.779	0.778	0.751
	SRO	2.68	2.213	2.425		0.424	0.437	0.416
	THA	1.878	1.864	2.305		0.847	0.847	0.752
	TMK	1.368	1.422	2.266		0.965	0.962	0.884
	U50	0.771	0.817	0.965		0.819	0.808	0.738
	WET	2.288	2.276	2.645		0.818	0.818	0.773
	Average RMSE	1.560	1.556	1.881	Average R²	0.759	0.757	0.704
SHRUB	KEN	1.070	1.073	1.398		0.641	0.641	0.559
	MIZ	2.377	2.345	2.536		0.401	0.410	0.451
	OEM	0.590	0.459	0.520		0.866	0.899	0.847
	SON	0.896	0.746	0.696		0.383	0.340	0.348
	SRM	0.730	0.730	0.748		0.661	0.657	0.628
	TRA	1.252	1.211	1.325		0.730	0.716	0.658
	U89	0.466	0.559	0.727		0.909	0.889	0.810
	U98	0.578	0.662	0.777		0.804	0.755	0.665
	Average RMSE	0.995	0.973	1.091	Average R²	0.674	0.663	0.621

Table A3. Assessment of 8-day GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.

**Table A3.** Assessment of 8-day GPP simulated using TL-LUEn, TL-LUE and MOD17 for the 58 evaluation site-years.
Vegetation Type	ID	RMSE(g C m ⁻² (8days) ⁻¹)				R²
Vegetation Type	ID	TL-LUEn	TL-LUE	MOD17		TL-LUEn	TL-LUE	MOD17
BF	BEP	8.797	9.867	11.087		0.940	0.908	0.875
	DHS	8.040	8.081	9.379		0.489	0.483	0.454
	HA	11.839	12.680	15.534		0.948	0.919	0.893
	HAF	10.318	11.533	13.496		0.952	0.939	0.905
	HES	11.323	13.312	17.844		0.940	0.913	0.858
	MMS	12.139	13.032	12.506		0.878	0.862	0.873
	MOZ	10.735	12.913	13.328		0.859	0.799	0.788
	PUE	10.386	11.343	13.264		0.564	0.518	0.443
	ROC	14.711	14.755	14.760		0.731	0.727	0.738
	UMBS	14.479	16.448	16.701		0.966	0.956	0.960
	WCR	9.311	10.438	9.748		0.961	0.952	0.956
	Average RMSE	11.098	12.218	13.422	Average R²	0.839	0.816	0.795
CROP	ASM	9.940	7.639	8.004		0.646	0.668	0.658
	BON	22.047	21.025	20.760		0.760	0.753	0.722
	MEI	21.868	22.272	23.100		0.883	0.878	0.868
	MER	36.640	34.434	33.774		0.889	0.888	0.883
	MIR	41.373	39.191	38.325		0.870	0.886	0.897
	RG19	14.858	15.987	16.825		0.797	0.776	0.738
	RG21	18.381	18.880	19.199		0.718	0.708	0.694
	Average RMSE	23.587	22.775	22.855	Average R²	0.795	0.794	0.780
GRASS	AUD	4.462	4.377	4.424		0.505	0.531	0.520
	FPE	8.178	7.494	7.389		0.565	0.601	0.614
	GCR	12.983	10.570	11.432		0.841	0.84	0.832
	HB	1.467	1.825	1.632		0.977	0.963	0.973
	KED	7.285	6.912	6.773		0.620	0.670	0.656
	NEU	20.615	20.126	20.91		0.776	0.779	0.769
	VRA	22.565	22.917	23.431		0.032	0.020	0.010
	Average RMSE	11.079	10.603	10.856	Average R²	0.617	0.629	0.625
MF	CBS	9.285	10.630	11.117		0.944	0.936	0.918
	HOF	6.798	6.388	7.703		0.96	0.955	0.935
	SOR	11.469	9.027	12.070		0.959	0.960	0.938
	VSA	10.844	9.517	12.613		0.929	0.933	0.906
	Average RMSE	9.599	8.891	10.876	Average R²	0.948	0.946	0.924
NF	ACA	9.207	9.107	9.201		0.349	0.382	0.380
	BD49	10.080	10.342	13.791		0.943	0.952	0.904
	DH00	10.258	10.962	10.835		0.846	0.850	0.841
	DH88	9.043	10.307	14.822		0.943	0.949	0.880
	DON	14.970	14.370	14.779		0.217	0.213	0.215
	ES	11.749	10.039	12.075		0.490	0.467	0.437
	FY	13.414	13.072	14.767		0.889	0.888	0.829
	HY	5.923	5.976	6.865		0.971	0.964	0.951
	LOB	15.885	13.218	14.689		0.943	0.940	0.926
	MIN	17.790	17.128	17.869		0.832	0.830	0.829
	MNY	7.965	7.875	8.036		0.870	0.857	0.808
	NCL	10.917	13.249	17.512		0.872	0.857	0.808
	NR	6.918	7.253	7.520		0.853	0.851	0.852
	QMB	8.092	7.034	7.159		0.589	0.586	0.577
	QYZ	8.782	8.131	9.146		0.951	0.953	0.939
	REN	14.123	13.660	13.975		0.862	0.863	0.862
	SRO	20.316	15.779	18.341		0.466	0.478	0.433
	THA	14.245	13.288	14.906		0.899	0.896	0.872
	TMK	9.117	9.443	10.819		0.968	0.972	0.963
	U50	5.158	4.878	5.818		0.895	0.887	0.854
	WET	20.463	18.842	21.864		0.878	0.882	0.862
	Average RMSE	11.639	11.141	12.609	Average R²	0.787	0.787	0.763
SHRUB	KEN	5.735	6.356	8.740		0.701	0.660	0.535
	MIZ	18.357	16.033	19.194		0.454	0.475	0.435
	OEM	8.708	3.493	3.302		0.933	0.938	0.912
	SON	13.356	5.074	4.735		0.403	0.407	0.455
	SRM	5.992	5.427	5.358		0.737	0.734	0.721
	TRA	14.741	8.007	8.603		0.758	0.797	0.758
	U89	4.485	4.168	4.582		0.868	0.909	0.895
	U98	5.674	4.391	4.793		0.867	0.813	0.782
	Average RMSE	9.631	6.619	7.413	Average R²	0.715	0.717	0.687

© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

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Wu, X.; Ju, W.; Zhou, Y.; He, M.; Law, B.E.; Black, T.A.; Margolis, H.A.; Cescatti, A.; Gu, L.; Montagnani, L.; et al. Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales. Remote Sens. 2015, 7, 2238-2278. https://doi.org/10.3390/rs70302238

AMA Style

Wu X, Ju W, Zhou Y, He M, Law BE, Black TA, Margolis HA, Cescatti A, Gu L, Montagnani L, et al. Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales. Remote Sensing. 2015; 7(3):2238-2278. https://doi.org/10.3390/rs70302238

Chicago/Turabian Style

Wu, Xiaocui, Weimin Ju, Yanlian Zhou, Mingzhu He, Beverly E. Law, T. Andrew Black, Hank A. Margolis, Alessandro Cescatti, Lianhong Gu, Leonardo Montagnani, and et al. 2015. "Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales" Remote Sensing 7, no. 3: 2238-2278. https://doi.org/10.3390/rs70302238

APA Style

Wu, X., Ju, W., Zhou, Y., He, M., Law, B. E., Black, T. A., Margolis, H. A., Cescatti, A., Gu, L., Montagnani, L., Noormets, A., Griffis, T. J., Pilegaard, K., Varlagin, A., Valentini, R., Blanken, P. D., Wang, S., Wang, H., Han, S., ... Liu, Y. (2015). Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales. Remote Sensing, 7(3), 2238-2278. https://doi.org/10.3390/rs70302238

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