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Academic literature on the topic 'Light use efficiency (LUE) model'
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Journal articles on the topic "Light use efficiency (LUE) model"
1
Zhang, Jun, Xufeng Wang, and Jun Ren. "Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models." Land 10, no. 3 (2021): 329. http://dx.doi.org/10.3390/land10030329.
Full textAbstract:
Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines the carbon that enters the ecosystem. The remote sensing-based light use efficiency (LUE) model is one of the primary tools that is currently used to estimate the GPP at the regional scale. Many remote sensing-based GPP models have been developed in the last several decades, and these models have been well evaluated at some sites. However, an accurate estimation of the GPP remains challenging work using LUE models because of uncertainties in the model caused by model parameters, model forcing, and vegetation spatial heterogeneity. In this study, five widely used LUE models, Glo-PEM, VPM, EC-LUE, the MODIS GPP algorithm, and C-fix, were selected to simulate the GPP of the Heihe River Basin forced using in situ measurements. A multiple-model averaging method, Bayesian model averaging (BMA), was used to combine the five models to obtain a more reliable GPP estimation. The BMA was trained using carbon flux data from five eddy covariance towers located at dominant vegetation types in the study area. Generally, the BMA method performed better than any single LUE model. From the case study in the study area, it is indicated that the trained BMA is an efficient method to combine multiple LUE models and can improve the GPP simulation accuracy.
2
McCallum, I., O. Franklin, E. Moltchanova, et al. "Improved light and temperature responses for light-use-efficiency-based GPP models." Biogeosciences 10, no. 10 (2013): 6577–90. http://dx.doi.org/10.5194/bg-10-6577-2013.
Full textAbstract:
Abstract. Gross primary production (GPP) is the process by which carbon enters ecosystems. Models based on the theory of light use efficiency (LUE) have emerged as an efficient method to estimate ecosystem GPP. However, problems have been noted when applying global parameterizations to biome-level applications. In particular, model–data comparisons of GPP have shown that models (including LUE models) have difficulty matching estimated GPP. This is significant as errors in simulated GPP may propagate through models (e.g. Earth system models). Clearly, unique biome-level characteristics must be accounted for if model accuracy is to be improved. We hypothesize that in boreal regions (which are strongly temperature controlled), accounting for temperature acclimation and non-linear light response of daily GPP will improve model performance. To test this hypothesis, we have chosen four diagnostic models for comparison, namely an LUE model (linear in its light response) both with and without temperature acclimation and an LUE model and a big leaf model both with temperature acclimation and non-linear in their light response. All models include environmental modifiers for temperature and vapour pressure deficit (VPD). Initially, all models were calibrated against five eddy covariance (EC) sites within Russia for the years 2002–2005, for a total of 17 site years. Model evaluation was performed via 10-out cross-validation. Cross-validation clearly demonstrates the improvement in model performance that temperature acclimation makes in modelling GPP at strongly temperature-controlled sites in Russia. These results would indicate that inclusion of temperature acclimation in models on sites experiencing cold temperatures is imperative. Additionally, the inclusion of a non-linear light response function is shown to further improve performance, particularly in less temperature-controlled sites.
3
McCallum, I., O. Franklin, E. Moltchanova, et al. "Improved light and temperature responses for light use efficiency based GPP models." Biogeosciences Discussions 10, no. 5 (2013): 8919–47. http://dx.doi.org/10.5194/bgd-10-8919-2013.
Full textAbstract:
Abstract. Gross primary production (GPP) is the process by which carbon enters ecosystems. Diagnostic models, based on the theory of light use efficiency (LUE) have emerged as one method to estimate ecosystem GPP. However, problems have been noted particularly when applying global results at regional levels. We hypothesize that accounting for non-linear light response and temperature acclimation of daily GPP in boreal regions will improve model performance. To test this hypothesis, we have chosen four diagnostic models for comparison, namely: an LUE model (linear in its light response) both with and without temperature acclimation and an LUE model and a big leaf model both with temperature acclimation and non-linear in their light response. All models include environmental modifiers for temperature and vapour pressure deficit (VPD). Initially, all models were calibrated against four eddy covariance sites within Russia for the years 2002–2004, for a total of 10 site years. Model evaluation was performed via 10-out cross-validation. This study presents a methodology for comparing diagnostic modeling approaches. Cross validation clearly demonstrates the improvement in model performance that temperature acclimation makes in modeling GPP at strongly temperature controlled sites in Russia. Additionally, the inclusion of a non-linear light response function is shown to further improve performance. Furthermore we demonstrate the parameterization of the big leaf model, incorporating environmental modifiers for temperature and VPD.
4
Goerner, A., M. Reichstein, E. Tomelleri, et al. "Remote sensing of ecosystem light use efficiency with MODIS-based PRI." Biogeosciences 8, no. 1 (2011): 189–202. http://dx.doi.org/10.5194/bg-8-189-2011.
Full textAbstract:
Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.
5
Xie, Zhiying, Cenliang Zhao, Wenquan Zhu, Hui Zhang, and Yongshuo H. Fu. "A Radiation-Regulated Dynamic Maximum Light Use Efficiency for Improving Gross Primary Productivity Estimation." Remote Sensing 15, no. 5 (2023): 1176. http://dx.doi.org/10.3390/rs15051176.
Full textAbstract:
The light use efficiency (LUE) model has been widely used in regional and global terrestrial gross primary productivity (GPP) estimation due to its simple structure, few input parameters, and particular theoretical basis. As a key input parameter of the LUE model, the maximum LUE (Ɛmax) is crucial for the accurate estimation of GPP and to the interpretability of the LUE model. Currently, most studies have assumed Ɛmax as a universal constant or constants depending on vegetation type, which means that the spatiotemporal dynamics of Ɛmax were ignored, leading to obvious uncertainties in LUE-based GPP estimation. Using quality-screened daily data from the FLUXNET 2015 dataset, this paper proposed a photosynthetically active radiation (PAR)-regulated dynamic Ɛmax (PAR-Ɛmax, corresponding model named PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation. The PAR-LUE was compared with static Ɛmax-based (MODIS and EC-LUE) and spatial dynamics Ɛmax-based (D-VPM) models at 171 flux sites. Validation results showed that (1) R2 and RMSE between PAR-LUE GPP and observed GPP were 0.65 (0.44) and 2.55 (1.82) g C m−2 MJ−1 d−1 at the 8-day (annual) scale, respectively; (2) GPP estimation accuracy of PAR-LUE was higher than that of other LUE-based models (MODIS, EC-LUE, and D-VPM), specifically, R2 increased by 29.41%, 2.33%, and 12.82%, and RMSE decreased by 0.36, 0.14, and 0.34 g C m−2 MJ−1 d−1 at the annual scale; and (3) specifically, compared to the static Ɛmax-based model (MODIS and EC-LUE), PAR-LUE effectively relieved the underestimation of high GPP. Overall, the newly developed PAR-Ɛmax provided an estimation method utilizing a spatiotemporal dynamic Ɛmax, which effectively reduced the uncertainty of GPP estimation and provided a new option for the optimization of Ɛmax in the LUE model.
6
Wellington, Michael J., Petra Kuhnert, Luigi J. Renzullo, and Roger Lawes. "Modelling Within-Season Variation in Light Use Efficiency Enhances Productivity Estimates for Cropland." Remote Sensing 14, no. 6 (2022): 1495. http://dx.doi.org/10.3390/rs14061495.
Full textAbstract:
Gross Primary Productivity (GPP) for cropland is often estimated using a fixed value for maximum light use efficiency (LUEmax) which is reduced to light use efficiency (LUE) by environmental stress scalars. This may not reflect variation in LUE within a crop season, and environmental stress scalars developed for ecosystem scale modelling may not apply linearly to croplands. We predicted LUE on several vegetation indices, crop type, and agroclimatic predictors using supervised random forest regression with training data from flux towers. Using a fixed LUEmax and environmental stress scalars produced an overestimation of GPP with a root mean square error (RMSE) of 6.26 gC/m2/day, while using predicted LUE from random forest regression produced RMSEs of 0.099 and 0.404 gC/m2/day for models with and without crop type as a predictor, respectively. Prediction uncertainty was greater for the model without crop type. These results show that LUE varies between crop type, is dynamic within a crop season, and LUE models that reflect this are able to produce much more accurate estimates of GPP over cropland than using fixed LUEmax with stress scalars. Therefore, we suggest a paradigm shift from setting the LUE variable in cropland productivity models based on environmental stress to focusing more on the variation of LUE within a crop season.
7
Goerner, A., M. Reichstein, E. Tomelleri, et al. "Remote sensing of ecosystem light use efficiency with MODIS-based PRI – the DOs and DON'Ts." Biogeosciences Discussions 7, no. 5 (2010): 6935–69. http://dx.doi.org/10.5194/bgd-7-6935-2010.
Full textAbstract:
Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the efficiency with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependance on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.
8
Wang, S., Z. Li, Y. Zhang, D. Yang, and C. Ni. "LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 571–78. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-571-2020.
Full textAbstract:
Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.
9
RATJEN, A. M., and H. KAGE. "Nitrogen-limited light use efficiency in wheat crop simulators: comparing three model approaches." Journal of Agricultural Science 154, no. 6 (2015): 1090–101. http://dx.doi.org/10.1017/s0021859615001082.
Full textAbstract:
SUMMARYThree different explanatory indicators for reduced light use efficiency (LUE) under limited nitrogen (N) supply were evaluated. The indicators can be used to adapt dry matter production of crop simulators to N-limited growth conditions. The first indicator, nitrogen factor (NFAC), originates from the CERES-Wheat model and calculates the critical N concentration of the shoot as a function of phenological development. The second indicator, N nutrition index (NNI), calculates a critical N concentration as a function of shoot dry matter. The third indicator, specific leaf nitrogen (SLN) index (SLNI), has been newly developed. It compares the actual SLN with the maximum SLN (SLNmax). The latter is calculated as a function of the green area index (GAI). The comparison was based on growth curves and fitted to empirical data, and was carried out independently from a dynamic crop model. The data set included four growing seasons (2004–2006, 2012) in Northern Germany and seven modern bread wheat cultivars with varying N fertilization levels (0–320 kg N/ha). The influence of N shortage on LUE was evaluated from the beginning of stem elongation until flowering. With the exception of 2005, the highest productivity was observed for the highest N level. A moderate N shortage primarily reduced GAI and therefore light interception, while LUE remained stable under moderate N shortage. The relative LUE (rLUE) of a specific day was defined as the ratio of actual to maximal LUE. None of the indicators was proportional to rLUE, but the relationships were described well by quadratic plateau curves. The correlation between simulated and measured rLUE was significant for all explanatory indicators, but different in terms of mean absolute error and coefficient of determination (R2). The performance of SLNI and NNI was similar, but the goodness of prediction was much lower for NFAC. Compared with NNI and NFAC, SLNI corresponded to leaf N and was therefore sensitive to N translocation from leaves to growing grains during the reproductive stage. For this reason, SLNI may have the potential to improve simulation of dry matter production in wheat crop simulators.
10
Wang, H., I. C. Prentice, and J. Ni. "Primary production in forests and grasslands of China: contrasting environmental responses of light- and water-use efficiency models." Biogeosciences 9, no. 11 (2012): 4689–705. http://dx.doi.org/10.5194/bg-9-4689-2012.
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Abstract. An extensive data set on net primary production (NPP) in China's forests is analysed with the help of two simple theoretically derived models based on the light use efficiency (LUE) and water use efficiency (WUE) concepts, respectively. The two models describe the data equally well, but their implied responses to [CO2] and temperature differ substantially. These responses are illustrated by sensitivity tests in which [CO2] is kept constant or doubled, temperatures are kept constant or increased by 3.5 K, and precipitation is changed by ±10%. Precipitation changes elicit similar responses in both models. But NPP in South China, especially, is reduced by warming in the LUE model, whereas it is increased in the WUE model. The [CO2] response of the WUE model is much larger than that of the LUE model. It is argued that the two models provide upper and lower bounds for this response, with the LUE model more realistic for forests. The differences between the two models illustrate some potential causes of the large differences (even in sign) in the global NPP response of different global vegetation models to temperature and [CO2].