1,796 research outputs found

    Do daily and seasonal trends in leaf solar induced fluorescence reflect changes in photosynthesis, growth or light exposure?

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    Solar induced chlorophyll fluorescence (SIF) emissions of photosynthetically active plants retrieved from space-borne observations have been used to improve models of global primary productivity. However, the relationship between SIF and photosynthesis in diurnal and seasonal cycles is still not fully understood, especially at large spatial scales, where direct measurements of photosynthesis are unfeasible. Motivated by up-scaling potential, this study examined the diurnal and seasonal relationship between SIF and photosynthetic parameters measured at the level of individual leaves. We monitored SIF in two plant species, avocado (Persea Americana) and orange jasmine (Murraya paniculatta), throughout 18 diurnal cycles during the Southern Hemisphere spring, summer and autumn, and compared them with simultaneous measurements of photosynthetic yields, and leaf and global irradiances. Results showed that at seasonal time scales SIF is principally correlated with changes in leaf irradiance, electron transport rates (ETR) and constitutive heat dissipation (YNO; p \u3c 0.001). Multiple regression models of correlations between photosynthetic parameters and SIF at diurnal time scales identified leaf irradiance as the principle predictor of SIF (p \u3c 0.001). Previous studies have identified correlations between photosynthetic yields, ETR and SIF at larger spatial scales, where heterogeneous canopy architecture and landscape spatial patterns influence the spectral and photosynthetic measurements. Although this study found a significant correlation between leaf-measured YNO and SIF, future dedicated up-scaling experiments are required to elucidate if these observations are also found at larger spatial scales

    Tracking Seasonal and Interannual Variability in Photosynthetic Downregulation in Response to Water Stress at a Temperate Deciduous Forest

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    The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a wellโ€characterized eddy covariance site in a droughtโ€prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescenceโ€photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ฯˆ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solarโ€induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experimentโ€2 (GOMEโ€2). The modeled and satelliteโ€observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOMEโ€2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy โ€œgreenness.โ€ However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOMEโ€2 footprint. Higherโ€resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of lateโ€season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future

    Measuring the dynamic photosynthome

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    Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The โ€˜dynamicโ€™ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal โ€œmismatchโ€ between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of โ€˜phenomicsโ€™ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the โ€˜photosynthomeโ€™. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved

    Performance of solar-induced chlorophyll fluorescence in estimating water-use efficiency in a temperate forest

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    ยฉ The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 10 (2018): 796, doi:10.3390/rs10050796.Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.This research was supported by U.S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI 959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to Jianwu Tang. This study was also supported by the open project grant (LBKF201701) of Key Laboratory of Land Surface Pattern and Simulation, Chinese Academy of Sciences

    Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees

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    Tree species in the boreal forest cycle between periods of active growth and dormancy alter their photosynthetic processes in response to changing environmental conditions. For deciduous species, these changes are readily visible, while evergreen species have subtler foliar changes during seasonal transitions. In this study, we used remotely sensed optical indices to observe seasonal changes in photosynthetic activity, or photosynthetic phenology, of six boreal tree species. We evaluated the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), the chlorophyll/carotenoid index (CCI), and steady-state chlorophyll fluorescence (FS) as a measure of solar-induced fluorescence (SIF), and compared these optical metrics to gas exchange to determine their efficacy in detecting seasonal changes in plant photosynthetic activity. The NDVI and PRI exhibited complementary responses. The NDVI paralleled photosynthetic phenology in deciduous species, but not in evergreens. The PRI closely paralleled photosynthetic activity in evergreens, but less so in deciduous species. The CCI and FS tracked photosynthetic phenology in both deciduous and evergreen species. The seasonal patterns of optical metrics and photosynthetic activity revealed subtle differences across and within functional groups. With the CCI and fluorescence becoming available from satellite sensors, they offer new opportunities for assessing photosynthetic phenology, particularly for evergreen species, which have been difficult to assess with previous methods

    Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at Integrated Carbon Observatory System (ICOS) ecosystem flux sites

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    An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrialโ€“atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80โ€‰% of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75โ€‰% of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.</p

    ๊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•

    Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy

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    Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZ

    From the Ground to Space: Using Solar-Induced Chlorophyll Fluorescence to Estimate Crop Productivity

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    Timely and accurate monitoring of crops is essential for food security. Here, we examine how well solarโ€induced chlorophyll fluorescence (SIF) can inform crop productivity across the United States. Based on towerโ€level observations and processโ€based modeling, we find highly linear gross primary production (GPP):SIF relationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4 crops yield higher GPP:SIF ratios (30โ€“50%) primarily because SIF is most sensitive to the light reactions (does not account for photorespiration). Scaling to the satellite, we compare SIF from the TROPOspheric Monitoring Instrument (TROPOMI) against towerโ€derived GPP and countyโ€level crop statistics. Temporally, TROPOMI SIF strongly agrees with GPP observations upscaled across a corn and soybean dominated cropland (Rยฒ = 0.89). Spatially, countyโ€level TROPOMI SIF correlates with crop productivity (Rยฒ = 0.72; 0.86 when accounting for planted area and C3/C4 contributions), highlighting the potential of SIF for reliable crop monitoring
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