1,417 research outputs found

    Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites:TL-LUE Parameterization and Validation

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    Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR

    Shedding Light on Photosynthesis: The Impacts of Atmospheric Conditions and Plant Canopy Structure on Ecosystem Carbon Uptake.

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    The Earth’s climate is influenced by complex interactions of physical, chemical, and biological processes that link terrestrial ecosystems and the atmosphere. One of these interactions involves the use of light in photosynthesis, which allows plants to remove CO2 from the atmosphere and slow the unprecedented rate of climate change the Earth is experiencing. However, modeling future climate remains challenging, in part because of limited knowledge of mechanisms controlling the effects of light on gross ecosystem CO2 uptake (conceptually, photosynthetic activity integrated across all leaves in a plant canopy). Unlike previous studies, this dissertation uses data from atmospheric science, ecosystem ecology, and plant physiology to provide evidence for mechanistic links between physical, biophysical, and ecological controls on the effects of light on processes tied to gross ecosystem CO2 uptake—specifically, ecosystem gross primary production (GPP) and leaf photosynthesis. First, this dissertation empirically demonstrates that the dominant effect of clouds is to reduce total light above canopies. However, optically thin clouds increase scattered, diffuse light, which canopies use more efficiently than they use direct light. This offsets reductions in total light and results in no net change in GPP under thin clouds, while GPP decreases under optically thick clouds because both diffuse and direct light decrease. Second, ground-based measurements indicate that the rate of increase in GPP with diffuse light changes throughout the day. The magnitude of increase depends on how canopies interact with the angle of incoming light to biophysically alter the distribution of light within canopies and thus, the proportions of leaves contributing to GPP. Third, the distribution of species and light within one forest canopy leads to differences in some of the rate-limiting biochemical reactions in leaf photosynthesis. These field-based data indicate which assumptions representing canopies in Earth system models may not have support in situ, and could be contributing to errors in model estimates of future climate. Overall, this dissertation identifies mechanisms through which clouds and plant canopy structure alter land-atmosphere CO2 fluxes and subsequently, Earth’s climate. It also provides an important interdisciplinary framework for testing assumptions about the feedbacks that living organisms form with their environment.PhDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133446/1/chengs_1.pd

    Modeling Gross Primary Production of Midwest Maize and Soybean Croplands with Satellite and Gridded Weather Data

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    The gross primary production (GPP) metric is useful in determining trends in the terrestrial carbon cycle. Models that determine GPP utilizing the light use efficiency (LUE) approach in conjunction with biophysical parameters that account for local weather conditions and crop specific factors are beneficial in that they combine the accuracy of the biophysical model with the versatility of the LUE model. One such model developed using in situ data was adapted to operate with remote sensing derived leaf area index (LAI) data and gridded weather datasets. The model, known as the Light Use Efficiency GPP Model (EGM), uses a four scalar approach to account for biophysical parameters including temperature, water stress, light quality, and phenology. The model was calibrated for four locations (seven fields) in the northern Midwest and was driven using remotely sensed LAI data and gridded weather data for these locations. Results showed reasonable error estimates (RMSE = 3.5 g C m-2 d-1). However, poor gridded weather atmospheric pressure and incoming solar radiation inputs, increased climatic variation in the study sites and contributed to higher RMSE that observed when the model was applied exclusively to in situ data from the Nebraska sites (2.6 g C m- 2 d- 1). Additionally, the application of LAI algorithms calibrated using solely Nebraska sites to sites in Iowa, Minnesota, and Illinois without verification of their accuracy potentially lead to increased error. Despite this, the study showed there is good correlation between measured and modeled GPP using this model for the field years under study. As the ultimate objective of research is to develop regional estimates of GPP, the decrease in model accuracy is somewhat offset by the model’s ability to function with gridded weather datasets and remotely sensed biophysical data. Advisor: Elizabeth A. Walter-She

    Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012

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    Terrestrial ecosystems have continued to provide the critical service of slowing the atmospheric CO2 growth rate. Terrestrial net primary productivity (NPP) is thought to be a major contributing factor to this trend. Yet our ability to estimate NPP at the regional scale remains limited due to large uncertainties in the response of NPP to multiple interacting climate factors and uncertainties in the driver data sets needed to estimate NPP. In this study, we introduced an improved NPP algorithm that used local driver data sets and parameters in China. We found that bias decreased by 30% for gross primary production (GPP) and 17% for NPP compared with the widely used global GPP and NPP products, respectively. From 2000 to 2012, a pixel-level analysis of our improved NPP for the region of China showed an overall decreasing NPP trend of 4.65 Tg C a−1. Reductions in NPP were largest for the southern forests of China (−5.38 Tg C a−1), whereas minor increases in NPP were found for North China (0.65 Tg C a−1). Surprisingly, reductions in NPP were largely due to decreases in solar radiation (82%), rather than the more commonly expected effects of drought (18%). This was because for southern China, the interannual variability of NPP was more sensitive to solar radiation (R2 in 0.29–0.59) relative to precipitation (R2 \u3c 0.13). These findings update our previous knowledge of carbon uptake responses to climate change in terrestrial ecosystems of China and highlight the importance of shortwave radiation in driving vegetation productivity for the region, especially for tropical forests

    Reviews and Syntheses: optical sampling of the flux tower footprint

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    The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over 2 decades of proximal remote sensing at flux tower sites are reviewed. The organizing framework used here is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the photochemical reflectance index (PRI) and solar-induced chlorophyll fluorescence (SIF), are discussed within the context of the LUE model. Multiscale analysis across temporal and spatial axes is a central theme because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of the sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface–atmosphere fluxes. Complementary normalized difference vegetation index (NDVI) and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. I conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging from assessing ecosystem health and productivity to quantifying biospheric carbon sequestration

    First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems

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    The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.Comment: Accepted manuscript; 12 pages, 4 tables, 9 figure
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