Estimation Of Light Use Efficiency Research Articles

Improving the ability of PRI in light use efficiency estimation by distinguishing sunlit and shaded leaves in rice canopy

ABSTRACT Light use efficiency (LUE) plays an important role in gross primary production (GPP), so accurate estimation of LUE is crucial. The estimation of LUE using the photochemical reflectance index (PRI) is considerably promising, but on the other hand there is always uncertainty in obtaining the PRI due to the changing viewing angle and sun position in remote sensing observations. The objective of this study was to distinguish between sunlit and shaded leaves to improve the performance of PRI estimating LUE, and we used rice canopy reflectance data collected by the automatic multi-angle remote sensing platform and flux data for each half hour from 9:00 to 15:00 in Shouxian, Anhui Province, China. We modified the two-leaf model based on the characteristics of the rice canopy, was thought of by distinguishing between sunlit and shaded leaves PRI by two-leaf model on the rice canopy. The results showed that the -LUE correlation improved relative to PRI-LUE on clear days and was essentially negligible on cloudy days. We found that the photosynthetically active radiation (PAR), temperature, and humidity had some influence on , the -LUE correlation was strongest at higher temperature and vapour pressure deficit (VPD) and was linear with the change of temperature. We conclude that the modified two-leaf model improves the integrity of the PRI remote sensing observation and increases the accuracy of its LUE estimation by adequately separating sunlit and shaded leaves in the rice canopy.

Estimation of photosynthetic dynamics in forests from daily measured fluorescence and PRI data with adjustment for canopy shadow fraction

We conducted year-long measurements of the photochemical reflectance index (PRI) and solar-induced fluorescence in the O2A oxygen band (SIFA) at a Norway spruce forest and a European beech forest to study relationships of these remote sensing variables to photosynthesis by trees in grown forest stands. Measured PRI and SIFA values were linked to changes in forest gross primary productivity (GPP) and light-use efficiency (LUE). Changes in the shadow fraction (αS) within tree crowns influenced PRI and fluorescence signals. In the spruce forest, the quantum yield of SIFA (FYSIFA) decreased around midday together with photosynthesis and GPP. Such decreases in FYSIFA were accompanied by an increase in the αS. In the beech forest, we detected an increase in FYSIFA together with a decrease in αS in the afternoon hours. The overall sensitivity of PRI to LUE was variable according to the season, presumably influenced by complex changes in photosynthetic pigments. PRI and FYSIFA showed weak correlations with canopy LUE; however, when considered together, the correlation was strengthened (R2 was 0.63 and 0.34 in spruce and beech forest, respectively). Our model predicting LUE dynamics includes a diurnal minimum of PRI and canopy αS to make allowances for seasonal changes in photosynthetic pigments and for diurnal variability of the shadow fraction in forests. The incorporation of these correcting factors allowed us to estimate LUE at R2 = 0.68 (spruce) and 0.53 (beech). The modeling equations appeared sensitive to the absorbed photosynthetically active radiation (APAR), but less sensitive to the GPP of these forests. Substituting pigments correction with introducing differential PRI (ΔPRI) into the model did not significantly improve the LUE estimation across the season. Our results show that the joint use of PRI and fluorescence improves LUE and GPP estimation accuracy in both daily and seasonal observations.

Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models

Diffuse photosynthetically active radiation (PARdiff) is instrumental to the light use efficiency (LUE) of vegetation. Accurately assessing the impact of PARdiff on crop LUE can better our understanding of the carbon cycle in cropland ecosystems. LUE estimates from six remote sensing models (including four big-leaf models and two two-leaf models) and two crop production models were compared with measured FLUXNET LUE data from cropland sites under different PARdiff fraction (FDIFFPAR) intervals. Compared with the FLUXNET observations, the Eddy Covariance-Light Use Efficiency (EC-LUEa) model exhibited the best LUE estimation (R2 = 0.250, RMSE = 0.868 gC·MJ−1, and Bias = −0.005 gC·MJ−1) owing to the use of more accurate calculation scheme of environmental stress factors. LUEs calculated from FLUXNET observational data were positively correlated with FDIFFPAR, but only LUEs simulated by the Moderate Resolution Imaging Spectroradiometer Photosynthesis (MOD17) and Two-Leaf Light Use Efficiency (TL-LUE) models increased with increasing FDIFFPAR. This is attributed to the fact that the MOD17 model divides the crop growth types into cereal and broadleaf, while the TL-LUE model considers the change of light interception with increased FDIFFPAR. Furthermore, the maximum LUE (LUEmax) increased with FDIFFPAR at FLUXNET observational sites, but the eight models could not capture the effects of PARdiff on the crop LUEmax. Among the eight models, the LUEmax–FDIFFPAR relationship simulated by the two-leaf models fluctuated because the crops were divided into sunlit and shaded leaves, while the big-leaf and crop production models used a constant LUEmax and showed a constant LUEmax–FDIFFPAR relationship. Additionally, big-leaf models performed better than two-leaf models for gross primary production (GPP) simulation in the cropland ecosystem, which is related to the planting density and vegetation structure. These results demonstrate the importance of considering the impact of FDIFFPAR on LUEmax in LUE modeling.

Combining NDVI, PRI and the quantum yield of solar-induced fluorescence improves estimations of carbon fluxes in deciduous and evergreen forests

We used automated spectroradiometers to continuously monitor changes in the optical parameters of phenological and photosynthetic traits in beech and spruce forests. We examined seasonal variations in the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and solar-induced fluorescence in the oxygen A band (SIFA) that was estimated using a 3-FLD discrimination method from radiance data. The optical parameters tracked the activation and cessation of photosynthesis in spring and autumn. Data at photon fluxes >1200 μmol m−2 s−1 during extended noon hours were used to link the seasonal PRI and SIFA variations to the dynamics of photosynthesis. Seasonal PRI was significantly correlated with photosynthetic light-use efficiency (LUE) with R2 values of 0.66 and 0.48 for the measurements in beech and spruce forests, respectively. SIFA emissions were significantly correlated with the gross primary production (GPP) of the evergreen spruce forest (R2 = 0.47), but R2 was only 0.13 when measured in the beech forest. The correlations between the optical parameters and GPP or LUE, however, tended to be lower when using a dataset with constant NDVI. Introducing an equation combining NDVI, PRI, and the quantum yield of SIFA emission increased R2 for LUE estimation to 0.77 in the spruce forest and 0.63 in the beech forest. GPP was estimated from the parametric equation with improved accuracy reaching R2 = 0.53 and RMSE = 5.95 μmol CO2 m−2 s−1 in spruce forest and R2 = 0.58 and RMSE = 5.23 μmol CO2 m−2 s−1 in beech forest. Parametric equations were more efficient in estimating photosynthesis in datasets that consisted of an entire season's data. By combining NDVI, PRI and the quantum yield of SIFA, we could thus substantially improve estimations of carbon fluxes in diverse deciduous and evergreen canopies.

Species and stand-age driven differences in photochemical reflectance index and light use efficiency across four temperate forests

Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUEmax) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress (fstress). We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUEmax (R2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations (p < 0.05) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.

Estimation of light use efficiency by using remote sensing data

Estimation of light use efficiency by using remote sensing data

Correction of PRI for carotenoid pigment pools improves photosynthesis estimation across different irradiance and temperature conditions

We studied the influence of changing carotenoid pigments on the sensitivity of the photochemical reflectance index (PRI) to photosynthesis dynamics. The goal of the measurements was to examine how the introduction of ΔPRI into the working dataset can improve the estimation of photosynthesis. Spectral and photosynthetic characteristics of European beech and Norway spruce saplings were periodically measured in growth chambers with an adjustable irradiance and temperature. Patterns of environmental changes inside the growth chambers were created by periodic changes in irradiance and temperature. Four general irradiance periods lasting 10–12 days each were established. Introduced irradiance regimes varied in the sum of daily irradiance and amplitude of irradiance changes. Temperature was changed with more complex patterns to induce changes in xanthophyll cycle pigments at various time scales within these regimes. Our measurements confirmed the PRI linkage to photosynthetic light use efficiency (LUE). However, the strength of this connection was found to be dependent on changing pigment concentrations, specifically on the change in the ratio of chlorophylls to carotenoids. Furthermore, a negative interference in photosynthesis estimation from PRI was recorded if the temperature was lowered overnight to 12 °C. The differential PRI (ΔPRI), calculated as the simple difference between the PRI value measured during the daytime period and in early morning (PRI0), revealed a decreased effect from pigments and cold temperature on LUE estimation. The regression analysis among all measured data identified an increased association between PRI and LUE following the introduction of ΔPRI from R2 = 0.26 to 0.69 in beech and from R2 = 0.61 to 0.77 in spruce data. The analyses showed that both leaf carotenoid concentrations and the conversion state of xanthophyll cycle pigments played a significant role in determining PRI and PRI0 values and that the accurate assessment of these pigments in PRI across multiple levels of stress from irradiance and temperature might improve estimations of LUE through ΔPRI. In our data, ΔPRI appeared to be a good measure of photosynthesis, the dynamics of which differed between beech and spruce saplings upon switching temperatures.

Assessing canopy structure effect on the estimation of light-use efficiency in a subtropical evergreen forest

Assessing canopy structure effect on the estimation of light-use efficiency in a subtropical evergreen forest

Potential of Photochemical Reflectance Index for Indicating Photochemistry and Light Use Efficiency in Leaves of European Beech and Norway Spruce Trees

Hyperspectral reflectance is becoming more frequently used for measuring the functions and productivity of ecosystems. The purpose of this study was to re-evaluate the potential of the photochemical reflectance index (PRI) for evaluating physiological status of plants. This is needed because the reasons for variation in PRI and its relationships to physiological traits remain poorly understood. We examined the relationships between PRI and photosynthetic parameters in evergreen Norway spruce and deciduous European beech grown in controlled conditions during several consecutive periods of 10–12 days between which the irradiance and air temperature were changed stepwise. These regime changes induced significant changes in foliar biochemistry and physiology. The responses of PRI corresponded particularly to alterations in the actual quantum yield of photosystem II photochemistry (ΦPSII). Acclimation responses of both species led to loss of PRI sensitivity to light use efficiency (LUE). The procedure of measuring PRI at multiple irradiance-temperature conditions has been designed also for testing accuracy of ΔPRI in estimating LUE. A correction mechanism of subtracting daily measured PRI from early morning PRI has been performed to account for differences in photosynthetic pigments between irradiance-temperature regimes. Introducing ΔPRI, which provided a better estimate of non-photochemical quenching (NPQ) compared to PRI, also improved the accuracy of LUE estimation. Furthermore, ΔPRI was able to detect the effect of drought, which is poorly observable from PRI.

Estimation of gross primary production in Moso bamboo forest based on light-use efficiency derived from MODIS reflectance data

ABSTRACTAssessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.

Improving the ability of the photochemical reflectance index to track canopy light use efficiency through differentiating sunlit and shaded leaves

Accurate estimation of light use efficiency (LUE) of plant canopies is essential for calculating gross primary productivity (GPP) using LUE models and is also useful for calibrating process-based models for regional and global applications. A promising method for estimating LUE is through remote sensing of the photochemical reflectance index (PRI). However, there are internal (e.g. pigment concentrations) and external factors (e.g. environmental conditions and sun-target-view geometry) that affect PRI signals. Considering the reflectance difference between sunlit and shaded leaves, the ratio of observed canopy reflectance to leaf reflectance is used to represent the observed fraction of sunlit leaves, and the observed fraction of shaded leaves is calculated with a geometrical optical model. Thus, a canopy-level PRI observation is separated into sunlit and shaded PRI values, and a two-leaf canopy PRI (PRIt) is calculated as sum of these two values weighted by their respective sunlit and shaded leaf area indices. The usefulness of PRIt in assessing the canopy-level LUE is evaluated with automated multi-angle PRI observations acquired on a flux tower from April to September 2013 over a sub-tropical coniferous forest in southern China. In each 15-minute observation cycle, PRI is observed at four view zenith angles fixed at (37°, 47°, 57°) or (42°, 52°, 62°) and the instantaneous solar zenith angle in the azimuth angle range from 45° to 325° (from the geodetic north). In both the half-hourly and daily time steps, PRIt can effectively improve (>50% and >35% increases in R2, respectively) the ability as a proxy of LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. In the dry season from July to September, correlations of PRI with LUE at daily time steps are much stronger in the two-leaf case than in the big-leaf case. The correlation between PRIt and LUE is the strongest (R2=0.785, p<0.001) in July. PRIt is very effective in detecting the light and low-moderate drought stress on LUE at half-hourly time steps, while ineffective in detecting severe atmospheric water and heat stress, which is probably due to alternative radiative energy sink, i.e. photorespiration. Overall, the two-leaf approach well overcomes some external effects (e.g. sun-target-view geometry) that interfere with PRI signals.

Photosynthetic efficiency of northern forest ecosystems using a MODIS-derived Photochemical Reflectance Index (PRI)

Abstract This study evaluates a direct remote sensing approach from space for the determination of ecosystem photosynthetic light use efficiency (LUE), through measurement of vegetation reflectance changes expressed with the Photochemical Reflectance Index (PRI). The PRI is a normalized difference index based on spectral changes at a physiologically active wavelength (~ 531 nm) as compared to a reference waveband, and is only available from a very few satellites. These include the two Moderate-Resolution Imaging Spectroradiometers (MODIS) on the Aqua and Terra satellites each of which have a narrow (10 nm) ocean band centered at 531 nm. We examined several PRI variations computed with candidate reference bands, since MODIS lacks the traditional 570 nm reference band. The PRI computed using MODIS land band 1 (620–670 nm) gave the best performance for daily LUE estimation. Through rigorous statistical analyses over a large image collection (n = 420), the success of relating in situ daily tower-derived LUE to MODIS observations for northern forests was strongly influenced by satellite viewing geometry. LUE was calculated from CO2 fluxes (mol C mol− 1 absorbed quanta) measured at instrumented Canadian Carbon Program flux towers in four Canadian forests: a mature fir site in British Columbia, mature aspen and black spruce sites in Saskatchewan, and a mixed deciduous/coniferous forest site in Ontario. All aspects of the viewing geometry had significant effects on the MODIS-PRI, including the view zenith angle (VZA), the view azimuth angle, and the displacement of the view azimuth relative to the solar principal plane, in addition to illumination related variables. Nevertheless, we show that forward scatter sector views (VZA, 16°–45°) provided the strongest relationships to daily LUE, especially those collected in the early afternoon by Aqua (r2 = 0.83, RMSE = 0.003 mol C mol− 1 absorbed quanta). Nadir (VZA, 0° ± 15°) and backscatter views (VZA, − 16° to − 45°) had lower performance in estimating LUE (nadir: r2 ~ 0.62–0.67; backscatter: r2 ~ 0.54–0.59) and similar estimation error (RMSE = 0.004–0.005). When directional effects were not considered, only a moderately successful MODIS-PRI vs. LUE relationship (r2 = 0.34, RMSE = 0.007) was obtained in the full dataset (all views & sites, both satellites), but site-specific relationships were able to discriminate between coniferous and deciduous forests. Overall, MODIS-PRI values from Terra (late morning) were higher than those from Aqua (early afternoon), before/after the onset of diurnal stress responses expressed spectrally. Therefore, we identified ninety-two Terra-Aqua “same day” pairs, for which the sum of Terra morning and Aqua afternoon MODIS-PRI values (PRIsum) using all available directional observations was linearly correlated with daily tower LUE (r2 = 0.622, RMSE = 0.013) and independent of site differences or meteorological information. Our study highlights the value of off-nadir directional reflectance observations, and the value of pairing morning and afternoon satellite observations to monitor stress responses that inhibit carbon uptake in Canadian forest ecosystems. In addition, we show that MODIS-PRI values, when derived from either: (i) forward views only, or (ii) Terra/Aqua same day (any view) combined observations, provided more accurate estimates of tower-measured daily LUE than those derived from either nadir or backscatter views or those calculated by the widely used semi-operational MODIS GPP model (MOD17) which is based on a theoretical maximum LUE and environmental data. Consequently, we demonstrate the importance of diurnal as well as off-nadir satellite observations for detecting vegetation physiological processes.

Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non-destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGMCC) or the weighted difference vegetation index (CGMWDVI). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGMWDVI improved yield prediction compared to CGMCC. Both CGMWDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incorporation of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato.

Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI)

Remote sensing of light use efficiency (LUE) is a prerequisite for a timely evaluation of vegetation primary productivity at regional scale. With a potential in detecting the leaf de-epoxidation state of the xanthophyll cycle for heat dissipation, the photochemical reflectance index (PRI) has been demonstrated as a proxy of LUE for various plant functional types at leaf, canopy and ecosystem scales. However, increasing evidence shows that the operational application of PRI using remote sensing data is confounded by canopy structural effects, e.g., the presence of shadows in remote sensing pixels and directional effects associated with changes in illumination and viewing angles. The most important variable that controls the structural associated characteristics is the leaf area index (LAI), which is a major determinant of the absorbed photosynthetic active radiation (APAR) by a canopy. Using ground measured PRI and LUE over three growing seasons on winter wheat in China during 2005–2007, we found that canopy LAI overall explained 66% (p<0.001) variance of PRI, indicating the essential of removing influences of external structural factors on PRI. Suggested by this, we defined the structural-related signal in PRI (sPRI) as a function of LAI and consequently, the residual PRI (rPRI=PRI-sPRI) would be independent on canopy structural characteristics. Our results showed that a mixed non-linear model using rPRI and variety and sampling time as fixed and random effects, respectively, had an improved accuracy in the estimation of LUE over PRI with increased coefficients of determination (R2) for both the overall dataset and data of each year. These findings support the structural dependence of PRI and provide a solution for the removal of the structural signal. Further analysis is needed for the application of our approach in other ecosystems that have more complicate canopy structural characteristics than herbaceous monocultures.

Estimation of light-use efficiency through a combinational use of the photochemical reflectance index and vapor pressure deficit in an evergreen tropical rainforest at Pasoh, Peninsular Malaysia

In the search for a better method of estimating the light-use efficiency (LUE) of evergreen tropical rainforests, we employed remotely sensed spectral vegetation indices (VIs) to monitor both CO2 flux and canopy spectral reflectance over 3years in a lowland dipterocarp forest in Peninsular Malaysia. We investigated the sensitivity of five VIs calculated from spectral reflectance: the photochemical reflectance index (PRI), the canopy chlorophyll index (CCI), the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI) and the water index (WI).During the monitoring period, clear seasonal variations were not found in LUE, the observed VIs or the phenological timing (particularly new leaf flush) of dominant dipterocarp trees. Although leaf phenology tended to correlate with variations in the CCI, the highest correlation coefficient among the relationships between LUE and the VIs was observed in PRI (R=0.341, n=699). Among the relationships between LUE and meteorological factors, the strongest correlation was found between LUE and vapor pressure deficit (VPD; R=−0.580). These results suggest that unseasonal variation in LUE would be more affected by water conditions than leaf phenology or green leaf mass, and that the PRI has lower sensitivity for direct estimation of LUE compared to VPD in this evergreen tropical rainforest.To improve the accuracy in estimating LUE, we examined the potential of combinational use of VIs and meteorological factors. Variable selection by stepwise multiple regression showed that the best variable combination for LUE estimation was the PRI and VPD (R=0.612). The relative root mean square error (rRMSE) in the simple regression models using PRI, VPD and PRI×VPD, and the multiple regression model using PRI and VPD, was 22.5%, 19.4%, 19.0% and 18.7%, respectively. Based on these results, we concluded that (1) the estimation method solely based on the PRI as in the case of other temperate deciduous forests is not suitable in the tropical evergreen rainforest, and (2) the combinational use of the PRI and VPD offers one of the better models for estimating LUE in tropical evergreen rainforests.

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (Tm) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI×Tm parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R2 of 0.51 (p<0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI×Tm correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R2=0.68, p=0.003) and the minimum land surface temperature (LST_min, R2=0.81, p=0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055gC per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6gCm−2month−1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.

Modelling light use efficiency using vegetation index and land surface temperature from MODIS in Harvard Forest

Light use efficiency (LUE) is of great importance for carbon cycle and climate change research. This study presents a new LUE model incorporation of vegetation indices (VIs) and land surface temperature (LST) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Harvard Forest. Three indices, including the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2) and the soil-adjusted vegetation index (SAVI), were selected as indicators of forest canopy greenness. A single VI provided moderate estimates of LUE with coefficients of determination (R 2) 0.6219, 0.7094 and 0.7502 for NDVI, EVI2 and SAVI, respectively. Our results demonstrated that canopy LUE was related both to the canopy photosynthesis efficiency and air temperature (R 2 = 0.5634). Therefore, the MODIS LST product was incorporated as a surrogate for monitoring of environmental stresses as the observed relationship between LST and both air temperature (R 2 = 0.8828) and vapour pressure deficit (VPD) (R 2 = 0.6887). The new model in terms of (VI) × (Scaled (LST)) provided improved estimates of LUE estimation with R 2 of 0.7349, 0.7561 and 0.7879 for NDVI, EVI2 and SAVI, respectively. The results will be useful for the development of future LUE models based entirely on remote-sensing observations.

Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C ab into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO 2 and energy fluxes using an analytical, light-use-efficiency (LUE) approach to estimating bulk canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes, internally estimating fluctuations in effective LUE from a nominal (species-dependent) value (LUE n) in response to short-term variations in environmental conditions. LUE n, however, may vary on a daily timescale, responding to changes in plant phenology, physiological condition and nutrient status. Therefore, remote sensing methodologies for improving daily estimates of LUE n have been investigated. Day-to-day variations in LUE n were assessed for a heterogeneous corn crop field in Maryland, U.S.A. through model optimization with eddy covariance CO 2 flux tower observations. The optimized daily LUE n values were then compared to gridded estimates of C ab over the tower flux footprint, retrieved from a canopy reflectance model driven by green, red and near-infrared imagery acquired with an aircraft imaging system. The tower-calibrated LUE n data were generally well correlated with airborne retrievals of C ab, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C ab for delineating spatio-temporal variations in LUE n. The study highlights the potential synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for estimating carbon, water and heat fluxes within a two-source energy balance framework.

Revised photochemical reflectance index (PRI) for predicting light use efficiency of wheat in a growth cycle: validation and comparison

The photochemical reflectance index (PRI) was developed to trace the changes in light use efficiency (LUE) as the two contributing reflectances at 531 nm and 570 nm are closely related to the xanthophyll pigment cycle. In this paper, two revised indices of PRI (PRIR1 and PRIR2) are derived for a better prediction of LUE during the growth cycle of wheat. The signal of chlorophyll content (reflectance at 550 nm) to PRI is incorporated so that the revised indices can be used to estimate LUE values at low chlorophyll concentration. A validation was conducted using ground data (reflectance and LUE data) during a growth cycle of wheat in 2007 (17 April, 28 April, 16, 29 May). The results demonstrate that PRI cannot be used as an index for LUE estimation during the growth cycle of wheat as the relationship between PRI and LUE significantly weakened (R2 = 0.20) on 29 May when the leaves lost chlorophyll concentration in the senescent period. PRIR1 and PRIR2 are more robust than PRI for LUE estimationm, not only with a relatively stable precision (R2 = 0.62, 0.76, 0.62, 0.57 for PRIR1 and R2 = 0.62, 0.76, 0.63, 0.59 for PRIR2) but also with better linearity with LUE (standard error of regression equation between LUE and index is 0.00187, 0.00127, 0.00116, 0.00103 for PRIR1 and 0.00186, 0.00117, 0.00114, 0.00102 for PRIR2). The result of the comparison analysis indicates that the revised indices (PRIR1 and PRIR2) are more sensitive than PRI to low chlorophyll content and low leaf area index, which means they are more appropriate for LUE interpretation in these situations. Sensitivity of Sun-sensor geometry to all indices implies that all indices exhibit large variations with changes in solar zenith angle and view zenith angle. As solar zenith angle increases, all indices display different sensitivity patterns before and after hotspot positions. All indices vary greatly as the view zenith angle increases. An acceptable precision of all indices can be acquired within a departure of 10° from the nadir view.

Estimation of gross primary production in wheat from in situ measurements

Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (fAPAR), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R2 of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R2 ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI×VI×PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R2 increased largely compared to the other two methods. EVI×EVI×PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R2 of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs.