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Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions. [ABSTRACT FROM AUTHOR]

Vegetation production is an important variable in terrestrial ecosystems, playing crucial roles in sustaining carbon balance, reducing atmospheric CO2 concentration, and mitigating global climate change. Satellite-based models, which benefit from spatially and temporally continuous remote sensing observations of vegetation growth conditions, are widely used for quantifying regional and global vegetation production. Satellite-based vegetation production models were initially simple statistical models, but later, process-based light use efficiency models were developed. The latest models are based on the relationship between solar-induced chlorophyll fluorescence and vegetation production. An increasing number of satellite-based studies are being conducted by Chinese scientists, who are developing and implementing plant production models, particularly by self-developing a number of light consumption efficiency models and establishing a long-term worldwide dataset of vegetation production. Furthermore, Chinese scientists have investigated the spatial and temporal patterns of vegetation using diverse models, significantly improving our understanding of terrestrial functions and structures. However, current models and estimation techniques need further improvement, and Chinese scientists had the opportunity to improve model capability and our understanding of vegetation production patterns and their regulating elements. [ABSTRACT FROM AUTHOR]

Diffuse radiation (If) is one of important variables determining photosynthetic rate and carbon uptake of forest ecosystems. However, the responses of gross primary productivity (GPP) and light use efficiency (LUE) to diffuse fraction (DF) are still poorly understood. We used a 6-year dataset of carbon flux at a warmtemperate mixed plantation site in North China to explore the impacts of DF on GPP and LUE. During 2011-2017, ecosystem apparent quantum yield (α) and photosynthesis at photosynthetically active radiation (PAR) of 1800 µmol m-2 s -1 (P1800) on cloudy days were 63% and 17% higher than on clear days, respectively. Under lower vapor pressure deficit (VPD) and air temperature (Ta) conditions, canopy photosynthesis was significantly higher on cloudy skies than on clear skies. On half-hourly scale, increased DF enhanced α and P1800. Daily GPP peaked at a median DF (=0.5), while daily LUE significantly increased with DF (p<0.01). Both GPP and LUE were mainly controlled directly by DF and PAR. DF had an indirect effect on LUE and GPP mainly through PAR. At high DF levels (>0.5), the increase in LUE did not make GPP enhancement. The direct effect of DF on GPP and LUE under lower Ta and VPD was more sensitive than under higher Ta and VPD. When DF was incorporated into the MichaelisMenten model, it performed well in the GPP estimation, and the determination coefficient increased by 32.61% and the root mean square error decreased by 25.74%. These findings highlight the importance of incorporating DF into carbon sequestration estimation in North China. [ABSTRACT FROM AUTHOR]

Both light use efficiency (LUE) and water use efficiency (WUE) play essential roles in ecosystem production. The extent to which ecosystem production is affected by the coupling between LUE and WUE remains unclear. In this study, we used data from flux measurements and weather stations in the Heihe River Basin, China, along a strong climatic gradient to quantify the relationship between LUE and WUE. Variations of LUE explained up to 85% of the variations of WUE. The contribution of LUE to WUE increased with increasing water stress. Pearson's correlation coefficient between LUE and WUE increased from −0.12 to +0.63 with decreasing LUE. The coordination between LUE and WUE was essential to explaining the insensitive of WUE and GPP with increasing water stress. These results suggest that ecosystems enhance the coordination between light and water use when water stress is high. They enhance our understanding of the drought resilience of ecosystems and reduce uncertainties associated with the carbon cycle in drylands. [ABSTRACT FROM AUTHOR]

Carbon sinks in terrestrial ecosystems can be significantly increased by afforestation, which will slow global warming. However, it is still unclear how different plantations influence the carbon sink and how they respond to environmental factors, especially in drylands. In this study, eddy correlation method (EC) was used to measure carbon and water fluxes and environmental factors of two artificial forests (Larix principis-rupprechtii and Pinus tabulaeformis) in the dryland of Northwest China, and the responses of evapotranspiration (ET), net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO) to environmental factors were also assessed. Results showed that the L. principis-rupprechtii forest ecosystem had higher water use efficiency (WUE), light use efficiency (LUE), GPP, and RECO than the P. tabulaeformis forest ecosystem. However, the proportion of net ecosystem production (NEP) to GPP in the P. tabulaeformis forest ecosystem (62.89%) was higher than that in the L. principis-rupprechtii forest ecosystem (47.49%), indicating that the P. tabulaeformis forest ecosystem had the higher carbon sequestration efficiency. In addition, the CO2 and H2O fluxes in the L. principis-rupprechtii forest ecosystem were more sensitive to environmental factors, compared with the P. tabulaeformis forest ecosystem. Further, the RECO of the L. principis-rupprechtii forest ecosystem was more sensitive to temperature changes, which implies that the L. principis-rupprechtii forest ecosystem will release more CO2 than the P. tabulaeformis forest ecosystem with a warming climate. Therefore, the P. tabulaeformis forest ecosystem may have better carbon sequestration potential. These results are important for understanding the effects of climate change on the CO2 and H2O cycles in coniferous plantation ecosystems in drylands. [ABSTRACT FROM AUTHOR]

Earth observation‐based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem‐level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field‐observed GPP, net primary productivity and solar‐induced fluorescence was better or equally well captured by our LRF‐based GPP when compared with six state‐of‐the‐art Earth observation‐based GPP products. Over the period 1982–2015, the LRF‐based average annual global terrestrial GPP budget was 121.8 ± 3.5 Pg C, with a detrended inter‐annual variability of 0.74 ± 0.13 Pg C. The strongest inter‐annual variability was observed in semi‐arid regions, but croplands in China and India also showed strong inter‐annual variations. The trend in global terrestrial GPP during 1982–2015 was 0.27 ± 0.02 Pg C year−1, and was generally larger in the northern than the southern hemisphere. Most positive GPP trends were seen in areas with croplands whereas negative trends were observed for large non‐cropped parts of the tropics. Trends were strong during the eighties and nineties but levelled off around year 2000. Other GPP products either showed no trends or continuous increase throughout the study period. This study benchmarks a first global Earth observation‐based model using an asymptotic light response function, improving simulations of GPP, and reveals a stagnation in the global GPP after the year 2000. [ABSTRACT FROM AUTHOR]

Diffuse radiation produces a higher light use efficiency (LUE) than does direct radiation. However, light components and their impacts on interannual variations of photosynthesis in China have not been intensively studied. We estimated gross primary production (GPP) from a two‐leaf LUE model that incorporates a diffuse radiation effect and compared it with a big‐leaf LUE model that ignores the diffuse radiation impact. The results show that a dimming occurred in China—the estimated annual photosynthetically active radiation (PAR) declined by −1.79 MJ m−2 yr−1. However, the diffuse fraction and diffuse PAR increased over this interval. Both GPP models simulated an increasing trend of GPP in China from 2000 to 2017. The big‐leaf GPP model underestimated the increasing trend of GPP from the two‐leaf GPP model by 6.4%. The diffuse fraction change during the dimming exerted an embedded positive impact relative to the big‐leaf GPP trend in China. To evaluate the impact of total PAR versus its components on GPP, annual anomaly percentage (Ap) of GPP shows that the big‐leaf GPP model had a positive correlation between Ap of GPP and PAR across China. Considering the diffuse radiation impact, Ap of two‐leaf GPP was negatively correlated with PAR in southern China, which had a higher leaf area index (2.02) and a greater diffuse fraction (0.63). Strong diffuse radiation effects in southern China produced GPP anomalies in opposition to the direction of the PAR changes. These findings highlight the distinctive impact of light components on interannual variations of plant photosynthesis. Key Points: A two‐leaf GPP model predicts that diffuse radiation effects offset the impact of dimming on photosynthesis over 2000–2017 in ChinaDue to strong diffuse radiation impact, the annual anomaly of modeled two‐leaf GPP shows a negative correlation with PAR in southern ChinaA big‐leaf GPP model underestimates the increasing trend attributed to the dimming in China [ABSTRACT FROM AUTHOR]

• Diffuse fraction (k d) was important factor for affecting light use efficiency (LUE). • LUE increased linearly with increasing k d , with a slope of about 0.326 g C mol−1. • Gross primary production was maximized when k d was about 0.53. • The Boland model was the best for modeling of daily k d in the North China Plain. The increase of diffuse radiation fraction (k d) has been reported to significantly impact light use efficiency (LUE) and carbon uptake in terrestrial ecosystems. The impact of k d on LUE should be considered in crop models to accurately evaluate the effect of radiation changes on crop production. However, the magnitude of the k d effect is difficult to quantify because of the complicated interacting relationships among all of the meteorological parameters, as well as the changing effects for various ecosystem types and research sites. Eight site-years of flux data and two years of diffuse radiation data from two field ecosystems in the North China Plain were used to (1) compare the performance of five k d models, (2) explore the impacts of environmental factors on LUE and gross primary production (GPP) of winter wheat (Triticum aestivum L.), and (3) quantify the relationships between k d and both LUE and GPP of winter wheat. Comparison results showed that the k d model developed by Boland et al. performed the best of the five models evaluated. This model was chosen to calculate k d in this research. Path analysis show that k d was the main factor affecting LUE of winter wheat, explaining up to 55% of the variability in LUE. The relationship between k d and LUE was significantly linear (slope of about 0.326 g C mol−1). GPP initially increased and then decreased with increasing k d. A moderate radiation condition (k d = 0.53) was favorable for increasing GPP. The effect of k d on LUE should be added in the LUE module of APSIM-Nwheat to improve simulation accuracy. The results of this study highlight the importance of k d in correctly modeling LUE for winter wheat with a crop model and provide quantitative relationships between these two parameters. These relationships will be helpful in improving crop model simulation accuracy under changed climate conditions. [ABSTRACT FROM AUTHOR]

The yield and quality of Arabica coffee ( Coffea arabica ) cannot be guaranteed due to irrational irrigation and light management in dry and hot regions of southwest China. The objective of this study was to obtain rational irrigation and shading mode for efficient light and water use, suitable yield and high nutritional quality of Arabica coffee. Taking full irrigation (FI) as the control, the effects of deficit irrigation (DI) (DI75 and DI50, 75 and 50% of full irrigation amount, respectively) on photosynthesis, yield, nutritional quality and water use efficiency (WUE) were investigated under four shading levels (Sh0 no shading, Sh30, Sh45 and Sh60, 30, 45 and 60% shading, respectively) using the field experiments, and a comprehensive benefit assessment model of yield, nutritional quality and WUE was established under different irrigation and shading treatments. Results indicated that DI75 increased leaf apparent radiation use efficiency and the contents of crude fat and chlorogenic acid in dry bean if compared to FI. In comparison with Sh0, Sh30 enhanced dry bean yield, WUE and the contents of total sugar and chlorogenic acid in dry bean. Compared with FISh0 (CK), DI75Sh30 raised dry bean yield, WUE, and the contents of total sugar, protein, crude fat and chlorogenic acid, but reduced the caffeine content. Principal component analysis showed that DI75Sh30 had optimal comprehensive nutritional quality, and TOPSIS method indicated that DI75Sh30 had the optimal comprehensive benefit, showing that our results can provide scientific basis for rational irrigation and light management of Arabica coffee in dry and hot regions. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) is one of the important parameters on calculating terrestrial gross primary productivity (GPP) and net primary productivity (NPP). Based on 5-year (2006-2010) carbon flux and climatic variable data of a mixed plantation in north China, the seasonal and interannual variation of LUE was investigated and the biophysical controls were examined. Our results show that LUE had a distinct seasonal course, and peaked in the vigorous growing season with a value of 0.92-1.27 g C MJ. During the period of 2006-2010, annual mean LUE ranged between 0.54 and 0.62 g C MJ, and it was linearly correlated with annual GPP. In the growing season, LUE was significantly linked with the water availability variables (including monthly mean vapor pressure deficit (VPD), precipitation, evaporative fraction (EF), and the ratio of precipitation to evapotranspiration (P/ET)) and canopy conductance ( g ). However, EF was a better estimator of LUE compared with other biophysical variables. LUE decreased with an increase of the clearness index ( CI), indicating that LUE was higher under cloudy sky conditions than that under sunny sky conditions in the mixed plantation. [ABSTRACT FROM AUTHOR]

• We examined environmental controls on ecosystem RUEs in a young, urban plantation. • Upward trends in RUEs corresponded with increases in NDVI. • Duration and timing of drought played a significant role in regulating RUEs. • Ample mid-summer rainfall lessened the impact early, spring drought had on RUEs. Understanding the long-term trends and interannual variability in ecosystem resource use efficiencies (RUEs) and their controlling factors is central to the prediction of ecosystem response to climate change. However, knowledge in this area, particularly as it pertains to young urban plantations, remains largely absent. In this study, we used continuous eddy covariance and site-specific climatic data collected from a 12-year-old mixedwood plantation in Beijing, China, to assess (i) long-term trends, (ii) interannual variability, and (iii) key controlling factors of RUEs with respect to plantation use of carbon (i.e., CUE), water (WUE) and light (LUE). On average, annual CUE and WUE over the 2012–2020 growing seasons increased by about 5.2 and 7.6%. Trends in RUEs corresponded with increases in normalized difference vegetation index (NDVI) when the plantation transitioned from a carbon source to a sink. Analysis of controlling factors indicated that RUEs were regulated by NDVI and other environmental factors at varying degrees dependent on season. Interannual variability in (i) CUE was largely controlled by spring soil temperature, (ii) WUE by summer water vapor pressure deficit and spring soil water content (SWC), and (iii) LUE by photosynthetically active radiation, summer SWC and spring air temperature. Relative seasonal changes in paired RUEs were positively correlated, with soil water availability being the most limiting. Long-term summer drought was shown to evoke severe and persistent decline in same-year RUEs. Ample mid-summer rainfall lessened the impact of early spring drought on plantation performance. Our results emphasized the need to account for the combined effects of climatic controls and canopy-physiological characteristics in the management of young plantations. [ABSTRACT FROM AUTHOR]

An accurate estimation of carbon fluxes is very important in carbon cycle studies. A remote sensing based gross primary production (GPP) and net ecosystem production (NEP) algorithm, RS-CFLUX, was presented in this work. The algorithm was calibrated with Markov Chain Monte Carlo (MCMC) method at Daman superstation and Zhangye wetland station in the midstream of the Heihe River Basin. Results indicated that both of the stations present high GPP (1442.04 g C/m2/year at Daman superstation and 928.89 g C/m2/year at Zhangye wetland station) and NEP (409.38 g C/m2/year at Daman superstation and 422.60 g C/m2/year at Zhangye wetland station). The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of carbon fluxes at both stations, using photosynthetically active radiation (PAR), land surface temperature (LST), normalized difference water index (NDWI) and enhanced vegetation index (EVI) as input. RS-CFLUX model were sensitive to maximum light use efficiency, respiration at reference temperature, activation energy parameter of respiration. [ABSTRACT FROM AUTHOR]

Estimates of regional net primary productivity (NPP) are very useful in modeling regional and global carbon cycles. This work proposed a new method to study NPP characteristics and changes in the Inner Mongolia Autonomous Region, China. To estimate NPP accurately, we integrated photosynthetically active radiation (PAR) with a light use efficiency model, derived from Moderate Resolution Imaging Spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values simulated by the model matched observed data well. Annual NPP in the study area was about 0.25PgCa−1 from 2003 to 2008. In spatial distribution, NPP decreased from northeast to southwest in the Inner Mongolia Autonomous Region. NPP from May to September accounted for 84.2% of annual NPP, while that from July to August accounted for 44.3%. NPP was significantly correlated to both precipitation and temperature at monthly temporal scales. NPP also changed with the fraction of absorbed PAR. [Copyright &y& Elsevier]

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ-1, 0.450 gC·MJ-1 and 0.126 gC·MJ-1 for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ-1 for the hygrophilous grasslands, and 0.125 gC·MJ-1 for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R2 of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m-2·a-1. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations. [ABSTRACT FROM AUTHOR]

Abstract: The accurate estimation of Gross Primary Production (GPP) is an important issue in carbon cycle studies. In this study, GPP dynamic of two typical vegetation in Heihe River Basin (alpine meadow in the upper stream and maize in middle stream) were estimated using Eddy Covariance (EC) and Vegetation Photosynthesis Model (VPM). Yearly GPP observed by EC at alpine meadow is 853gC/m2/yr, which is only half of GPP of the value 1567gC/m2/yr at maize field. This is mainly attributed to irrigation, fertilization and high light use efficiency at maize field. More than 80% carbon fixed by photosynthesis is release to atmosphere through ecosystem respiration at alpine meadow and at maize field 60%. The maximum light use efficiency of maize is 2.66gC/MJ ARAR, while it is 1.6gC/MJ ARAR at alpine meadow. VPM can correctly simulate seasonal dynamic and magnitude of GPP at both of the two sites. GPP values predicted by VPM were 872gC/m2/yr at alpine meadow site and 1246gC/m2/yr at maize site. The determination coefficients were as large as 0.9 at the alpine meadow site and 0.93 at the maize site. Average GPP value at each season predicted by VPM is very close to that observed by EC. [Copyright &y& Elsevier]

Abstract: As a weather element, clouds can affect CO2 exchange between terrestrial ecosystems and the atmosphere by altering environmental conditions, such as solar radiation received on the ground surface, temperature, and moisture. Based on the flux data measured at five typical ecosystems of China during mid-growing season (June–August) from 2003 to 2006, we analyzed the responses of net ecosystem exchange of carbon dioxide (NEE), light use efficiency (LUE, defined as Gross ecosystem photosynthesis (GEP)/Photosynthetically active radiation (PAR)), and water use efficiency (WUE, defined as GEP/Evapotranspiration (ET)) to the changes in cloudiness. The five ecological sites included Changbaishan temperate mixed forest (CBS), Dinghushan subtropical evergreen broad-leaved forest (DHS), Xishuangbanna tropical rainforest (XSBN), Inner Mongolia semi-arid Leymus chinensis steppe (NMG), and Haibei alpine frigid Potentilla fruticosa shrub (HB). Our analyses show that cloudy sky conditions with cloud index (k t) values between 0.4 and 0.6 increased NEE, LUE, and WUE of the ecosystems at CBS, DHS, NMG and HB from June to August. The LUE of tropical rainforest at XSBN was higher under cloudy than under clear sky conditions, but NEE and WUE did not decrease significantly under clear sky conditions from June to August. The increase in GEP with increasing diffuse radiation received by ecosystems under cloudy skies was the main reason that caused the increases in LUE and net carbon uptake in forest ecosystem at CBS, DHS, and alpine shrub ecosystem at HB, compared with clear skies. Moreover, for the ecosystem at CBS, DHS, and HB, when sky condition became from clear to cloudy, GEP increased and ET decreased with decreasing VPD, leading to the increase in WUE and NEE under cloudy sky conditions. The decrease in R e with decreasing temperature and increase in GEP with decreasing VPD under cloudy skies led to the increase in LUE, WUE, and net carbon uptake of semi-arid steppe at NMG, compared to clear skies. These different responses among the five ecosystems are attributable to the differences in canopy characteristics and water conditions. From June to August, the peaks of the k t frequency distribution in temperate ecosystems (e.g., CBS, NMG, and HB) were larger than 0.5, but they were smaller than 0.4 in subtropical/tropical forest ecosystems (e.g., DHS and XSBN). These results suggest that the pattern of cloudiness during the years from 2003 to 2006 in the five ecosystems was not the best condition for their net carbon uptake. This study highlights the importance of cloudiness factor in the prediction of net carbon absorption in the Asia monsoon region under climate change. [Copyright &y& Elsevier]

Abstract: Measurements from individual CO2 eddy flux sites provide valuable information on the seasonal dynamics of gross primary production (GPP). In this study, we estimated seasonal dynamics of GPP from 3 years (2004–2006) of the eddy covariance observations at maize cropland and degraded grassland in a semi-arid area of Tongyu county (44.5667°N, 122.8833°E), Northeast China. The biophysical performance of vegetation indices (EVI, NDVI, and LSWI) derived from the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product and their relations to GPP dynamics were evaluated. The quantitative relationships between the vegetation indices and CO2 flux data clearly demonstrated the improvement of EVI over NDVI, in terms of the phase and magnitude of photosynthesis. Canopy-level maximum light use efficiency, ɛ 0, was estimated for both maize and grassland by using the observed CO2 flux data and Photosynthetically Active Radiation (PAR) data from eddy flux tower sites. For maize cropland, the ɛ 0 value was 0.56gC/mol PAR, and for degraded grassland, the ɛ 0 value was 0.37gC/mol PAR. We conducted a simulation of the Vegetation Photosynthesis Model (VPM) using the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI) derived from the 8-day (MODIS) surface reflectance product, as well as site-specific climate data. The comparison between simulated GPP and estimated GPP from tower CO2 flux data showed good agreement in both maize cropland and degraded grassland. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the satellite-driven VPM model for scaling-up of GPP of maize cropland and grassland in semi-arid ecosystems. [Copyright &y& Elsevier]

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale. [ABSTRACT FROM AUTHOR]

The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO2 between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO2 eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO2 flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai–Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai–Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales. [Copyright &y& Elsevier]

Poplars are the major tree component of traditional agroforestry systems throughout the south temperate central area of China that includes all or portions of Jiangsu, Anhui, Zhejiang, Hubei, Henan, Shandong, and Shanxi provinces, an area of ~600,000 km2. The lack of experidmental data on the effect of various interplanting systems on crop production represents a serious gap in our knowledge, and consequently, a stable, optimized poplar-crop interplanting pattern is difficult to achieve. In order to develop a poplar-crop interplanting pattern that is economically viable, environmentally sound, technically workable, and socially compatible in floodplain areas, new poplar-crop interplanting patterns were designed using the principle of edge effects. Six patterns were designed and established in 1992 with different narrow - wide spacings, i.e., I: (3 × 3) × 20 m, II: (3 × 3) × 30 m, III: (3 × 3) × 40 m, IV: (4 × 4) . 20 m, V: (4 × 4) × 30 m, and VI: (4 × 4) × 40 m. A randomized block arrangement was used with two replications for each pattern. Based on 7 years of investigation, this paper examines the effects of these patterns on temporal and spatial variations in microclimate at various phenological phases of winter wheat, variations in wheat yield and quality, wood production, biomass productivity and light use efficiency, and economic assessment. The feasibility and viability of these new patterns is also discussed. These preliminary results provide some basic principles for developing optimized poplar-crop interplanting patterns in the wetland plain areas of China. [ABSTRACT FROM AUTHOR]

We examined the differences of maximum light use efficiency (LUE max ), most effective light intensity (PAR e ), light compensation point (LCP) and light saturation point (LSP) in diffe-rent vertical positions and different phenological periods of tree crown in Larix principis-rupprechtii plantation in Saihanba Mechanical Forest Farm of Hebei Province. We analyzed the main influencing factors for all the variables. The results showed that LUE max increased with the increasing crown depth, and that PAR e , LCP and LSP decreased with the increasing crown depth. Such a result indicated that upper crown had higher utilization efficiency to strong light and that the lower crown was more efficient to adapt to weak light environment. During the leaf development in the growth period, the LUE max approximately increased except in July, while the changes of other photosynthe-tic-light factors showed a tendency of unimodal curve. Environmental factors were significantly correlated with the indices of light response, mainly due to the stress reaction of leaf stomata to surrounding environment. Understanding the spatial and seasonal changes of the photosynthetic-light indicators was important for formulating scientific management measures, optimizing stand structure, improving local microenvironment and maximizing forest productivity.

The maximum rate of carboxylation in leaves (V cmax ) is an important parameter used to determine the photosynthetic rate. An accurate estimation of V cmax is important for evaluating the photosynthetic capacity of vegetation productivity and the carbon budget of forest ecosystems. In this study, we measured the light use efficiency (LUE) and V cmax of leaves and their corresponding spectral characteristics. Spectral analysis was used to find a modified photochemical reflectance index (PRI) of leaves to estimate LUE and thereby estimate leaf V cmax . The results showed that the precision with which the modified ratio PRI index estimated LUE was significantly increased. The R 2 of the estimation model was 0.69, and the root mean square error (RMSE) was 0.0024. The R 2 of the model for estimating leaf V cmax based on the LUE was 0.72, and the RMSE was 8.439 μmol/m 2 /s. The V cmax values of conifer trees (Scots pine, Korean pine, and larch) were higher than those of broadleaf trees (white birch, Manchurian walnut, Siberian elm, Manchurian ash, Mongolian oak, Korean aspen, and Amur linden). This method avoids problem that conventional PRI is not sensitive to high leaf LUE values and provides important parameter data for the quantitative estimation of the gross primary productivity of forest ecosystems.

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP VPM ) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP VPM is also significantly positive correlated with GOME-2 SIF (R 2  > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models., (Copyright © 2018 Elsevier B.V. All rights reserved.)