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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (

To estimate global gross primary production (GPP), which is an important parameter for studies of vegetation productivity and the carbon cycle, satellite data are useful. In 2014, the Japan Aerospace Exploration Agency (JAXA) plans to launch the Global Change Observation Mission-Climate (GCOM-C) satellite carrying the second-generation global imager (SGLI). The data obtained will be used to estimate global GPP. The rate of photosynthesis depends on photosynthesis reduction and photosynthetic capacity, which is the maximum photosynthetic velocity at light saturation under adequate environmental conditions. Photosynthesis reduction is influenced by weather conditions, and photosynthetic capacity is influenced by chlorophyll and RuBisCo content. To develop the GPP estimation algorithm, we focus on photosynthetic capacity because chlorophyll content can be detected by optical sensors. We hypothesized that the maximum rate of low-stress GPP (called “GPP capacity”) is mainly dependent on the chlorophyll content that can be detected by a vegetation index (VI). The objective of this study was to select an appropriate VI with which to estimate global GPP capacity with the GCOM-C/SGLI. We analyzed reflectance data to select the VI that has the best linear correlation with chlorophyll content at the leaf scale and with GPP capacity at canopy and satellite scales. At the satellite scale, flux data of seven dominant plant functional types and reflectance data obtained by the Moderate-resolution Imaging Spectroradiometer (MODIS) were used because SGLI data were not available. The results indicated that the green chlorophyll index, CI_{green}(ρ_{NIR}/ρ_{green}-1), had a strong linear correlation with chlorophyll content at the leaf scale (R^{2} = 0.87, p < 0.001) and with GPP capacity at the canopy (R^{2} = 0.78, p < 0.001) and satellite scales (R^{2} = 0.72, p < 0.01). Therefore, CI_{green} is a robust and suitable vegetation index for estimating global GPP capacity.

Terrestrial ecosystems are major sinks in the global carbon cycle, sequestering carbon and slowing the increase in CO_{2} concentration in the atmosphere [

Many studies have estimated GPP based on the concept of the light-use efficiency (LUE) model [_{max}), the reduction of LUE_{max} due to environmental stress [_{max}, the stress factor, and leaf area index (LAI) estimations are crucial to the LUE model. The LUE concept has been applied to diagnostic models for estimating GPP, such as the Biosphere Model Integrating Ecophysiological and Mechanistic Approaches Using Satellite Data (BEAMS) [_{max} does not vary spatially and temporally. However, process-based models are more complex and require many parameters, whereas LUE models have the advantage of simplicity.

GPP is affected by seasonal changes in the maximum velocity of carboxylation (Vcmax) [_{cmax} is estimated using satellite-derived LAI. Therefore, it is worthwhile to directly extract the ecophysiological and physiochemical properties of vegetation from satellite data. To accomplish this, many vegetation indices (VIs) have been developed, such as those related to the chlorophyll content of a leaf or the canopy. One VI uses NIR and green reflectance to estimate the chlorophyll content of a rice canopy [_{531}−R_{570}]/[R_{531}+R_{570}]) [_{830}/R_{550}) [_{880}/R_{red-edge or green} − 1 [_{720}/D_{700} (where D_{x} is the first derivative of reflectance at wavelength x [_{red-edge}, CCI, PRI, GRI, TCARO/OSAVI, and MCARI/OSAVI are calculated from hyperspectral satellite sensor data such as those obtained by the Project for On-Board Autonomy (PROBA) satellite carrying a Compact High Resolution Imaging Spectrometer (CHRIS) and by the Earth-Observing 1 (EO-1) satellite carrying the Hyperion imaging spectrometer. These indices cannot be calculated from data retrieved by multispectral satellite sensors such as SGLI and MODIS.

Gitelson

Another global GPP estimation model that uses light-response curves has also been applied to VIs to estimate maximum photosynthesis under light saturation (P_{max}) [_{max} as possible and to facilitate scaling and extrapolations across regional and global resolutions. The model, however, did not include weather conditions and used only a single shape of the light-response curve representing temperate vegetation in Japan. Ide _{max} and the initial slope from seasonal and short-term variations. The seasonal variations in P_{max} and initial slope were correlated with the ratio VI (RVI) or enhanced VI (EVI). These two research approaches estimated GPP using the light-response curve to examine the relationship between GPP and the VI. However, for global GPP estimation, the relationship between P_{max} and VI should be determined more widely in other main biomes.

To estimate global GPP, photosynthesis of a single leaf is key. The leaf photosynthetic rate depends on photosynthetic capacity and photosynthesis reduction. Photosynthetic capacity is influenced by chlorophyll and RuBisCo (ribulose-1, 5-bisphosphate carboxylase/oxygenase) content [

At the canopy scale, photosynthetic capacity is the integration of single-leaf chlorophyll content and the total leaf area [

We defined the GPP under low-stress conditions as GPP capacity. The light-response curve under low-stress conditions using a rectangular hyperbolic function is described as:
_{capacity} (mg·CO_{2}·m^{−2}·s^{−1}) is the low-stress GPP, P_{max_capacity} (mg·CO_{2}·m^{−2}·s^{−1}) is the maximum GPP_{capacity} under light saturation, α_{slope} is a photosynthetic quantum efficiency representing the initial slope of the light-response curve, and PAR (μmol·m^{−2}·s^{−1}) is photosynthetically active radiation (

To apply satellite data for estimating GPP_{capacity} using _{slope} and P_{max_capacity} should be determined. Leaf physiological research has revealed that the initial slope of the light-response curve depends on the efficiency of light conversion into fixed carbon [_{max} both exhibit a linear relationship with the same VI [_{max} are linearly correlated. On the other hand, other studies have reported that P_{max} is related to the amount of chloroplasts [_{slope} is a constant for each plant functional type. In addition, we assume that P_{max_capacity} is related to the amount of chlorophyll. From this perspective, we examined the method and VI for estimating P_{max_capacity} from satellite data.

First, we examined spectral reflectance to select potential candidate VIs that show linear correlations with chlorophyll content from a set of VIs. Second, we studied the relationship between candidate VIs and P_{max_capacity} of canopy light-response curves under low-stress conditions. Finally, the selected VI was validated.

At leaf and canopy scales, we resampled spectral reflectance for the following spectral bands of GCOM-C/SGLI: blue (_{blue}; 438–448 nm), green (_{green}; 520–540 nm), red (_{red}; 663.5–683.5 nm), and NIR (_{N}_{IR}

Gitelson _{green}) and 675 nm (ρ_{red}). For low chlorophyll concentrations, the reflectance sensitivity is higher at the maximum absorption located around 675 nm (ρ_{red}). At medium to high concentrations, reflectance sensitivity is higher at 550 nm (ρ_{green}). Thus, we selected several VIs that may relate to chlorophyll content. Moreover, it better to select the VI which has the best linear relationship with chlorophyll content to estimate GPP at the global scale. Because the uncertainty involved in aggregating remote sensing data from smaller to larger spatial scales (up-scaling) is related both to non-linearity in the response function and to heterogeneity within a site. When the response is non-linear, conventional averaging of reflected radiation gives a biased estimate of photosynthesis [

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_{green})_{green} can estimate canopy chlorophyll content under a wide range of canopy conditions.

To investigate relationships between VIs and photosynthesis capacity, data at three scales were used: leaf, canopy, and satellite scales.

Data sets of the chlorophyll content (μg·cm^{−2}) of 19 sampled leaves (minimum chlorophyll content of 0.88 μg·cm^{−2}, maximum of 50.76 μg·cm^{−2}, average of 16.08 μg·cm^{−2}, standard deviation of 14.56 μg·cm^{−2}) were used, and reflectance data with a spectral range of 350 to 2,500 nm, were obtained from Furumi

Eddy covariance (EC) flux data and canopy spectral reflectance of broadleaf deciduous trees at a site in Takayama, Japan, were used. The EC flux data for 2003 and 2004 were used to calculate P_{max_capacity} from the light-response curve. These data were downloaded from AsiaFlux (_{2}·m^{−2}·s^{−1}), friction velocity (U*; m·s^{−1}), photosynthetic photon flux density (PAR; mol·m^{−2}·s^{−1}), net radiation (Rn; W·m^{−2}), air temperature (T_{air}; °C), relative humidity (Rh; %), soil temperature (T_{soil}; °C), vapor pressure deficit (VPD; kPa), and precipitation (PPT; mm). Precipitation data were provided by the Institute for Basin Ecosystem Studies, Gifu University, Japan. Respiration, NEE, and GPP data were provided by the National Institute for Environmental Studies (NIES). The data had a time interval of 30 min.

For canopy reflectance, data measured by a hemispherical spectroradiometer (HSSR; MS-700, EKO Instruments Co., Ltd.) in 2004 were used. The HSSR had a spectral range of 350 to 1,050 nm and spectral interval of 3.3 nm. The data were downloaded from the PEN website (

Seven EC flux tower sites were selected from Bonan, 1996 [

All of the EC flux data were used to calculate P_{max_capacity} using the light-response curve. The EC flux datasets were downloaded from the FluxNet project (

All data for the year 2003 were used. To examine relationships between VIs and P_{max_capacity}, 8-day composited level-3 global reflectance data from TERRA/MODIS (MOD09A1.005) with 500-m resolution were used. The datasets were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC;

Data processing included two main parts: the reflectance data process for calculating the VI and the photosynthetic capacity calculation processes. At the leaf scale, we analyzed the relationship between SGLI band reflectance and chlorophyll content. At the canopy scale, we analyzed the relationship between SGLI band reflectance and P_{max_capacity}. At the satellite scale, we analyzed the relationship between MOD09A1 and P_{max_capacity}. P_{max_capacity} was calculated from EC flux data by fitting the light-response curve.

At the leaf and canopy scales, the reflectance was averaged over the wavelength interval of the SGLI sensor (

To identify criteria for selecting low-stress conditions, we investigated the diurnal change in net ecosystem production (NEP) by averaging half-hourly NEP over a 16-day period. We used a 16-day period to avoid strong confounding seasonal effects, to have a period long enough to provide sufficient data [

To draw the light-response curve, GPP_{capacity} data were plotted against PAR. GPP data are typically provided by the FluxNet project. When GPP data were not provided by a flux tower, we calculated GPP as a sum of NEP and ecosystem respiration (Rec). Rec was estimated using nighttime NEP as a function of air temperature. _{max_capacity} and α_{slope} were calculated from the light-response curve by fitting half-hourly GPP_{capacity} data (not averaged) for each 16-day period to PAR. First, GPP_{capacity} values were fitted to the light-response curve (_{max_capacity} and α_{slope} were determined. GPP_{capacity} values were fitted again as a function of PAR to determine P_{max_capacity} using α_{slope} averaged from each 16-day photosynthesis period over the growing season. The photosynthesis periods used were those for which NEP in daytime was greater than zero.

To test the potential of the selected VI to estimate P_{max_capacity} in a different year, we used the empirical relationship between the selected VI and P_{max_capacity} of year 2003. The selected VI was calculated from MOD09A1 for the CA-Let, JPTMK, JP-Mase, and TH-SKR sites in 2002 and the JP-TKY and JP-FJY sites in 2004. We then compared estimated P_{max_capacity} and observed P_{max_capacity}.

The relationship between the reflectance at each SGLI spectral band and chlorophyll content is illustrated in _{blue} was stable at all chlorophyll contents, and ρ_{NIR} was not sensitive to the chlorophyll content. ρ_{red} and ρ_{green} exhibited negative relationship with chlorophyll content. However, when chlorophyll content was more than 25 μg·cm^{−2}, ρ_{red} remained constant. On the other hand, ρ_{green} exhibited a strong negative relationship with chlorophyll content across a wide range from 0.3 to 50 μg·cm^{−2}.

The seven VIs calculated from ^{−2}, and EVI and GNDVI saturated when chlorophyll content was higher than 20 μg·cm^{−2} and 35 μg·cm^{−2}, respectively. EVI, NDVI, GNDVI, and SR were strongly positively correlated with chlorophyll content, with coefficients of determination (R^{2}) of 0.66, 0.68, 0.83, and 0.74, respectively (P < 0.001). GRVI and mNDVI were more weakly correlated with chlorophyll content, with R^{2} values of 0.32 (P < 0.05) and 0.58 (P < 0.001), respectively. In contrast, CI_{green} had the strongest linear correlation with chlorophyll content (R^{2} = 0.87; P < 0.001). Therefore, CI_{green}, EVI, NDVI, GNDVI, and SR were selected as candidate VIs for the canopy- and satellite-scale analyses.

For GPP_{capacity} selection, diurnal NEP and VPD averaged over 16-day periods were examined. The results are shown in _{capacity} selection criteria for each study site.

To determine P_{max_capacity} and α_{slope}, the relationships between GPP_{capacity} and PAR were examined for broadleaf deciduous temperate trees at JP-TKY in 2003 and 2004, as shown in _{capacity}. The lines represent the fitting curves of each 16-day period with α_{slope} averaged over the growing season. In many cases, the fitted P_{max_capacity} saturated in regions of unrealistically high PAR, such as 2,500 μmol·m^{−2}·s^{−1}. Thus, we fixed maximum PAR at 2,000 μmol·m^{−2}·s^{−1} and defined the maximum GPP_{capacity} when PAR = 2,000 μmol·m^{−2}·s^{−1} as P_{max_capacity2000}.

The relationships between the five candidate VIs (CI_{green}, EVI, NDVI, GNDVI, and SR) and P_{max_capacity2000} at the canopy scale are shown in _{max_capacity2000}, with R^{2} values of 0.78, 0.62, 0.64, 0.63, and 0.69, respectively (all P < 0.001). EVI, NDVI, GNDVI, and SR became clearly saturated when P_{max_capacity2000} was more than about 0.4 mg·CO_{2}·m^{−2}·s^{−1}, whereas CI_{green} did not saturate. The strongest linear correlation occurred for CI_{green}. We obtained the following empirical equation for the relationship between CI_{green} and P_{max_capacity2000} (mg·CO_{2}·m^{−2}·s^{−1}): P_{max_capacity2000} = 0.13 × CI_{green}–0.13.

_{green}, EVI, NDVI, GNDVI, and SR) and P_{max_capacity2000} at the satellite scale. EVI, NDVI, GNDVI, and SR exhibited strong correlations with P_{max_capacity2000}, with R^{2} values of 0.63 (P < 0.05), 0.64 (P < 0.05), 0.69 (P < 0.01), and 0.59 (P < 0.05), respectively. However, the strongest linear correlation occurred for CI_{green} (R^{2} = 0.72, P < 0.01). Thus, compared to EVI, NDVI, GNDVI, and SR, CI_{green} was the best VI for estimating P_{max_capacity2000}. We obtained the following empirical equation for the relationship between CI_{green} and P_{max_capacity2000} (mg·CO_{2}·m^{−2}·s^{−1}): P_{max_capacity2000} = 0.15 × CI_{green} – 0.37.

The relationships of GPP_{capacity} and PAR for various plant functional types are shown in _{max_capacity2000} was 1.87 mg·CO_{2}·m^{−2}·s^{−1}. _{max_capacity2000} and α_{slope} averaged over the growing season and the VPD thresholds for each of the plant functional types.

_{green} and P_{max_capacity2000} for the seven plant functional types. For CA-Let, JP-TMK, JP-TKY, JP-Mase, and JP-FJY, the R^{2} values of the CI_{green}–P_{max_capacity2000} correlations were 0.81, 0.84, 0.67, 0.95, and 0.70, respectively. A linear correlation was not found for TH-SKR, as CI_{green} and P_{max_capacity2000} changed very little. Additionally, a linear correlation was not observed for US-Dk1. The linear regression functions (

To test the potential of CI_{green} to estimate P_{max_capacity2000} in a different year, we used the empirical equations of year 2003 (_{green} calculated from MOD09A1 for the CA-Let, JP-TMK, JP-Mase, and TH-SKR sites in 2002 and the JP-TKY and JP-FJY sites in 2004. Comparisons of estimated P_{max_capacity2000} and observed P_{max_capacity2000} with standard error bars are shown in _{max_capacity2000} was obtained from the empirical equations in _{max_capacity2000} was calculated from the flux data. Standard errors of estimated P_{max_capacity2000} were calculated from standard-error propagation of empirical relationships between CI_{green} and P_{max_capacity2000}. Standard errors of observed P_{max_capacity2000} were calculated from standard-error propagation of least-square fitting of the light-response curves. The standard errors of the estimated P_{max_capacity2000} from linear regression were larger than those of observed P_{max_capacity2000}. _{max_capacity2000} values for all plant functional types were within the standard error of the estimated P_{max_capacity2000}, especially in spring, when most of the plant functional types showed better matching. These results indicate that the CI_{green} and empirical equations successfully estimated P_{max_capacity2000} for all plant functional types. For TH-SKR, only five periods are shown because most CI_{green} data were affected by clouds.

At the leaf scale, the results show that CI_{green} has a strong correlation with variation in leaf chlorophyll content from a wide range of species and leaf development stages (

Because leaf chlorophyll content increases toward the middle of a leaf [_{green} because NIR is insensitive to chlorophyll content and the ratio between the insensitive and sensitive bands can minimize the variations in leaf-scattering properties [_{green} is suitable for estimating leaf chlorophyll content.

Yoder and Waring [

At the canopy and satellite scales, P_{max_capacity2000} was used instead of the canopy chlorophyll content because chlorophyll content within a vegetation canopy is positively related to the productivity of that vegetation [_{max_capacity} from the light-response curve, some researchers have applied a non-rectangular hyperbolic equation [_{max}, and convexity. A non-rectangular hyperbola yielded a better fit than a rectangular hyperbola equation with only two parameters (initial slope and P_{max}). However, we chose to use a rectangular hyperbola (_{max_capacity} with constant α_{slope} to simplify the methodology and minimize the number of parameters [_{slope} for calculating P_{max_capacity2000} for each plant functional type.

To estimate GPP at the global scale, we prefer the VI which has linear relation with chlorophyll content and P_{max_capacity2000}. If we use the VI which has exponential relation with chlorophyll content and P_{max_capacity2000}, it may cause big error in high chlorophyll content or P_{max_capacity2000} region. In big VI region, and with exponential form VI, small error of VI makes big error of chlorophyll content and P_{max_capacity2000}. Therefore, we selected VIs that had linear correlations with P_{max_capacity2000}. The CI_{green} showed a strong linear correlation (R^{2} > 0.67) with P_{max_capacity2000} for each of the plant functional types and did not have saturation problems in various plant-canopy structures. Linear correlations between remote sensing-obtained VI values and GPP from local flux measurements were found in North American vegetation [

In _{green} range of 3.5–6.5 is relatively high for grassland, suggesting contamination by light reflected from surrounding evergreen and deciduous forests. Similarly, linear correlations were not found at TH-SKR, where CI_{green} and large photosynthetic capacity changed less year-round. These results indicate that when canopy greenness is stable, other meteorological factors will play limiting roles for GPP [_{max_capacity2000} at TH-SKR in 2003 were 1.37 and 0.94 mg·CO_{2}·m^{−2}·s^{−1}, respectively. These results are consistent with those of Aguilos _{max}) at TH-SKR in 2003 were 1.54 and 0.82 mg·CO_{2}·m^{−2}·s^{−1}, respectively. Tropical forests have the least seasonality in terms of carbon absorption, emission, and greenness [_{max_capacity2000} = 0.001 × CI_{green} + 0.006; R^{2} = 0.44, P<0.01).

P_{max_capacity2000} can be converted to the maximum incident LUE (GPP/PAR) as shown in _{green}.

Our P_{max_capacity2000} estimation concept is more similar to the greenness and radiation (GR) model [_{capacity} differs from those of both the GR and LUE models. The GR and LUE models assume a linear relationship between GPP and incident PAR or PAR during an integral time such as 1 day or 1 month. Our GPP capacity estimation framework introduced a non-linear relationship between photosynthesis velocity and PAR. P_{max_capacity} was the parameter of the photosynthesis response curve (_{green}. Using this parameter, the photosynthesis response curve was determined. Using the response curve and PAR data, the velocity of GPP_{capacity} was estimated. For calculation of GPP during a particular time frame, integration of the velocity of GPP_{capacity} is required. Our GPP_{capacity} estimation framework is the differential form.

Moreover, our approach coincides with that of Gitelson _{potential}) arose because of its failure to detect variation in GPP related to short-term (minutes to hours) changes in controlling factors that do not immediately affect crop chlorophyll content. And Hashimoto _{capacity}) is quite efficient.

In the present study, we applied a VI to estimate canopy chlorophyll content of the effective leaf area exposed to light. The next step is to refine the effective leaf area by incorporating plant structural characteristics, such as leaf angle orientation and sunlit/shaded leaf and foliar clumping, which are important to GPP estimation because they affect light interception by leaves and light penetration into the canopy [_{green} and P_{max_capacity2000} for each plant functional type should be compared with results from a radiative transfer model that can predict the radiative transfer of solar energy or changes in leaf physiology as canopy profiles adapt to sunlight [

To accurately estimate the maximum rate of low-stress GPP (called “GPP_{capacity}”) based on the light-response curve, an appropriate VI was selected. The green chlorophyll index, CI_{green} (ρ_{NIR}/ρ_{green}-1), had a strong linear correlation with chlorophyll content at the leaf scale and with GPP_{capacity} at the canopy and satellite scales. We demonstrated that CI_{green} could capture seasonal changes and variation in photosynthesis patterns in six main plant functional types. Therefore, we consider CI_{green} to be suitable for GPP_{capacity} estimation globally, especially for GCOM-C/SGLI satellite data. Nevertheless, CI_{green} should be validated further in different areas and with other plant functional types [

For implementation of GPP_{capacity} global estimation using GCOM-C/SGLI, we should first calculate CI_{green} from satellite data and use the empirical linear equation from _{max_capacity2000} (the maximum GPP_{capacity} when PAR = 2,000 μmol·m^{−2}·s^{−1}). The P_{max_capacity} (the maximum GPP_{capacity} under light saturation) in _{max_capacity2000}, PAR of 2,000 μmol·m^{−2}·s^{−1} and α_{slope} (photosynthetic quantum efficiency) in _{capacity} should be estimated with the PAR obtained by satellite using

Additionally, in the future, the global GPP_{capacity} can be combined with vegetation stress maps to estimate global GPP. Stress maps may be calculated from environmental conditions such as the vapor pressure deficit, leaf water potential, and soil water content. We expect that our approach will be useful for improving the accuracy of global GPP estimations derived from satellite data.

This research was partly supported by a Global Change Observation Mission (GCOM; PI#102 and 106) of the Japan Aerospace Exploration Agency (JAXA). We acknowledge JAXA for GLI data, the Land Processes (LP) DAAC for MODIS datasets and FLUXNET Network (AmeriFlux, Fluxnet-Canada and Asia flux) for supporting flux data. We are grateful to Nobuko Saigusa at National Institute for Environmental Studies (NIES) for supporting Tomakomai dataset, Hiroaki Kondo and Shohei Murayama at National Institute of Advanced Industrial Science and Technology (AIST) for supporting Takayama dataset. We gratefully thank Yoshikazu Ohtani from Forestry and Forest Products Research Institute (FFPRI) for Fujiyoshida dataset and Takeshi Motohka from Phenological Eyes Network (PEN) for providing Takayama site’s HSSR data. We acknowledge Institute for Basin Ecosystem Studies, Gifu University for providing precipitation data of JP-TKY in 2004. We thank Minoru Gamo from AIST, Samreong Panuthai from Department of National Parks, Wildlife and Plants Conservation, Thailand and Taksin Artchawakom from Sakaerat Environmental Research Station, Institute of Scientific and Technological Research (TISTR), Thailand for supporting Sakaerat dataset. We acknowledge Akira Miyata from National Institute for Agro-Environmental Sciences (NIAES) for Mase dataset. Authors would like to thank Ministry of Education, Culture, Sports, Science and Technology (MEXT) for supporting a scholarship. We acknowledge a project of Nara Women’s University for partly research budget support. The authors also would like to thank anonymous reviewers who gave valuable suggestion that has helped to improve the quality of the manuscript.

_{2}/H

_{2}O exchange in three Mediterranean ecosystems

_{2}flux measurements in rice

_{2}exchange in response to drought in a Southern California chaparral ecosystem

_{2}flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: Potential for quantitative spatial extrapolation

_{2}flux for diverse vegetation types and climate conditions

_{2}fluxes over plant canopies and solar radiation: a review

_{2}fluxes in cool-temperate coniferous and deciduous broadleaf forests in Japan

GPP data derived from EC flux data were used to calculate P_{max_capacity2000}. If GPP data were not available from flux projects, we could calculate GPP using respiration data [

GPP is calculated using the NEP data plus ecosystem respiration (Rec) as
_{air}).

We determined the nighttime Rec [_{air} and applied the function to the daytime data to estimate daytime Rec using the following simple exponential function:

_{air} and Rec at sites measured by the EC method under nearly neutral atmospheric stability using U* filtering to avoid the flux underestimation on stable nights caused by friction velocity [^{−1} for JP-Mase [^{−1} for JP-FJY [^{−1} for CA-Let [^{−1} for JP-TMK [^{−1} for JP-TKY [_{air}[

GPP was calculated using the NEP daytime and respiration estimation results. To estimate respiration, the NEP nighttime data were used (NEP <zero). We excluded NEP nighttime data with more than zero precipitation because soil water may affect the respiration rate. _{air} for CA-Let, JP-TKY2004, JP-Mase, JP-FJY, and TH-SKR, respectively. At the JP-TMK site [_{soil} instead of T_{air}. The value of Rec had a significant positive correlation with T_{air}. The relationships were more exponential than linear, as shown in _{air} or T_{soil} and nighttime NEP with an exponential equation. The lowest Rec was found at CA-Let, which is located in the arctic zone [

Ecosystem respiration (Rec),

Canopy light-response curve. Low-stress global gross primary production (GPP_{capacity}) (mg·CO_{2}·m^{−2}·s^{−1}) is the low-stress GPP, P_{max_capacity} (mg·CO_{2}·m^{−2}·s^{−1}) is the maximum GPP_{capacity} under light saturation, and α_{slope} is photosynthetic quantum efficiency representing the initial slope of the light-response curve.

Three patterns of diurnal variation in net ecosystem production for the photosynthesis. Pattern 1: a single diurnal peak, indicating that no stress occurs; Pattern 2: two diurnal peaks, which is a common occurrence in nature; and Pattern 3: one peak with severe midday depression, which occurs mostly in drought areas.

Relationship between the reflectance at each SGLI spectral band and the leaf chlorophyll content.

The best-fit regression of the linear relationships between (_{green}), (^{−2}) at the leaf scale.

Diurnal variation in the mean half-hourly dataset over 16-day periods: (

Light-response curve of low-stress GPP (GPP_{capacity}) and PAR in broadleaf deciduous temperate trees (JP-TKY) in (_{capacity}.

The best fit regression of the linear relationships between various VIs calculated from daily HSSR datasets and P_{max_capacity2000} of broadleaf deciduous temperate trees at JP-TKY 2004 at the canopy scale: (_{green}, (

The best fit regression of the linear relationships between various VIs from 16-day-period MOD09A1 data and P_{max_capacity2000} of broadleaf deciduous temperate trees at JP-TKY 2004 at the satellite scale: (_{green}, (

The canopy light-response curve of half-hourly data of low-stress GPP (GPP_{capacity}) and PAR for the various plant functional types in 2003: (_{capacity}.

Relationships between CI_{green} calculated from 16-day period MOD09A1 data and P_{max_capacity2000} and maximum light-use efficiency (LUE_{max}) for the seven plant functional types in 2003.

Comparison of estimated P_{max_capacity2000} (black line) and observed P_{max_capacity2000} (red line) with error bars. (_{max_capacity2000} calculated using empirical linear correlation of 2003 and CI_{green} of 2002 and 2004. Observed P_{max_capacity2000} estimated from EC flux tower data of 2002 and 2004 using light-response curve fitting by the least-squares method.

Global Change Observation Mission-Climate (GCOM-C)/ second-generation global imager (SGLI) and TERRA/MODIS spectral bands (showing only bands 1 to 4).

ρ_{blue} |
443 | 10 | 469 | 20 |

ρ_{green} |
530 | 20 | 555 | 20 |

ρ_{red} |
673.5 | 20 | 655 | 50 |

ρ_{NIR} |
868.5 | 20 | 858.5 | 35 |

Description of the study sites representing seven plant functional types.

Lethbridge | Hokkaido | Takayama | Tsukuba | Yamanashi | Nc | Sakaerat | |

Canada | Japan | Japan | Japan | Japan | US. | Thailand | |

49.709°N | 42.737°N | 36.146°N | 36.054°N | 35.454°N | 35.971°N | 14.492°N | |

−112.940°W | 141.519°E | 137.423°E | 140.027°E | 138.762°E | −79.093°W | 101.916°E | |

C3 grass, arctic | NDT | BDT,Temperate | Crop | NET,Temperate | C3 grass | BET,Tropical | |

Short/mixed grass prairie (C3/C4) | Japanese Larch | Deciduous Oak, Birch | Rice | Japanese red pine | Tall fescue,C3 grass and forbs | Dipterocarp | |

- | 45 | 50 | - | 90 | 1 | - | |

960 | 140 | 1420 | 13 | 1030 | 163 | 535 | |

- | 16 | 15–20 | 1.2 | 20 | 0.1–1 | 35 | |

4 | 27 | 25 | 3 | 25.4 | 3 | 45 | |

5.36 | 6.61 | 7.2 | 12.9 | 10.1 | 15.5 | 24.1 | |

0.2 | 0.3 | 0.5 | 0.1 | 0.12 | 0.2 | 0.2 | |

Plant Functional Types (PFTs) 1. NET, temperate = Needleleaf Evergreen Temperate Trees 2. NDT = NeedleleafDeciduous Trees 3. B = 4.BDT,Temperate = Broadleaf Deciduous Temperate Trees, U* = friction velocity.

Summary of the growing season average P_{max_capacity2000} and initial slope, and the VPD threshold for the seven plant functional types.

_{max_capacity2000}; mg·CO_{2}·m^{−2}·s^{−1} (Growing Season) |
_{slope}) | |||
---|---|---|---|---|

CA-Let | Rec = 0.29exp(0.037T_{air}) |
2 | 0.47 (Apr–Sept) | 0.0029 |

JP-TMK | Rec = 0.3exp(0.08T_{soil}) |
2 | 1.37 (May–Oct) | 0.0016 |

JP-TKY | Rec = 0.23 × 0.23exp(0.08T_{air}) |
2 | 0.97 (May–Sept) | 0.0023 |

JP-Mase | Rec = 0.18exp(0.067T_{air}) |
2 | 0.96 (May–Aug2) | 0.0017 |

JP-FJY | Rec= 0.25 × 0.25exp(0.08T_{air}) |
2 | 1.00 (Apr–Oct) | 0.0014 |

US-Dk1 | - |
2 | 0.70 (Apr–Oct) | 0.0035 |

TH-SKR | Rec = 0.025 × 2.57exp((T_{air} – 10)/10) |
2 | 1.18 (Apr–Oct, except May2 and June) | 0.0009 |

Averaged values (averaged over growing season), T_{soil} = Soil temperature, T_{air} = Air temperature, VPD = Vapor Pressure Deficit;

Rec was not calculated because AmeriFlux provided GPP for this site.

Coefficients, intercepts and correlation coefficients (R^{2}) of the linear correlations (_{green} and P_{max_capacity2000} for CA-Let, JP-TMK, JP-TKY, JP-Mase, and JP-FJY.

^{2} | ||||
---|---|---|---|---|

C3 grass, arctic | 0.388 | −0.235 | 0.81 | |

Needleleaf deciduous trees (NDT) | 0.232 | −0.145 | 0.84 | |

Broadleaf deciduous trees, temperate (BDT, temperate) | 0.169 | −0.355 | 0.67 | |

Crops (paddy field) | 0.371 | −0.361 | 0.95 | |

Needleleaf evergreen trees, temperate (NET, temperate) | 0.179 | 0.182 | 0.70 |