1,508 research outputs found

    New Methods for Measurements of Photosynthesis from Space

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    Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when, and how carbon dioxide (CO2) is exchanged between the land and atmosphere. In particular, determining the rate of carbon fixation by the Earth's biosphere (commonly referred to as gross primary productivity, or GPP) and the dependence of this productivity on climate is a central goal. Historically, GPP has been inferred from spectral imagery of the land and ocean. Assessment of GPP from the color of the land and ocean requires, however, additional knowledge of the types of plants in the scene, their regulatory mechanisms, and climate variables such as soil moistureโ€”just the independent variables of interest! Sunlight absorbed by chlorophyll in photosynthetic organisms is mostly used to drive photosynthesis, but some can also be dissipated as heat or reโ€radiated at longer wavelengths (660โ€“800 nm). This nearโ€infrared light reโ€emitted from illuminated plants is termed solarinduced fluorescence (SIF), and it has been found to strongly correlate with GPP. To advance our understanding of SIF and its relation to GPP and environmental stress at the planetary scale, the Keck Institute for Space Studies (KISS) convened a workshopโ€”held in Pasadena, California, in August 2012โ€”to focus on a newly developed capacity to monitor chlorophyll fluorescence from terrestrial vegetation by satellite. This revolutionary approach for retrieving global observations of SIF promises to provide direct and spatially resolved information on GPP, an ideal bottomโ€up complement to the atmospheric net CO2 exchange inversions. Workshop participants leveraged our efforts on previous studies and workshops related to the European Space Agencyโ€™s FLuorescence EXplorer (FLEX) mission concept, which had already targeted SIF for a possible satellite mission and had developed a vibrant research community with many important publications. These studies, mostly focused on landscape, canopy, and leafโ€level interpretation, provided the groundโ€work for the workshop, which focused on the global carbon cycle and synergies with atmospheric net flux inversions. Workshop participants included key members of several communities: plant physiologists with experience using active fluorescence methods to quantify photosynthesis; ecologists and radiative transfer experts who are studying the challenge of scaling from the leaf to regional scales; atmospheric scientists with experience retrieving photometric information from spaceโ€borne spectrometers; and carbon cycle experts who are integrating new observations into models that describe the exchange of carbon between the atmosphere, land and ocean. Together, the participants examined the link between โ€œpassiveโ€ fluorescence observed from orbiting spacecraft and the underlying photochemistry, plant physiology and biogeochemistry of the land and ocean. This report details the opportunity for forging a deep connection between scientists doing basic research in photosynthetic mechanisms and the more applied community doing research on the Earth System. Too often these connections have gotten lost in empiricism associated with the coarse scale of global models. Chlorophyll fluorescence has been a major tool for basic research in photosynthesis for nearly a century. SIF observations from space, although sensing a large footprint, probe molecular events occurring in the leaves below. This offers an opportunity for direct mechanistic insight that is unparalleled for studies of biology in the Earth System. A major focus of the workshop was to review the basic mechanisms that underlie this phenomenon, and to explore modeling tools that have been developed to link the biophysical and biochemical knowledge of photosynthesis with the observableโ€”in this case, the radiance of SIFโ€”seen by the satellite. Discussions led to the identification of areas where knowledge is still lacking. For example, the inability to do controlled illumination observations from space limits the ability to fully constrain the variables that link fluorescence and photosynthesis. Another focus of the workshop explored a โ€œtopโ€downโ€ view of the SIF signal from space. Early studies clearly identified a strong correlation between the strength of this signal and our best estimate of the rate of photosynthesis (GPP) over the globe. New studies show that this observation provides improvements over conventional reflectanceโ€based remote sensing in detecting seasonal and environmental (particularly drought related) modulation of photosynthesis. Apparently SIF responds much more quickly and with greater dynamic range than typical greenness indices when GPP is perturbed. However, discussions at the workshop also identified areas where topโ€down analysis seemed to be โ€œout in frontโ€ of mechanistic studies. For example, changes in SIF based on changes in canopy light interception and the light use efficiency of the canopy, both of which occur in response to drought, are assumed equivalent in the topโ€down analysis, but the mechanistic justification for this is still lacking from the bottomโ€up side. Workshop participants considered implications of these mechanistic and empirical insights for largeโ€scale models of the carbon cycle and biogeochemistry, and also made progress toward incorporating SIF as a simulated output in land surface models used in global and regionalโ€scale analysis of the carbon cycle. Comparison of remotely sensed SIF with modelsimulated SIF may open new possibilities for model evaluation and data assimilation, perhaps leading to better modeling tools for analysis of the other retrieval from GOSAT satellite, atmospheric CO2 concentration. Participants also identified another application for SIF: a linkage to the physical climate system arising from the ability to better identify regional development of plant water stress. Decreases in transpiration over large areas of a continent are implicated in the development and โ€œlockingโ€inโ€ of drought conditions. These discussions also identified areas where current land surface models need to be improved in order to enable this research. Specifically, the radiation transport treatments need dramatic overhauls to correctly simulate SIF. Finally, workshop participants explored approaches for retrieval of SIF from satellite and groundโ€based sensors. The difficulty of resolving SIF from the overwhelming flux of reflected sunlight in the spectral region where fluorescence occurs was once a major impediment to making this measurement. Placement of very high spectral resolution spectrometers on GOSAT (and other greenhouse gasโ€“sensing satellites) has enabled retrievals based on infilling of solar Fraunhofer lines, enabling accurate fluorescence measurements even in the presence of moderately thick clouds. Perhaps the most interesting challenge here is that there is no readily portable groundโ€based instrumentation that even approaches the capability of GOSAT and other planned greenhouse gas satellites. This strongly limits scientistsโ€™ ability to conduct groundโ€based studies to characterize the footprint of the GOSAT measurement and to conduct studies of radiation transport needed to interpret SIF measurement. The workshop results represent a snapshot of the state of knowledge in this area. New research activities have sprung from the deliberations during the workshop, with publications to follow. The introduction of this new measurement technology to a wide slice of the community of Earth System Scientists will help them understand how this new technology could help solve problems in their research, address concerns about the interpretation, identify future research needs, and elicit support of the wider community for research needed to support this observation. Somewhat analogous to the original discovery that vegetation indices could be derived from satellite measurements originally intended to detect clouds, the GOSAT observations are a rare case in which a (fortuitous) global satellite dataset becomes available before the research community had a consolidated understanding on how (beyond an empirical correlation) it could be applied to understanding the underlying processes. Vegetation indices have since changed the way we see the global biosphere, and the workshop participants envision that fluorescence can perform the next indispensable step by complementing these measurements with independent estimates that are more indicative of actual (as opposed to potential) photosynthesis. Apart from the potential FLEX mission, no dedicated satellite missions are currently planned. OCOโ€2 and โ€3 will provide much more data than GOSAT, but will still not allow for regional studies due to the lack of mapping capabilities. Geostationary observations may even prove most useful, as they could track fluorescence over the course of the day and clearly identify stressโ€related downโ€regulation of photosynthesis. Retrieval of fluorescence on the global scale should be recognized as a valuable tool; it can bring the same quantum leap in our understanding of the global carbon cycle as vegetation indices once did

    Satellite-detected fluorescence reveals global physiology of ocean phytoplankton

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    ยฉ 2009 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 6 (2009): 779-794, doi: 10.5194/bg-6-779-2009Phytoplankton photosynthesis links global ocean biology and climate-driven fluctuations in the physical environment. These interactions are largely expressed through changes in phytoplankton physiology, but physiological status has proven extremely challenging to characterize globally. Phytoplankton fluorescence does provide a rich source of physiological information long exploited in laboratory and field studies, and is now observed from space. Here we evaluate the physiological underpinnings of global variations in satellite-based phytoplankton chlorophyll fluorescence. The three dominant factors influencing fluorescence distributions are chlorophyll concentration, pigment packaging effects on light absorption, and light-dependent energy-quenching processes. After accounting for these three factors, resultant global distributions of quenching-corrected fluorescence quantum yields reveal a striking consistency with anticipated patterns of iron availability. High fluorescence quantum yields are typically found in low iron waters, while low quantum yields dominate regions where other environmental factors are most limiting to phytoplankton growth. Specific properties of photosynthetic membranes are discussed that provide a mechanistic view linking iron stress to satellite-detected fluorescence. Our results present satellite-based fluorescence as a valuable tool for evaluating nutrient stress predictions in ocean ecosystem models and give the first synoptic observational evidence that iron plays an important role in seasonal phytoplankton dynamics of the Indian Ocean. Satellite fluorescence may also provide a path for monitoring climate-phytoplankton physiology interactions and improving descriptions of phytoplankton light use efficiencies in ocean productivity models.This work was supported by grants from the NASA Ocean Biology and Biogeochemistry Program and the NSF Biological Oceanography Program

    Satellite-Detected Fluorescence Reveals Global Physiology of Ocean Phytoplankton

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    Phytoplankton photosynthesis links global ocean biology and climate-driven fluctuations in the physical environment. These interactions are largely expressed through changes in phytoplankton physiology, but physiological status has proven extremely challenging to characterize globally. Phytoplankton fluorescence does provide a rich source of physiological information long exploited in laboratory and field studies, and is now observed from space. Here we evaluate the physiological underpinnings of global variations in satellite-based phytoplankton chlorophyll fluorescence. The three dominant factors influencing fluorescence distributions are chlorophyll concentration, pigment packaging effects on light absorption, and light-dependent energy-quenching processes. After accounting for these three factors, resultant global distributions of quenching-corrected fluorescence quantum yields reveal a striking consistency with anticipated patterns of iron availability. High fluorescence quantum yields are typically found in low iron waters, while low quantum yields dominate regions where other environmental factors are most limiting to phytoplankton growth. Specific properties of photosynthetic membranes are discussed that provide a mechanistic view linking iron stress to satellite-detected fluorescence. Our results present satellite-based fluorescence as a valuable tool for evaluating nutrient stress predictions in ocean ecosystem models and give the first synoptic observational evidence that iron plays an important role in seasonal phytoplankton dynamics of the Indian Ocean. Satellite fluorescence may also provide a path for monitoring climate-phytoplankton physiology interactions and improving descriptions of phytoplankton light use efficiencies in ocean productivity models

    The effect of climate change on the carbon balance between photosynthesis and respiration in Antarctic microalgae

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    The biological process of the carbon cycle in the Antarctic Ocean is controlled by the photosynthetic activity of the primary producers. The amount of fixed carbon does not only depend on the photosynthetic activity but also on the carbon losses due to respiration. Thus, the ratio photosynthesis to respiration (rP/R) is an important parameter to predict the effect of climate change on the Antarctic ecosystem. Indeed, the ongoing changes in climate change are influencing the dynamics of environmental conditions, which has tremendous effects on the phytoplankton community. Therefore, two ecologically relevant species from the Southern Ocean were here investigated: the diatom Chaetoceros sp. and the prymnesiophyte Phaeocystis antarctica, studying the changes in the rP/R under global climate change conditions. Three main parameters were examined i.e temperature, salinity and iron limitation. The P/R ratio was significantly affected by temperature, while salinity had only a secondary importance, although with species-specific differences. More specifically, the values were ranging from 12.3 to 7.5 for Chaetoceros sp. and from 12.4 to 2.5 for P. antarctica. The changes in this ratio were principally due to variations in respiration, rather than in photosynthesis. Chaetoceros sp. appears to be less flexible in the regulation of the extent of photoprotective mechanisms (non-photochemical quenching and alternative electrons), but its photoprotective level was generally higher than in P. antarctica. Regarding iron limitation, data were successfully collected only for Chaetoceros sp.. The P/R ratio, equal to 2.8, did not change under iron limitation, with iron limited cells showing a very efficient acclimation to the lowered assimilatory metabolism by decreasing their respiratory losses

    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

    Developing screening tools for abiotic stresses using cowpea [Vigna unguiculata (L.) Walp.] as a model crop

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    Abiotic stresses cause extensive loss to agriculture production worldwide. Cowpea is an important legume crop grown widely in tropical and subtropical regions where high temperature, ultraviolet-B (UVB) radiation and drought are the common stress factors limiting production. Various vegetative, physiological, biochemical and reproductive plant attributes were assessed under a range of UVB radiation levels in Experiment I and in a combination with two doses of each carbon dioxide concentration [CO2], temperature, and UVB radiation and their interactions in Experiment II by using six cowpea genotypes and sunlit plant growth chambers. The dynamics of photosynthesis and fluorescence processes were assessed in 15 cowpea genotypes under drought condition in Experiment III in pot-grown plants under sunlit conditions. A distinct response pattern was not observed in cowpea in response to UVB radiation form 0 to 15 kJ; however, plants grown under elevated UVB showed reduced photosynthesis resulting in shorter plants and produced smaller flowers and lower seed yield. Increased phenolic compounds appeared to be a defense response to UVB radiation. The growth enhancements observed by doubling of [CO2] were not observed when plants were grown in combination with elevated UVB or temperature which also showed the most detrimental effects on plant growth and seed yield. Results form Experiment I and II revealed that cowpea reproductive traits were highly sensitive to abiotic stresses compared to the vegetative growth and development. A total stress response index (TSRI) technique, derived from all vegetative and reproductive parameters, was used to screen genotypes for their stress tolerance to UVB or combination of stresses. An increase in water use efficiency while maintaining higher rate of photosynthesis was an important drought tolerance mechanism in tolerant cowpea genotypes. Using principal component analysis technique, four groups of the genotypes were identified for their drought tolerance. Evaluating same genotypes across stress conditions revealed that no single genotype has the absolute tolerance characters to all stress conditions. The identified diversity for abiotic stress tolerance among cowpea genotypes and associated traits can be used to develop tolerant genotypes suitable for an agro-ecological niche though traditional breeding or genetic engineering methods

    New Methods for Measurements of Photosynthesis from Space

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    Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when, and how carbon dioxide (CO2) is exchanged between the land and atmosphere. In particular, determining the rate of carbon fixation by the Earth's biosphere (commonly referred to as gross primary productivity, or GPP) and the dependence of this productivity on climate is a central goal. Historically, GPP has been inferred from spectral imagery of the land and ocean. Assessment of GPP from the color of the land and ocean requires, however, additional knowledge of the types of plants in the scene, their regulatory mechanisms, and climate variables such as soil moistureโ€”just the independent variables of interest! Sunlight absorbed by chlorophyll in photosynthetic organisms is mostly used to drive photosynthesis, but some can also be dissipated as heat or reโ€radiated at longer wavelengths (660โ€“800 nm). This nearโ€infrared light reโ€emitted from illuminated plants is termed solarinduced fluorescence (SIF), and it has been found to strongly correlate with GPP. To advance our understanding of SIF and its relation to GPP and environmental stress at the planetary scale, the Keck Institute for Space Studies (KISS) convened a workshopโ€”held in Pasadena, California, in August 2012โ€”to focus on a newly developed capacity to monitor chlorophyll fluorescence from terrestrial vegetation by satellite. This revolutionary approach for retrieving global observations of SIF promises to provide direct and spatially resolved information on GPP, an ideal bottomโ€up complement to the atmospheric net CO2 exchange inversions. Workshop participants leveraged our efforts on previous studies and workshops related to the European Space Agencyโ€™s FLuorescence EXplorer (FLEX) mission concept, which had already targeted SIF for a possible satellite mission and had developed a vibrant research community with many important publications. These studies, mostly focused on landscape, canopy, and leafโ€level interpretation, provided the groundโ€work for the workshop, which focused on the global carbon cycle and synergies with atmospheric net flux inversions. Workshop participants included key members of several communities: plant physiologists with experience using active fluorescence methods to quantify photosynthesis; ecologists and radiative transfer experts who are studying the challenge of scaling from the leaf to regional scales; atmospheric scientists with experience retrieving photometric information from spaceโ€borne spectrometers; and carbon cycle experts who are integrating new observations into models that describe the exchange of carbon between the atmosphere, land and ocean. Together, the participants examined the link between โ€œpassiveโ€ fluorescence observed from orbiting spacecraft and the underlying photochemistry, plant physiology and biogeochemistry of the land and ocean. This report details the opportunity for forging a deep connection between scientists doing basic research in photosynthetic mechanisms and the more applied community doing research on the Earth System. Too often these connections have gotten lost in empiricism associated with the coarse scale of global models. Chlorophyll fluorescence has been a major tool for basic research in photosynthesis for nearly a century. SIF observations from space, although sensing a large footprint, probe molecular events occurring in the leaves below. This offers an opportunity for direct mechanistic insight that is unparalleled for studies of biology in the Earth System. A major focus of the workshop was to review the basic mechanisms that underlie this phenomenon, and to explore modeling tools that have been developed to link the biophysical and biochemical knowledge of photosynthesis with the observableโ€”in this case, the radiance of SIFโ€”seen by the satellite. Discussions led to the identification of areas where knowledge is still lacking. For example, the inability to do controlled illumination observations from space limits the ability to fully constrain the variables that link fluorescence and photosynthesis. Another focus of the workshop explored a โ€œtopโ€downโ€ view of the SIF signal from space. Early studies clearly identified a strong correlation between the strength of this signal and our best estimate of the rate of photosynthesis (GPP) over the globe. New studies show that this observation provides improvements over conventional reflectanceโ€based remote sensing in detecting seasonal and environmental (particularly drought related) modulation of photosynthesis. Apparently SIF responds much more quickly and with greater dynamic range than typical greenness indices when GPP is perturbed. However, discussions at the workshop also identified areas where topโ€down analysis seemed to be โ€œout in frontโ€ of mechanistic studies. For example, changes in SIF based on changes in canopy light interception and the light use efficiency of the canopy, both of which occur in response to drought, are assumed equivalent in the topโ€down analysis, but the mechanistic justification for this is still lacking from the bottomโ€up side. Workshop participants considered implications of these mechanistic and empirical insights for largeโ€scale models of the carbon cycle and biogeochemistry, and also made progress toward incorporating SIF as a simulated output in land surface models used in global and regionalโ€scale analysis of the carbon cycle. Comparison of remotely sensed SIF with modelsimulated SIF may open new possibilities for model evaluation and data assimilation, perhaps leading to better modeling tools for analysis of the other retrieval from GOSAT satellite, atmospheric CO2 concentration. Participants also identified another application for SIF: a linkage to the physical climate system arising from the ability to better identify regional development of plant water stress. Decreases in transpiration over large areas of a continent are implicated in the development and โ€œlockingโ€inโ€ of drought conditions. These discussions also identified areas where current land surface models need to be improved in order to enable this research. Specifically, the radiation transport treatments need dramatic overhauls to correctly simulate SIF. Finally, workshop participants explored approaches for retrieval of SIF from satellite and groundโ€based sensors. The difficulty of resolving SIF from the overwhelming flux of reflected sunlight in the spectral region where fluorescence occurs was once a major impediment to making this measurement. Placement of very high spectral resolution spectrometers on GOSAT (and other greenhouse gasโ€“sensing satellites) has enabled retrievals based on infilling of solar Fraunhofer lines, enabling accurate fluorescence measurements even in the presence of moderately thick clouds. Perhaps the most interesting challenge here is that there is no readily portable groundโ€based instrumentation that even approaches the capability of GOSAT and other planned greenhouse gas satellites. This strongly limits scientistsโ€™ ability to conduct groundโ€based studies to characterize the footprint of the GOSAT measurement and to conduct studies of radiation transport needed to interpret SIF measurement. The workshop results represent a snapshot of the state of knowledge in this area. New research activities have sprung from the deliberations during the workshop, with publications to follow. The introduction of this new measurement technology to a wide slice of the community of Earth System Scientists will help them understand how this new technology could help solve problems in their research, address concerns about the interpretation, identify future research needs, and elicit support of the wider community for research needed to support this observation. Somewhat analogous to the original discovery that vegetation indices could be derived from satellite measurements originally intended to detect clouds, the GOSAT observations are a rare case in which a (fortuitous) global satellite dataset becomes available before the research community had a consolidated understanding on how (beyond an empirical correlation) it could be applied to understanding the underlying processes. Vegetation indices have since changed the way we see the global biosphere, and the workshop participants envision that fluorescence can perform the next indispensable step by complementing these measurements with independent estimates that are more indicative of actual (as opposed to potential) photosynthesis. Apart from the potential FLEX mission, no dedicated satellite missions are currently planned. OCOโ€2 and โ€3 will provide much more data than GOSAT, but will still not allow for regional studies due to the lack of mapping capabilities. Geostationary observations may even prove most useful, as they could track fluorescence over the course of the day and clearly identify stressโ€related downโ€regulation of photosynthesis. Retrieval of fluorescence on the global scale should be recognized as a valuable tool; it can bring the same quantum leap in our understanding of the global carbon cycle as vegetation indices once did

    Application of FTIR spectroscopy for monitoring water quality in a hypertrophic aquatic ecosystem (Lake Auensee, Leipzig)

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    FTIR spectroscopy as molecular fingerprint has been used to assess macromolecular and ele-mental stoichiometry as well as growth rates of phytoplankton cells. Chemometric models have been developed to extract quantitative information from FTIR spectra to reveal macro-molecular composition (of proteins, carbohydrates and lipids), C:N ratio, and growth potential. In this study, we tested these chemometric models based on lab-cultured algal species in mon-itoring changes of phytoplankton community structure in a hypertrophic lake (Lake Auensee, Leipzig, Germany), where a seasonal succession of spring green algal bloom followed by cya-nobacterial dominance in summer can be commonly observed. Our results demonstrated that green algae reacted to environmental changes such as nitrogen limitation (due to imbalanced nitrogen and phosphorus supply) with restricted growth by changing carbon allocation from protein synthesis to storage carbohydrates and/or lipids, and increased C:N ratio. By contrast, cyanobacteria proliferated under nitrogen limiting conditions. Furthermore, the FTIR-based growth potential of green alga matched well with the population biomass determined by the Chl-a concentration. However, the predicted growth potential based on FTIR spectroscopy cannot describe the realistic growth development of cyanobacteria in this lake. These results revealed that green algae and cyanobacteria have different strategies of C-allocation stoichi-ometry and growth patterns in response to environmental changes. These taxon-specific re-sponses may explain at a molecular level why green algae bloomed in the spring under condi-tions with sufficient nutrient, lower pH and lower water temperature; while cyanobacteria overgrew green algae and dominated in the summer under conditions with limited nutrient availability, higher pH and higher water temperature. In addition, the applicability of these chemometric models for predicting field cyanobacterial growth is of limited value. This may be attributed to other special adaptation properties of cyanobacterial species under stress growth conditions. We used flow cytometry to isolate functional algal groups from the water samples. Despite some drawbacks of the flow cytometry combined FTIR spectroscopy tech-nique, this method provides prospects of monitoring water quality and early warning of harmful algal blooms

    ๊ทผ์ ‘ ํ‘œ๋ฉด ์›๊ฒฉ ์„ผ์‹ฑ ์‹œ์Šคํ…œ๋“ค์„ ์ด์šฉํ•œ ์ง€์†์  ์‹๋ฌผ ๊ณ„์ ˆ ๋ฐ ํƒœ์–‘ ์œ ๋„ ์—ฝ๋ก์†Œ ํ˜•๊ด‘๋ฌผ์งˆ ๊ด€์ธก

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ˜‘๋™๊ณผ์ • ์กฐ๊ฒฝํ•™, 2022.2. ๋ฅ˜์˜๋ ฌ.Monitoring phenology, physiological and structural changes in vegetation is essential to understand feedbacks of vegetation between terrestrial ecosystems and the atmosphere by influencing the albedo, carbon flux, water flux and energy. To this end, normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) from satellite remote sensing have been widely used. However, there are still limitations in satellite remote sensing as 1) satellite imagery could not capture fine-scale spatial resolution of SIF signals, 2) satellite products are strongly influenced by condition of the atmosphere (e.g. clouds), thus it is challenging to know physiological and structural changes in vegetation on cloudy days and 3) satellite imagery captured a mixed signal from over- and understory, thus it is difficult to study the difference between overstory and understory phenology separately. Therefore, in order to more accurately understand the signals observed from the satellite, further studies using near-surface remote sensing system to collect ground-based observed data are needed. The main purpose of this dissertation is continuous observation of vegetation phenology and SIF using near-surface remote sensing system. To achieve the main goal, I set three chapters as 1) developing low-cost filter-based near-surface remote sensing system to monitor SIF continuously, 2) monitoring SIF in a temperate evergreen needleleaf forest continuously, and 3) understanding the relationships between phenology from in-situ multi-layer canopies and satellite products. In Chapter 2, I developed the filter-based smart surface sensing system (4S-SIF) to overcome the technical challenges of monitoring SIF in the field as well as to decrease sensor cost for more comprehensive spatial sampling. I verified the satisfactory spectral performance of the bandpass filters and confirmed that digital numbers (DN) from 4S-SIF exhibited linear relationships with the DN from the hyperspectral spectroradiometer in each band (R2 > 0.99). In addition, we confirmed that 4S-SIF shows relatively low variation of dark current value at various temperatures. Furthermore, the SIF signal from 4S-SIF represents a strong linear relationship with QEpro-SIF either changing the physiological mechanisms of the plant using DCMU (3-(3, 4-dichlorophenyl)-1, 1-dimethyurea) treatment. I believe that 4S-SIF will be a useful tool for collecting in-situ data across multiple spatial and temporal scales. Satellite-based SIF provides us with new opportunities to understand the physiological and structural dynamics of vegetation from canopy to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. In Chapter 3, I reported the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, I investigated the relationships of light use efficiency (LUE_p), chlorophyll fluorescence yield (ฮฆ_F), and the fraction of emitted SIF photons escaping from the canopy (f_esc) separately. I found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. In addition, there were differential responses of LUE_p and ฮฆ_F to air temperature. While LUE_p reached saturation at high air temperatures, ฮฆ_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level ฮฆ_F (R2 = 0.84) implying that ฮฆ_F could be more closely related to pigment pool changes rather than LUE_p. In addition, I found that the f_esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE_p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forests. We do not fully understand what satellite NDVI derived leaf-out and full leaf dates actually observe because deciduous broadleaf forest consists of multi-layer canopies typically and mixed-signal from multi-layer canopies could affect satellite observation. Ultimately, we have the following question: What phenology do we actually see from space compared to ground observations on multi-layer canopy phenology? In Chapter 4, I reported the results of 8 years of continuous observations of multi-layer phenology and climate variables in a deciduous broadleaf forest, South Korea. Multi-channel spectrometers installed above and below overstory canopy allowed us to monitor over- and understory canopy phenology separately, continuously. I evaluated the widely used phenology detection methods, curvature change rate and threshold with NDVI observed above top of the canopy and compared leaf-out and full leaf dates from both methods to in-situ observed multi-layer phenology. First, I found that NDVI from the above canopy had a strong linear relationship with satellites NDVI (R2=0.95 for MODIS products and R2= 0.85 for Landsat8). Second, leaf-out dates extracted by the curvature change rate method and 10% threshold were well matched with understory leaf-out dates. Third, the full-leaf dates extracted by the curvature change rate method and 90% threshold were similar to overstory full-leaf dates. Furthermore, I found that overstory leaf-out dates were closely correlated to accumulated growing degree days (AGDD) while understory leaf-out dates were related to AGDD and also sensitive to the number of chill days (NCD). These results suggest that satellite-based leaf-out and full leaf dates represent understory and overstory signals in the deciduous forest site, which requires caution when using satellite-based phenology data into future prediction as overstory and understory canopy show different sensitivities to AGDD and NCD.์‹๋ฌผ ๊ณ„์ ˆ ๋ฐ ์‹์ƒ์˜ ์ƒ๋ฆฌํ•™์ , ๊ตฌ์กฐ์ ์ธ ๋ณ€ํ™”๋ฅผ ์ง€์†์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋ง ํ•˜๋Š” ๊ฒƒ์€ ์œก์ƒ์ƒํƒœ๊ณ„์™€ ๋Œ€๊ธฐ๊ถŒ ์‚ฌ์ด์˜ ์—๋„ˆ์ง€, ํƒ„์†Œ ์ˆœํ™˜ ๋“ฑ์˜ ํ”ผ๋“œ๋ฐฑ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ํ•„์ˆ˜์ ์ด๋‹ค. ์ด๋ฅผ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์œ„์„ฑ์—์„œ ๊ด€์ธก๋œ ์ •๊ทœํ™” ์‹์ƒ ์ง€์ˆ˜ (NDVI) ํƒœ์–‘ ์œ ๋„ ์—ฝ๋ก์†Œ ํ˜•๊ด‘๋ฌผ์งˆ (SIF)๋Š” ๋Œ€์ค‘์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์šฐ์ฃผ ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ ์ž๋ฃŒ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•œ๊ณ„์ ๋“ค์ด ์กด์žฌํ•œ๋‹ค. 1) ์•„์ง๊นŒ์ง€ ๊ณ ํ•ด์ƒ๋„์˜ ์œ„์„ฑ ๊ธฐ๋ฐ˜ SIF ์ž๋ฃŒ๋Š” ์—†๊ณ , 2) ์œ„์„ฑ ์ž๋ฃŒ๋“ค์€ ๋Œ€๊ธฐ์˜ ์งˆ (์˜ˆ, ๊ตฌ๋ฆ„)์— ์˜ํ–ฅ์„ ๋ฐ›์•„, ํ๋ฆฐ ๋‚ ์˜ ์‹์ƒ์˜ ์ƒ๋ฆฌํ•™์ , ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ํƒ์ง€ํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ๋˜ํ•œ, 3) ์œ„์„ฑ ์ด๋ฏธ์ง€๋Š” ์ƒ๋ถ€ ์‹์ƒ๊ณผ ํ•˜๋ถ€ ์‹์ƒ์ด ํ˜ผํ•ฉ๋˜์–ด ์„ž์ธ ์‹ ํ˜ธ๋ฅผ ํƒ์ง€ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ฐ ์ธต์˜ ์‹๋ฌผ ๊ณ„์ ˆ์„ ๊ฐ๊ฐ ์—ฐ๊ตฌํ•˜๊ธฐ์— ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ, ์œ„์„ฑ์—์„œ ํƒ์ง€ํ•œ ์‹ ํ˜ธ๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ , ์‹์ƒ์˜ ์ƒ๋ฆฌํ•™์ , ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ๋ณด๋‹ค ์ •ํ™•ํžˆ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทผ์ ‘ ํ‘œ๋ฉด ์›๊ฒฉ ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์‹ค์ธก ์ž๋ฃŒ ๊ธฐ๋ฐ˜์˜ ์—ฐ๊ตฌ๋“ค์ด ์š”๊ตฌ๋œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์˜ ์ฃผ ๋ชฉ์ ์€ ๊ทผ์ ‘ ํ‘œ๋ฉด ์›๊ฒฉ ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ์‹๋ฌผ ๊ณ„์ ˆ ๋ฐ SIF๋ฅผ ํ˜„์žฅ์—์„œ ์ง€์†์ ์œผ๋กœ ์‹ค์ธกํ•˜๊ณ , ์œ„์„ฑ ์˜์ƒ ๊ธฐ๋ฐ˜์˜ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋Š” ํ•œ๊ณ„์  ๋ฐ ๊ถ๊ธˆ์ฆ๋“ค์„ ํ•ด๊ฒฐ ๋ฐ ๋ณด์™„ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์•„๋ž˜์™€ ๊ฐ™์€ ์„ธ๊ฐ€์ง€ Chapter: 1) SIF๋ฅผ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•œ ํ•„ํ„ฐ ๊ธฐ๋ฐ˜์˜ ์ €๋ ดํ•œ ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ, 2)์˜จ๋Œ€ ์นจ์—ฝ์ˆ˜๋ฆผ์—์„œ์˜ ์—ฐ์†์ ์ธ SIF ๊ด€์ธก, 3)์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ ์‹๋ฌผ ๊ณ„์ ˆ๊ณผ ์‹ค์ธกํ•œ ๋‹ค์ธต ์‹์ƒ์˜ ์‹๋ฌผ ๊ณ„์ ˆ ๋น„๊ต๋กœ ๊ตฌ์„ฑํ•˜๊ณ , ์ด๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. SIF๋Š” ์‹์ƒ์˜ ๊ตฌ์กฐ์ , ์ƒ๋ฆฌํ•™์  ๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•˜๊ณ , ์ถ”์ •ํ•˜๋Š” ์ธ์ž๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์–ด, SIF๋ฅผ ํ˜„์žฅ์—์„œ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๊ทผ์ ‘ ํ‘œ๋ฉด ์›๊ฒฉ ์„ผ์‹ฑ ์‹œ์Šคํ…œ๋“ค์ด ์ตœ๊ทผ ์ œ์‹œ๋˜์–ด ์˜ค๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์•„์ง๊นŒ์ง€ SIF๋ฅผ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•œ ์ƒ์—…์ ์œผ๋กœ ์œ ํ†ต๋˜๋Š” ๊ด€์ธก ์‹œ์Šคํ…œ์€ ํ˜„์ €ํžˆ ๋ถ€์กฑํ•˜๋ฉฐ, ๋ถ„๊ด‘๊ณ„์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ์ƒ ํ˜„์žฅ์—์„œ ๊ด€์ธก ์‹œ์Šคํ…œ์„ ๋ณด์ • ๋ฐ ๊ด€๋ฆฌํ•˜๊ธฐ๊ฐ€ ์–ด๋ ค์›Œ ๋†’์€ ์งˆ์˜ SIF๋ฅผ ์ทจ๋“ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ๋„์ „ ์ ์ธ ๋ถ„์•ผ์ด๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ Chapter 2์—์„œ๋Š” SIF๋ฅผ ํ˜„์žฅ์—์„œ ๋ณด๋‹ค ์†์‰ฝ๊ฒŒ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•œ ํ•„ํ„ฐ ๊ธฐ๋ฐ˜์˜ ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ(Smart Surface Sensing System, 4S-SIF)์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์„ผ์„œ๋Š” ๋Œ€์—ญ ํ•„ํ„ฐ๋“ค๊ณผ ํฌํ† ๋‹ค์ด์˜ค๋“œ๊ฐ€ ๊ฒฐํ•ฉ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์„œ๋ณด ๋ชจํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋Œ€์—ญ ํ•„ํ„ฐ ๋ฐ ๊ด€์ธก ๋ฐฉํ–ฅ์„ ์ž๋™์ ์œผ๋กœ ๋ณ€๊ฒฝํ•จ์œผ๋กœ์จ, ํ•œ ๊ฐœ์˜ ํฌํ† ๋‹ค์ด์˜ค๋“œ๊ฐ€ 3๊ฐœ์˜ ํŒŒ์žฅ ๋ฒ”์œ„(757, 760, 770 nm)์˜ ๋น› ๋ฐ ํƒœ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ž…์‚ฌ๋˜๋Š” ๊ด‘๋Ÿ‰๊ณผ ์‹์ƒ์œผ๋กœ ๋ฐ˜์‚ฌ/๋ฐฉ์ถœ๋œ ๊ด‘๋Ÿ‰์„ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ณ ์•ˆ๋˜์—ˆ๋‹ค. ํฌํ† ๋‹ค์ด์˜ค๋“œ๋กœ๋ถ€ํ„ฐ ์ธ์‹๋œ ๋””์ง€ํ„ธ ์ˆ˜์น˜ ๊ฐ’์€ ์ƒ์—…์ ์œผ๋กœ ํŒ๋งค๋˜๋Š” ์ดˆ๊ณ ํ•ด์ƒ๋„ ๋ถ„๊ด‘๊ณ„(QE Pro, Ocean Insight)์™€ ๋šœ๋ ทํ•œ ์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค (R2 > 0.99). ์ถ”๊ฐ€์ ์œผ๋กœ, 4S-SIF์—์„œ ๊ด€์ธก๋œ SIF์™€ ์ดˆ๊ณ ํ•ด์ƒ๋„ ๋ถ„๊ด‘๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ์ถ”์ถœํ•œ SIF๊ฐ€ ์„ ํ˜•์ ์ธ ๊ด€๊ณ„๋ฅผ ์ด๋ฃจ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹์ƒ์˜ ์ƒ๋ฆฌํ•™์  ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ค๋Š” ํ™”ํ•™ ๋ฌผ์งˆ์ธ DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)์„ ์ฒ˜๋ฆฌํ–ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์‚ฐ์ถœ๋œ SIF๋“ค์€ ์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์„ผ์„œ๋Š” ๊ธฐ์กด ์‹œ์Šคํ…œ๋“ค์— ๋น„ํ•ด ์‚ฌ์šฉํ•˜๊ธฐ ์‰ฝ๊ณ  ๊ฐ„๋‹จํ•˜๋ฉฐ, ์ €๋ ดํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•œ ์‹œ๊ณต๊ฐ„์  ์Šค์ผ€์ผ์˜ SIF๋ฅผ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ SIF๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด์ผ์ฐจ์ƒ์‚ฐ์„ฑ(gross primary productivity, GPP)์„ ์ถ”์ •ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ์ตœ๊ทผ ํƒ„์†Œ ์ˆœํ™˜ ์—ฐ๊ตฌ ๋ถ„์•ผ์—์„œ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋Š” ์—ฐ๊ตฌ ์ฃผ์ œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, SIF์™€ GPP์˜ ๊ด€๊ณ„๋Š” ์—ฌ์ „ํžˆ ๋งŽ์€ ๋ถˆํ™•์‹ค์„ฑ์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋Š”๋ฐ, ์ด๋Š” SIF-GPP ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ์‹์ƒ์˜ ๊ตฌ์กฐ์  ๋ฐ ์ƒ๋ฆฌํ•™์  ์š”์ธ์„ ๋”ฐ๋กœ ๋ถ„๋ฆฌํ•˜์—ฌ ๊ณ ์ฐฐํ•œ ์—ฐ๊ตฌ๋“ค์ด ๋ถ€์กฑํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ Chapter 3์—์„œ๋Š” ์ง€์†์ ์œผ๋กœ SIF, GPP, ํก์ˆ˜๋œ ๊ด‘ํ•ฉ์„ฑ์œ ํšจ๋ณต์‚ฌ๋Ÿ‰ (absorbed photosynthetically active radiation, APAR), ๊ทธ๋ฆฌ๊ณ  ํด๋กœ๋กœํ•„๊ณผ ์นด๋กœํ‹ฐ๋…ธ์ด๋“œ์˜ ๋น„์œจ ์ธ์ž (chlorophyll: carotenoid index, CCI)๋ฅผ ์˜จ๋Œ€์นจ์—ฝ์ˆ˜๋ฆผ์—์„œ ์—ฐ์†์ ์œผ๋กœ ๊ด€์ธกํ•˜์˜€๋‹ค. SIF-GPP ๊ด€๊ณ„์˜ ๊ตฌ์ฒด์ ์ธ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๊ธฐ ์œ„ํ•˜์—ฌ, ๊ด‘ ์ด์šฉํšจ์œจ (light use efficiency, LUE_p), ์—ฝ๋ก์†Œ ํ˜•๊ด‘ ์ˆ˜๋“๋ฅ  (chlorophyll fluorescence yield, ฮฆ_F) ๊ทธ๋ฆฌ๊ณ  SIF ๊ด‘์ž๊ฐ€ ๊ตฐ๋ฝ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐฉ์ถœ๋˜๋Š” ๋น„์œจ (escape fraction, f_esc)์„ ๊ฐ๊ฐ ๋„์ถœํ•˜๊ณ  ํƒ๊ตฌํ•˜์˜€๋‹ค. SIF์™€ GPP์˜ ๊ด€๊ณ„๋Š” ๋šœ๋ ทํ•œ ๋น„ ์„ ํ˜•์ ์ธ ๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ์œผ๋ฉฐ(R2 = 0.91 with a hyperbolic regression function, daily), ์ผ์ฃผ๊ธฐ ๋‹จ์œ„์—์„œ SIF๋Š” APAR์— ๋Œ€ํ•ด ์„ ํ˜•์ ์ด์—ˆ์ง€๋งŒ GPP๋Š” APAR์— ๋Œ€ํ•ด ๋šœ๋ ทํ•œ ํฌํ™” ๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ LUE_p ์™€ ฮฆ_F ๊ฐ€ ๋Œ€๊ธฐ ์˜จ๋„์— ๋”ฐ๋ผ ๋ฐ˜์‘ํ•˜๋Š” ์ •๋„๊ฐ€ ๋‹ค๋ฅธ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. LUE_p๋Š” ๋†’์€ ์˜จ๋„์—์„œ ํฌํ™” ๋˜์—ˆ์ง€๋งŒ, ฮฆ_F๋Š” ํฌํ™” ํŒจํ„ด์„ ํ™•์ธํ•  ์ˆ˜ ์—†์—ˆ๋‹ค. ๋˜ํ•œ, ๊ตฐ๋ฝ ์ˆ˜์ค€์—์„œ์˜ CCI์™€ ฮฆ_F๊ฐ€ ๋šœ๋ ทํ•œ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค(R2 = 0.84). ์ด๋Š” ฮฆ_F๊ฐ€ ์—ฝ๋ก์†Œ ์ƒ‰์†Œ์— ์˜ํ–ฅ์„ LUE_p์— ๋น„ํ•ด ๋” ๊ฐ•ํ•œ ๊ด€๊ณ„๊ฐ€ ์žˆ์„ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, f_esc๊ฐ€ ํƒœ์–‘๊ด‘์˜ ์‚ฐ๋ž€๋œ ์ •๋„์— ๋”ฐ๋ผ ๋ฐ˜์‘์„ ํ•˜์—ฌ, ฮฆ_F์™€ LUE_p์˜ ๊ฐ•ํ•œ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ํ˜•์„ฑํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ์˜จ๋Œ€ ์นจ์—ฝ์ˆ˜๋ฆผ์—์„œ ๊ตฐ๋ฝ ์ˆ˜์ค€์˜ SIF-GPP๊ด€๊ณ„๋ฅผ ์ƒ๋ฆฌํ•™์  ๋ฐ ๊ตฌ์กฐ์  ์ธก๋ฉด์—์„œ ์ดํ•ดํ•˜๊ณ  ๊ทœ๋ช…ํ•˜๋Š”๋ฐ ํฐ ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค. ์‹๋ฌผ ๊ณ„์ ˆ์€ ์‹์ƒ์ด ์ฒ ์„ ๋”ฐ๋ผ ์ฃผ๊ธฐ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ด๋Š” ๋ณ€ํ™”๋ฅผ ๊ด€์ธกํ•˜๋Š” ๋ฐ˜์‘์ด๋‹ค. ์‹๋ฌผ ๊ณ„์ ˆ์€ ์œก์ƒ์ƒํƒœ๊ณ„์™€ ๋Œ€๊ธฐ๊ถŒ ์‚ฌ์ด์˜ ๋ฌผ์งˆ ์ˆœํ™˜์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ NDVI๋Š” ์‹๋ฌผ ๊ณ„์ ˆ์„ ํƒ์ง€ํ•˜๊ณ  ์—ฐ๊ตฌํ•˜๋Š”๋ฐ ๊ฐ€์žฅ ๋Œ€์ค‘์ ์œผ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํ™œ์—ฝ์ˆ˜๋ฆผ์—์„œ์˜ ์œ„์„ฑ NDVI ๊ธฐ๋ฐ˜์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ ๋ฐ ์„ฑ์ˆ™ ์‹œ๊ธฐ๊ฐ€ ์‹ค์ œ ์–ด๋Š ์‹œ์ ์„ ํƒ์ง€ํ•˜๋Š”์ง€๋Š” ๋ถˆ๋ถ„๋ช…ํ•˜๋‹ค. ์‹ค์ œ ํ™œ์—ฝ์ˆ˜๋ฆผ์€ ๋‹ค์ธต ์‹์ƒ ๊ตฌ์กฐ์˜ ์‚ผ์ฐจ์›์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋Š” ๋ฐ˜๋ฉด, ์œ„์„ฑ ์˜์ƒ์€ ๋‹ค์ธต ์‹์ƒ์˜ ์‹ ํ˜ธ๊ฐ€ ์„ž์—ฌ ์žˆ๋Š” ์ด์ฐจ์›์˜ ๊ฒฐ๊ณผ๋ฌผ์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ์œ„์„ฑ NDVI ๊ธฐ๋ฐ˜์˜ ์‹๋ฌผ ๊ณ„์ ˆ์ด ๋‹ค์ธต ์‹์ƒ ๊ตฌ์กฐ๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ๋Š” ํ™œ์—ฝ์ˆ˜๋ฆผ์—์„œ ์‹ค์ œ ํ˜„์žฅ ๊ด€์ธก๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ์–ด๋Š ์‹œ์ ์„ ํƒ์ง€ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๊ถ๊ธˆ์ฆ์ด ๋‚จ๋Š”๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ Chapter 4์—์„œ๋Š” ์ง€์†์ ์œผ๋กœ 8๋…„ ๋™์•ˆ ํ™œ์—ฝ์ˆ˜๋ฆผ๋‚ด์˜ ๋‹ค์ธต ์‹์ƒ์˜ ์‹๋ฌผ ๊ณ„์ ˆ์„ ๊ทผ์ ‘ ํ‘œ๋ฉด ์›๊ฒฉ ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ๊ด€์ธกํ•˜๊ณ , ์œ„์„ฑ NDVI ๊ธฐ๋ฐ˜์˜ ์‹๋ฌผ ๊ณ„์ ˆ๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋‹ค์ฑ„๋„ ๋ถ„๊ด‘๊ณ„๋ฅผ ์ƒ๋ถ€ ์‹์ƒ์˜ ์œ„์™€ ์•„๋ž˜์— ์„ค์น˜ํ•จ์œผ๋กœ์จ, ์ƒ๋ถ€ ์‹์ƒ๊ณผ ํ•˜๋ถ€ ์‹์ƒ์˜ ์‹๋ฌผ ๊ณ„์ ˆ์„ ๊ฐ๊ฐ ์—ฐ์†์ ์œผ๋กœ ๊ด€์ธกํ•˜์˜€๋‹ค. ์‹๋ฌผ ๊ณ„์ ˆ์„ ํƒ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•์ธ 1) ์—ญ์น˜๋ฅผ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ 2) ์ด๊ณ„๋„ํ•จ์ˆ˜๋ฅผ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐœ์—ฝ ์‹œ๊ธฐ ๋ฐ ์„ฑ์ˆ™ ์‹œ๊ธฐ๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ์ด๋ฅผ ๋‹ค์ธต ์‹์ƒ์˜ ์‹๋ฌผ ๊ณ„์ ˆ๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ฒซ๋ฒˆ์งธ๋กœ, ๊ตฐ๋ฝ์˜ ์ƒ์ธต๋ถ€์—์„œ ์‹ค์ธกํ•œ NDVI์™€ ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ NDVI๊ฐ€ ๊ฐ•ํ•œ ์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค (R2=0.95 ๋Š” MODIS ์˜์ƒ๋“ค ๋ฐ R2= 0.85 ๋Š” Landsat8). ๋‘๋ฒˆ์งธ๋กœ, ์ด๊ณ„๋„ํ•จ์ˆ˜ ๋ฐฉ๋ฒ•๊ณผ 10%์˜ ์—ญ์น˜ ๊ฐ’์„ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•์ด ๋น„์Šทํ•œ ๊ฐœ์—ฝ ์‹œ๊ธฐ๋ฅผ ์ถ”์ •ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ํ•˜๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•œ ์‹œ๊ธฐ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ, ์ด๊ณ„๋„ํ•จ์ˆ˜ ๋ฐฉ๋ฒ•๊ณผ 90%์˜ ์—ญ์น˜ ๊ฐ’์„ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•์ด ๋น„์Šทํ•œ ์„ฑ์ˆ™ ์‹œ๊ธฐ๋ฅผ ์‚ฐ์ถœํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” ์ƒ๋ถ€ ์‹์ƒ์˜ ์„ฑ์ˆ™ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•˜์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ์ƒ๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ์™€ ํ•˜๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ๊ฐ€ ์˜จ๋„์™€ ๋ฐ˜์‘ํ•˜๋Š” ์ •๋„๊ฐ€ ๋šœ๋ ทํ•˜๊ฒŒ ์ฐจ์ด๊ฐ€ ๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ƒ๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ๋Š” ์ ์‚ฐ ์ƒ์žฅ ์˜จ๋„ ์ผ์ˆ˜ (AGDD)์™€ ๊ฐ•ํ•œ ์ƒ๊ด€์„ฑ์„ ๋ณด์˜€๊ณ , ํ•˜๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ๋Š” AGDD์™€ ์—ฐ๊ด€์„ฑ์„ ๊ฐ–๊ณ  ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ถ”์œ„ ์ผ์ˆ˜(NCD)์—๋„ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์œ„์„ฑ NDVI ๊ธฐ๋ฐ˜์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ๋Š” ํ•˜๋ถ€ ์‹์ƒ์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ์™€ ์—ฐ๊ด€์„ฑ์ด ๋†’๊ณ , ์„ฑ์ˆ™ ์‹œ๊ธฐ๋Š” ์ƒ๋ถ€ ์‹์ƒ์˜ ์„ฑ์ˆ™ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ, ์ƒ๋ถ€ ์‹์ƒ๊ณผ ํ•˜๋ถ€ ์‹์ƒ์ด ์˜จ๋„์— ๋‹ค๋ฅธ ๋ฏผ๊ฐ์„ฑ์„ ๊ฐ–๊ณ  ์žˆ์–ด, ์œ„์„ฑ์—์„œ ์‚ฐ์ถœ๋œ ์‹๋ฌผ ๊ณ„์ ˆ์„ ์ด์šฉํ•˜์—ฌ ๊ธฐํ›„๋ณ€ํ™”๋ฅผ ์ดํ•ดํ•˜๊ณ ์ž ํ•  ๋•Œ, ์–ด๋–ค ์ธต์˜ ์‹์ƒ์ด ์œ„์„ฑ ์˜์ƒ์— ์ฃผ๋œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์œ„์„ฑ์€ ๋„“์€ ์ง€์—ญ์˜ ๋ณ€ํ™”๋ฅผ ์†์‰ฝ๊ฒŒ ๋ชจ๋‹ˆํ„ฐ๋งํ•  ์ˆ˜ ์žˆ์–ด ๋งŽ์€ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ–๊ณ  ์žˆ๋Š” ๋„๊ตฌ์ด์ง€๋งŒ, ๋ณด๋‹ค ์ •ํ™•ํ•œ ์œ„์„ฑ ๊ด€์ธก ๊ฐ’์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ˜„์žฅ์—์„œ ๊ด€์ธก๋œ ์ž๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ฒ€์ฆ์ด ์š”๊ตฌ๋œ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” 1) ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœ, 2) ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ํ™œ์šฉํ•œ ์‹์ƒ์˜ ์ƒ๋ฆฌํ•™์  ๊ตฌ์กฐ์  ๋ณ€ํ™”์˜ ์ง€์†์ ์ธ ๊ด€์ธก, 3) ๋‹ค์ธต ์‹์ƒ ๊ตฌ์กฐ์—์„œ ๊ด€์ธก๋˜๋Š” ์‹๋ฌผ ๊ณ„์ ˆ ๋ฐ ์œ„์„ฑ์—์„œ ์ถ”์ •๋œ ์‹๋ฌผ ๊ณ„์ ˆ์˜ ์—ฐ๊ด€์„ฑ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์„œ๋Š” ์ƒ์—… ์„ผ์„œ๋“ค๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ๊ฐ€๊ฒฉ์ ์œผ๋กœ ์ €๋ ดํ•˜๊ณ  ์† ์‰ฝ๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์„ฑ๋Šฅ์ ์œผ๋กœ๋„ ๋ถ€์กฑํ•จ์ด ์—†์—ˆ๋‹ค. ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ SIF๋ฅผ ์˜จ๋Œ€ ์นจ์—ฝ์ˆ˜๋ฆผ์—์„œ ์ง€์†์ ์œผ๋กœ ๊ด€์ธกํ•œ ๊ฒฐ๊ณผ, ์ด์ผ์ฐจ์ƒ์‚ฐ์„ฑ๊ณผ SIF๋Š” ๋น„์„ ํ˜• ๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ๋งŽ์€ ์„ ํ–‰ ์—ฐ๊ตฌ๋“ค์—์„œ ๋ฐœํ‘œํ•œ ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ SIF์™€ GPP๊ฐ€ ์„ ํ˜•์ ์ธ ๊ด€๊ณ„๋ฅผ ๋ณด์ธ๋‹ค๋Š” ๊ฒƒ๊ณผ๋Š” ๋‹ค์†Œ ์ƒ๋ฐ˜๋œ ๊ฒฐ๊ณผ์ด๋‹ค. ๋‹ค์ธก ์‹์ƒ์˜ ๋ด„์ฒ  ์‹๋ฌผ ๊ณ„์ ˆ์„ ์—ฐ์†์ ์œผ๋กœ ๊ด€์ธกํ•˜๊ณ , ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ ์‹๋ฌผ ๊ณ„์ ˆ๊ณผ ๋น„๊ตํ‰๊ฐ€ํ•œ ์—ฐ๊ตฌ์—์„œ๋Š” ์œ„์„ฑ ๊ธฐ๋ฐ˜์˜ ๊ฐœ์—ฝ ์‹œ๊ธฐ๋Š” ํ•˜๋ถ€ ์‹์ƒ์— ์˜ํ–ฅ์„ ์ฃผ๋กœ ๋ฐ›๊ณ , ์„ฑ์ˆ™ ์‹œ๊ธฐ๋Š” ์ƒ๋ถ€ ์‹์ƒ์˜ ์‹œ๊ธฐ์™€ ๋น„์Šทํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฆ‰, ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ํ˜„์žฅ์—์„œ ์‹ค์ธกํ•œ ๊ฒฐ๊ณผ๋Š” ์œ„์„ฑ ์˜์ƒ์„ ํ™œ์šฉํ•œ ์—ฐ๊ตฌ๋“ค๊ณผ๋Š” ๋‹ค๋ฅธ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ผ ์ˆ˜๋„ ์žˆ์œผ๋ฉฐ, ์œ„์„ฑ ์˜์ƒ์„ ํ‰๊ฐ€ ๋ฐ ์ดํ•ดํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณด๋‹ค ์ •ํ™•ํ•œ ์‹์ƒ์˜ ๊ตฌ์กฐ์ , ์ƒ๋ฆฌํ•™์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทผ์ ‘ ํ‘œ๋ฉด ์„ผ์‹ฑ์„ ํ™œ์šฉํ•œ ํ˜„์žฅ์—์„œ ๊ตฌ์ถ•ํ•œ ์ž๋ฃŒ ๊ธฐ๋ฐ˜์˜ ๋” ๋งŽ์€ ์—ฐ๊ตฌ๋“ค์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค.Abstract i Chapter 1. Introduction 2 1. Background 2 2. Purpose 5 Chapter 2. Monitoring SIF using a filter-based near surface remote sensing system 9 1. Introduction 9 2. Instrument desing and technical spefications of the filter-based smart surface sensing system (4S-SIF) 12 2.1. Ultra-narrow band pass filter 14 2.2. Calibration of 4S-SIF 15 2.3. Temperature and humidity response 16 2.4. Evaluate SIF quality from 4S-SIF in the field 17 3. Results 20 4. Discussion 23 Chapter 3. SIF is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during fall transition 27 1. Introduction 27 2. Methods and Materials 31 2.1. Study site 31 2.2. Leaf-level fluorescence measurement 32 2.3. Canopy-level SIF and spectral reflectance measurement 34 2.4. SIF retrieval 37 2.5. Canopy-level photosynthesis estimates 38 2.6. Meteorological variables and APAR 39 2.7. Statistical analysis 40 3. Results 41 4. Discussion 48 4.1. Non-linear relationships between SIF and GPP 49 4.2. Role of f_esc in SIF-GPP relationship 53 4.3. Implications of non-linear SIF-GPP relationship in temperate ENF 54 5. Conclusion 57 6. Appendix 59 Chapter 4. Monitoring spring phenology of multi-layer canopy in a deciduous broadleaf forest: What signal do satellites actually see in space 65 1. Introduction 65 2. Materials and Methods 69 2.1. Study site 69 2.2. Multi-layer spectral reflectance and transmittance measurement 70 2.3. Phenometrics detection 72 2.4. In-situ multi-layer phenology 74 2.5. Satellite remote sensing data 75 2.6. Meteorological variables 75 3. Results 76 3.1. Seasonal to interannual variations of NDVI, 1-transmittance, and air temperature 76 3.2. Inter-annual variation of leaf-out and full-leaf dates 78 3.3. The relationships between dates calculated according tothreshold and in-situ multi-layer phenology 80 3.4. The relationship between multi-layer phenology, AGDD and NCD 81 4. Discussion 82 4.1. How do satellite-based leaf-out and full-leaf dates differ from in-situ multi-layer phenology 83 4.2. Are the 10 % and 90 % thresholds from satellite-basedNDVI always well matched with the leaf-out and full-leaf dates calculated by the curvature change rate 86 4.3. What are the implications of the difference between satellite-based and multi-layer phenology 87 4.4. Limitations and implications for future studies 89 5. Conclusion 91 6. Appendix 92 Chapter 5. Conclusion 114 Abstract in Korean 115๋ฐ•
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