389 research outputs found

    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

    ๊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•

    Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

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    Remote sensing methods enable detection of solar-induced chlorophyll a fluorescence. However, to unleash the full potential of this signal, intensive cross-disciplinary work is required to harmonize biophysical and ecophysiological studies. For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.Peer reviewe

    Remote sensing of photosynthetic-light-use efficiency

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    Correction of PRI for carotenoid pigment pools improves photosynthesis estimation across different irradiance and temperature conditions

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    We studied the influence of changing carotenoid pigments on the sensitivity of the photochemical reflectance index (PRI) to photosynthesis dynamics. The goal of the measurements was to examine how the introduction of ฮ”PRI into the working dataset can improve the estimation of photosynthesis. Spectral and photosynthetic characteristics of European beech and Norway spruce saplings were periodically measured in growth chambers with an adjustable irradiance and temperature. Patterns of environmental changes inside the growth chambers were created by periodic changes in irradiance and temperature. Four general irradiance periods lasting 10-12โ€ฏdays each were established. Introduced irradiance regimes varied in the sum of daily irradiance and amplitude of irradiance changes. Temperature was changed with more complex patterns to induce changes in xanthophyll cycle pigments at various time scales within these regimes. Our measurements confirmed the PRI linkage to photosynthetic light use efficiency (LUE). However, the strength of this connection was found to be dependent on changing pigment concentrations, specifically on the change in the ratio of chlorophylls to carotenoids. Furthermore, a negative interference in photosynthesis estimation from PRI was recorded if the temperature was lowered overnight to 12โ€ฏยฐC. The differential PRI (ฮ”PRI), calculated as the simple difference between the PRI value measured during the daytime period and in early morning (PRI0), revealed a decreased effect from pigments and cold temperature on LUE estimation. The regression analysis among all measured data identified an increased association between PRI and LUE following the introduction of ฮ”PRI from R2โ€ฏ=โ€ฏ0.26 to 0.69 in beech and from R2โ€ฏ=โ€ฏ0.61 to 0.77 in spruce data. The analyses showed that both leaf carotenoid concentrations and the conversion state of xanthophyll cycle pigments played a significant role in determining PRI and PRI0 values and that the accurate assessment of these pigments in PRI across multiple levels of stress from irradiance and temperature might improve estimations of LUE through ฮ”PRI. In our data, ฮ”PRI appeared to be a good measure of photosynthesis, the dynamics of which differed between beech and spruce saplings upon switching temperatures

    In situ measurement of Scots pine needle PRI

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    Background: The Photochemical Reflectance Index (PRI) calculated from narrow-band spectral reflectance data is a vegetation index which is increasingly used as an indicator of photosynthetic activity. The leaf-level link between the status of photosynthetic apparatus and PRI has been robustly established under controlled light conditions. However, when a whole canopy is measured instantaneously, the PRI signal is heavily modified by vegetation structure and local variations in incident light conditions. To apply PRI for monitoring the photosynthesis of whole canopies under natural conditions, these large-scale measurements need to be validated against simultaneous leaf PRI. Unfortunately, PRI changes dynamically with incident light and has a large natural variation. No generally accepted procedure exists today for determining the PRI of canopy elements in situ. Results: We present a successful procedure for in situ measurements of needle PRI. We describe, characterize and test an optical measurement protocol and demonstrate its applicability in field conditions. The measurement apparatus consisted of a light source, needle clip, spectroradiometer and a controlling computer. The light level inside the clip was approximately two-thirds of that on sunlit needle surfaces at midday. During each measurement the needle was inserted into the clip for approximately 5 s. We found no near-instantaneous changes (sub-second scale jumps) in PRI during the measurements. The time constants for PRI variation in light to full shade acclimations were approximately 10 s. The procedure was successfully applied to monitor the greening-up of Scots pine trees. We detected both facultative (diurnal) PRI changes of 0.02 (unitless) and constitutive (seasonal) variations of 0.1. In order to reliably detect the facultative PRI change of 0.02, 20 needles need to be sampled from both sunlit and shaded locations. Conclusions: We established a robust procedure for irradiance-dependent leaf (needle) PRI measurements, facilitating empirical scaling of PRI from leaf (needle) to full canopy level and the application of PRI to monitoring the changes in highly structured vegetation. The measured time constants, and facultative and constitutive PRI variations support the use of an artificial light for in situ PRI measurements at leaf (needle) level.Peer reviewe

    In situ measurement of Scots pine needle PRI

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    Abstract Background The Photochemical Reflectance Index (PRI) calculated from narrow-band spectral reflectance data is a vegetation index which is increasingly used as an indicator of photosynthetic activity. The leaf-level link between the status of photosynthetic apparatus and PRI has been robustly established under controlled light conditions. However, when a whole canopy is measured instantaneously, the PRI signal is heavily modified by vegetation structure and local variations in incident light conditions. To apply PRI for monitoring the photosynthesis of whole canopies under natural conditions, these large-scale measurements need to be validated against simultaneous leaf PRI. Unfortunately, PRI changes dynamically with incident light and has a large natural variation. No generally accepted procedure exists today for determining the PRI of canopy elements in situ. Results We present a successful procedure for in situ measurements of needle PRI. We describe, characterize and test an optical measurement protocol and demonstrate its applicability in field conditions. The measurement apparatus consisted of a light source, needle clip, spectroradiometer and a controlling computer. The light level inside the clip was approximately two-thirds of that on sunlit needle surfaces at midday. During each measurement the needle was inserted into the clip for approximately 5 s. We found no near-instantaneous changes (sub-second scale jumps) in PRI during the measurements. The time constants for PRI variation in light to full shade acclimations were approximately 10ย s. The procedure was successfully applied to monitor the greening-up of Scots pine trees. We detected both facultative (diurnal) PRI changes of 0.02 (unitless) and constitutive (seasonal) variations of 0.1. In order to reliably detect the facultative PRI change of 0.02, 20 needles need to be sampled from both sunlit and shaded locations. Conclusions We established a robust procedure for irradiance-dependent leaf (needle) PRI measurements, facilitating empirical scaling of PRI from leaf (needle) to full canopy level and the application of PRI to monitoring the changes in highly structured vegetation. The measured time constants, and facultative and constitutive PRI variations support the use of an artificial light for in situ PRI measurements at leaf (needle) level

    Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence

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    State-of-the-art optical remote sensing of vegetation canopies is reviewed here to stimulate support from laboratory and field plant research. This overview of recent satellite spectral sensors and the methods used to retrieve remotely quantitative biophysical and biochemical characteristics of vegetation canopies shows that there have been substantial advances in optical remote sensing over the past few decades. Nevertheless, adaptation and transfer of currently available fluorometric methods aboard air- and space-borne platforms can help to eliminate errors and uncertainties in recent remote sensing data interpretation. With this perspective, red and blue-green fluorescence emission as measured in the laboratory and field is reviewed. Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics and primary production on a large scale. The review summarizes several scientific challenges that still need to be resolved to achieve operational fluorescence based remote sensing approache
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