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Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAI su ) can separate a portion of LAI su even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAI su to improve GPP estimation, which considers the comprehensive contribution of LAI su when the canopy does and does not receive direct radiation. In particular, the new model decreases LAI su to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m −2 d −1 and 1.53 g C m −2 d −1 to 1.36 g C m −2 d −1 and increasing the coefficient of determination ( R 2 ) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m −2 month −1 and28.11 g C m −2 month −1 to 25.81 g C m −2 month −1 at a monthly scale and from 231.82 g C m −2 yr −1 and 221.60 g C m −2 yr −1 –200.00 g C m −2 yr −1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m −2 d −1 and 0.46 g C m −2 d −1 , respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

Light‐use efficiency (LUE) is at the core of mechanistic modeling of global gross primary production (GPP). However, most LUE estimates in global models are satellite based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower‐based LUE from 237 FLUXNET towers, and scaled up LUEs from in situ tower level to global biome level. We integrated the tower‐based LUE estimates with key environmental and biological variables at 0.5° × 0.5° grid‐cell resolutions, using a random forest regression (RFR) approach. Then, we developed a RFR‐LUE‐GPP model using the grid‐cell LUE data. In order to calibrate the LUE model, we developed a data‐driven RFR‐GPP model using RFR method only. Our results showed LUE varies largely with latitude. We estimated a global area‐weighted average of LUE at 1.23 ± 0.03 g C·m−2·MJ−1 APAR, which led to an estimate of global GPP of 107.5 ± 2.5 Gt C/yr from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e., 20° S–40° S and 5° N–40° N) due to the lack of available data. Model results were improved by incorporating Köppen climate types to represent climate/meteorological information in machine‐learning modeling. This brought a new understanding to the recognized problem of climate dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broadleaf forests (EBF). The divergent responses of GPP to temperature and precipitation at middle to high latitudes and at middle to low latitudes echo the necessity of modeling GPP separately by latitudes.

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.

Science-based indicators are needed to monitor ecosystems at different scales, especially with increasing climate change and anthropogenic pressures, and varying impacts according to the scale of analysis. Ecosystem functional properties report on key ecosystem processes and dynamics at multiple scales, from community to biome level, providing a dynamic view of ecosystem carbon- and energy-related processes, useful for monitoring short-term changes. These quantities can be derived from flux measurements, such as those collected by the Integrated Carbon Observation System flux tower network. Here, modeling of selected ecosystem functional properties with hyperspectral satellite data was carried out at fifteen European sites belonging to five different plant functional types. The results, compared to those obtained using a dense Sentinel-2 time series, highlight the potential of hyperspectral narrowbands. Gross primary productivity, light use efficiency and net ecosystem exchange have been predicted with reasonable accuracy, independently by the plant functional type or site latitude . Two different modeling approaches have been compared: Random Forests and Extreme Gradient Boosting. The potential of monitoring ecosystems using the EFPs approach is discussed in consideration of the pros and cons in data use, and the future increased availability of hyperspectral missions.

Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C∙yr−1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

This study explores the cultural significance of the Phaya Luang, a mythical creature worshipped by the Tai Lue people in Thailand, aiming to harness these beliefs to innovate and market new cultural products for heritage tourism. Employing qualitative methods, including document analysis, in-depth interviews, focus groups, and workshops, this research investigates the ancestral transmission of Phaya Luang beliefs through oral literature. It reveals the potential for these beliefs to inspire the creation of culturally relevant products, specifically satchel bags and yarn lanterns adorned with Phaya Luang motifs. These products cater to the community’s needs, attract tourists and appeal to younger generations through contemporary design. The integration of traditional beliefs preserves cultural heritage and imbues the items with new meanings, particularly the promise of prosperity associated with Phaya Luang. This strategic use of traditional motifs in product development offers insights into effective heritage marketing, significantly enhancing both the cultural and economic value of community products and presenting a successful model for the revitalisation of traditional beliefs in contemporary tourism and product design.

Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAIG) measurements were used to revise the key inputs, the maximum light use efficiency (εmax) and the fractional photosynthetically active radiation (FPARM) in the MOD17 algorithm. Using two approaches to determine the site-specific εmax value, we suggested that the suitable εmax was about 1.61 g C MJ−1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ−1 for grassland. The FPARM underestimated 22.2% of the actual FPAR (FPARG) simulated from the LAIG during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the εmax and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the εmax and FPARG, are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow.

As a desert shrub, Haloxylon ammodendron combines ecological, economic, and social benefits and plays an important role in the ecological conservation of arid desert areas. Understanding its physiological characteristics and its mechanism of light energy utilization is important for the conservation and utilization of H. ammodendron . Therefore, we selected five stands (5-, 11-, 22-, 34-, and 46-year-old) of H. ammodendron as research objects in the study and measured their photosynthetic light response curves by a portable open photosynthesis system (Li-6400) with a red-blue light source (6400-02B). Then, we measured the leaf chlorophyll parameters in the laboratory, calculated the photosynthetic characteristics by using Ye Zipiao’s photosynthetic model, analyzed their variation patterns across stand ages, and explored the relationships between leaf chlorophyll parameters and photosynthetic characteristics. The results showed that leaf chlorophyll parameters and photosynthetic characteristics of H. ammodendron at different stand ages were significantly different. Chl content, P nmax , and LUE max of H. ammodendron were V-shaped with the increase of stand age. The 5-year-old H. ammodendron was in the rapid growth period, synthesized more Chl a+b content (8.47 mg g −1 ) only by using a narrower range of light, and the P nmax and LUE max were the highest with values of 36.21 μmol m −2 s −1 and 0.0344, respectively. For the 22-year-old H. ammodendron , due to environmental stress, the values of Chl a+b content, P nmax , and LUE max were the smallest and were 2.64 mg g −1 , 25.73 μmol m −2 s −1 , and 0.0264, respectively. For the older H. ammodendron , its Chl content, P nmax , and LUE max were not significantly different and tended to stabilize but were slightly higher than those of the middle-aged H. ammodendron . On the other hand, the other photosynthetic parameters did not show significant variation patterns with stand age, such as R d , AQE, LSP, LCP, and I L-sat . In addition, we found that the relationships between Chl a+b content and P nmax and between Chl a+b content and LUE max were highly correlated, except for the older H. ammodendron . Thus, using leaf chlorophyll content as a proxy for photosynthetic capacity and light use efficiency should be considered with caution. This work will provide a scientific reference for the sustainable management of desert ecosystems and vegetation restoration in sandy areas.

The duration of light exposure each day, termed the photoperiod, is a crucial environmental cue that influence several aspects of plant physiology, including growth, development, and metabolic activity. Adjusting the photoperiod in controlled agriculture systems has the potential to improve crop yield and nutritional content. However, the benefits of longer photoperiods compared to higher light intensities under a fixed daily light integral (DLI) have not been thoroughly examined for many leafy vegetables. DLI is the total amount of light a plant receives per day and it is the product of photoperiod and light intensity. This study aimed to determine to what extent the effect of DLI on pak choi ( subsp. ) growth, yield and quality depends on whether DLI is changed by light intensity (PPFD) or by photoperiod. Three cultivars ('Hybrid Special', 'Red Summer', and 'Shanghai Green') were grown under four different DLIs (10.8, 13.5, 16.2, and 18.9 mol m d ). These DLIs were achieved either by varying the photoperiod (12, 15, 18 and 21 hours) at a constant PPFD 250 µmol m s or by varying the PPFD (167, 208, 250, and 292 µmol m s ) at a constant photoperiod (18 hours). Increasing DLI by extending the photoperiod resulted in more growth than increasing DLI by increasing PPFD. Photoperiod extension also generally resulted in higher light use efficiency and energy use efficiency than increasing DLI by increasing PPFD. The content of vitamin C, glucosinolates and many other metabolites increased significantly with higher DLI regardless whether DLI was increased through PPFD or photoperiod. However, DLI did not affect shelf life and overall visual quality. These results suggest that extending photoperiod is a more effective strategy than increasing light intensity for optimizing leafy vegetable production in controlled environments.

Vertical farming systems (VFs) offer high production efficiency in controlled environments (CEA), but their energy requirement and associated carbon footprint are strongly constrained by the high energy demand of artificial lighting is strongly constrained by the energy demand of artificial lighting. This study assessed whether different combinations of photoperiod and photosynthetic photon flux density (PPFD; 16 L:8 D at 250 µmol m⁻² s⁻¹, 12 L:12 D at 340 µmol m⁻² s⁻¹, and continuous 24 L:0 D at 170 µmol m⁻² s⁻¹) affect growth, physiology, and energy performance of two crisphead lettuce cultivars [( Lactuca sativa L. var. crispa - ‘Falstaff’ (green) and ‘Copacabana’ (red)] when the daily light integral (DLI) is maintained constant (14.4 mol m⁻² day⁻¹). Yield, morphological traits, chlorophyll fluorescence, and gas exchange parameters did not differ among lighting treatments, indicating comparable photosynthetic functioning under all photoperiod–PPFD combinations. However, continuous lighting (24 L:0 D) improved energy use efficiency (EUE) and light use efficiency (LUE), while reducing lighting costs per unit of produced biomass and demonstrating a clear benefit in terms of resource utilization. Cultivar-related differences were more pronounced than treatment effects, with red lettuce showing higher levels of phenolic compounds, carotenoids, anthocyanins, and antioxidant capacity, while maintaining similar morphological responses. Overall, the results show that under a constant DLI, photoperiod manipulation obtained by adjusting PPFD has a limited impact on plant physiology but can substantially influence yield and energy efficiency. Continuous moderate-intensity lighting thus emerges as an effective strategy to enhance the economic and environmental sustainability of VFs without compromising crop performance.