31 research outputs found
Ground-Based Measurements and Validation Protocols for Flex
The upcoming ESA Fluorescence Explorer (FLEX) mission will incorporate ground-based validations for fluorescence parameters and reflectance indices, drawing on an international network of sensors located at eddy covariance tower sites. A program has been initiated by the OPTIMISE program to develop methods and protocols for this network. A sensor system suite under evaluation by OPTIMISE includes the FLoX hyperspectral spectroradiometers. The NASA team at GSFC is participating in this experiment and we report first results from the 2017 summer measurements made above the canopy at the USDA/ARS Beltsville cornfield using the DFLoX and two other leaf-level measurement systems, the MONI-PAM and the FluoWat
The Photochemical Reflectance Index from Directional Cornfield Reflectances: Observations and Simulations
The two-layer Markov chain Analytical Canopy Reflectance Model (ACRM) was linked with in situ hyperspectral leaf optical properties to simulate the Photochemical Reflectance Index (PRI) for a corn crop canopy at three different growth stages. This is an extended study after a successful demonstration of PRI simulations for a cornfield previously conducted at an early vegetative growth stage. Consistent with previous in situ studies, sunlit leaves exhibited lower PRI values than shaded leaves. Since sunlit (shaded) foliage dominates the canopy in the reflectance hotspot (coldspot), the canopy PRI derived from field hyperspectral observations displayed sensitivity to both view zenith angle and relative azimuth angle at all growth stages. Consequently, sunlit and shaded canopy sectors were most differentiated when viewed along the azimuth matching the solar principal plane. These directional PRI responses associated with sunlit/shaded foliage were successfully reproduced by the ACRM. As before, the simulated PRI values from the current study were closer to in situ values when both sunlit and shaded leaves were utilized as model input data in a two-layer mode, instead of a one-layer mode with sunlit leaves only. Model performance as judged by correlation between in situ and simulated values was strongest for the mature corn crop (r = 0.87, RMSE = 0.0048), followed by the early vegetative stage (r = 0.78; RMSE = 0.0051) and the early senescent stage (r = 0.65; RMSE = 0.0104). Since the benefit of including shaded leaves in the scheme varied across different growth stages, a further analysis was conducted to investigate how variable fractions of sunlit/shaded leaves affect the canopy PRI values expected for a cornfield, with implications for 20 remote sensing monitoring options. Simulations of the sunlit to shaded canopy ratio near 50/50 +/- 10 (e.g., 60/40) matching field observations at all growth stages were examined. Our results suggest in the importance of the sunlit/shaded fraction and canopy structure in understanding and interpreting PRI
Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy
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
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κ³Όνλν νλκ³Όμ λλ¦ΌκΈ°μν, 2022. 8. λ₯μλ ¬.Hyperspectral remote sensing is becoming a powerful tool for monitoring vegetation structure and functions. Especially, Sun-Induced chlorophyll fluorescence (SIF) and canopy reflectance monitoring have been widely used to understand physiological and structural changes in plants, and field spectroscopy has become established as an important technique for providing high spectral-, temporal resolution in-situ data as well as providing a means of scaling-up measurements from small areas to large areas. Recently, several tower-based remote sensing systems have been developed. However, in-situ studies have only monitored either BRF or BHR and there is still a lack of understanding of the geometric and optical differences in remote sensing observations, particularly between hemispheric-conical and bi-hemispheric configurations.
Here, we developed an automated ground-based field spectroscopy system measuring far-red SIF and canopy hyperspectral reflectance (400β900β―nm) with hemispherical-conical as well as bi-hemispherical configuration. To measure both bi-hemispherical and hemispherical-conical reflectance, we adopted a rotating prism by using a servo motor to face three types of ports that measure incoming-, outgoing irradiance and outgoing radiance. A white diffuse glass and collimating lens were used to measure the irradiance, and a collimating lens was used to measure the radiance with a field of view of 20 degrees. Additionally, we developed data management protocol that includes radiometric-, and wavelength calibrations. Finally, we report how BRF and BHR data differ in this system and investigated SIF and vegetation index from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in the growing seasons of a deciduous broad-leaved forests.μ΄ λΆκ΄ μ격 κ°μ§λ μμ ꡬ쑰μ κΈ°λ₯μ λͺ¨λν°λ§νλ κ°λ ₯ν λκ΅¬κ° λκ³ μλ€. νΉν, μλ¬Όμ μ리μ , ꡬ쑰μ λ³νλ₯Ό μ΄ν΄νκΈ° μν΄ νμκ΄ μ λ μ½λ‘μ νκ΄ (SIF)κ³Ό μΊλ
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ΈνΌ μ΄ λΆκ΄ λ°μ¬μ¨(400β900nm)μ μΈ‘μ νλ μλνλ μ§μ κΈ°λ° νλ λΆκ΄ μμ€ν
μ κ°λ°νλ€. μλ°©ν₯ λ°μ¬μ¨κ³Ό λ°κ΅¬ν μμΆν λ°μ¬μ¨μ λͺ¨λ μΈ‘μ νκΈ° μν΄ μ보 λͺ¨ν°λ₯Ό μ¬μ©νμ¬ ν리μ¦μ νμ νμ¬ μΈκ°μ§ νμ
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μμ μΈ‘μ λ BRFμ BHR λ°μ΄ν°κ° μ΄λ»κ² λ€λ₯Έμ§ λ³΄κ³ νμλ€.Chapter 1. Introduction οΌ
1.1. Study Background οΌ
1.2. Purpose of Research οΌ
Chapter 2. Developing and Testing of Hyperspectral System οΌ
2.1 Development of Hyperspectral System and Data Collecting οΌ
2.1.1 The Central Control Unit and Spectrometer οΌ
2.1.2 RotaPrism οΌ
2.1.3 Data Collection οΌ
2.3 Data Managing and Processing οΌοΌ
2.3.1 Preprocessing of Spectra οΌοΌ
2.3.2 Radiometric Calibration οΌοΌ
2.3.3 Retrieval of SIF and Vegetation Indices οΌοΌ
2.4 Ancillary Measurements to Monitoring Ecosystem. οΌοΌ
Chapter 3. Application of Hyperspectral System οΌοΌ
3.1 Study Site οΌοΌ
3.2 Diurnal and Variation of Spectral Reflectance and SIF οΌοΌ
3.3 Seasonal Variation of Vegetation Index and SIF οΌοΌ
3.4 Broader Implications οΌοΌ
Chapter 4. Summary and Conclusions οΌοΌ
Bibliography οΌοΌμ
Do all chlorophyll fluorescence emission wavelengths capture the spring recovery of photosynthesis in boreal evergreen foliage?
Chlorophyll a fluorescence (ChlF) is closely related to photosynthesis and can be measured remotely using multiple spectral features as solar-induced fluorescence (SIF). In boreal regions, SIF shows particular promise as an indicator of photosynthesis, in part because of the limited variation of seasonal light absorption in these ecosystems. Seasonal spectral changes in ChlF could yield new information on processes such as sustained nonphotochemical quenching (NPQ(S)) but also disrupt the relationship between SIF and photosynthesis. We followed ChlF and functional and biochemical properties of Pinus sylvestris needles during the photosynthetic spring recovery period to answer the following: (a) How ChlF spectra change over seasonal timescales? (b) How pigments, NPQ(S), and total photosynthetically active radiation (PAR) absorption drive changes of ChlF spectra? (c) Do all ChlF wavelengths track photosynthetic seasonality? We found seasonal ChlF variation in the red and far-red wavelengths, which was strongly correlated with NPQ(S), carotenoid content, and photosynthesis (enhanced in the red), but not with PAR absorption. Furthermore, a rapid decrease in red/far-red ChlF ratio occurred in response to a cold spell, potentially relating to the structural reorganization of the photosystems. We conclude that all current SIF retrieval features can track seasonal photosynthetic dynamics in boreal evergreens, but the full SIF spectra provides additional insight.Peer reviewe
Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales
There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling
CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands
The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : νκ²½λνμ νλκ³Όμ μ‘°κ²½ν, 2022.2. λ₯μλ ¬.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 (Ξ¦_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 Ξ¦_F to air temperature. While LUE_p reached saturation at high air temperatures, Ξ¦_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Ξ¦_F (R2 = 0.84) implying that Ξ¦_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.μλ¬Ό κ³μ λ° μμμ μ리νμ , ꡬ쑰μ μΈ λ³νλ₯Ό μ§μμ μΌλ‘ λͺ¨λν°λ§ νλ κ²μ μ‘μμνκ³μ λκΈ°κΆ μ¬μ΄μ μλμ§, νμ μν λ±μ νΌλλ°±μ μ΄ν΄νλλ° νμμ μ΄λ€. μ΄λ₯Ό κ΄μΈ‘νκΈ° μνμ¬ μμ±μμ κ΄μΈ‘λ μ κ·ν μμ μ§μ (NDVI) νμ μ λ μ½λ‘μ νκ΄λ¬Όμ§ (SIF)λ λμ€μ μΌλ‘ μ¬μ©λκ³ μλ€. κ·Έλ¬λ, μ°μ£Ό μμ± κΈ°λ°μ μλ£λ λ€μκ³Ό κ°μ νκ³μ λ€μ΄ μ‘΄μ¬νλ€. 1) μμ§κΉμ§ κ³ ν΄μλμ μμ± κΈ°λ° SIF μλ£λ μκ³ , 2) μμ± μλ£λ€μ λκΈ°μ μ§ (μ, ꡬλ¦)μ μν₯μ λ°μ, νλ¦° λ μ μμμ μ리νμ , ꡬ쑰μ λ³νλ₯Ό νμ§νκΈ° νλ€λ€. λν, 3) μμ± μ΄λ―Έμ§λ μλΆ μμκ³Ό νλΆ μμμ΄ νΌν©λμ΄ μμΈ μ νΈλ₯Ό νμ§νκΈ° λλ¬Έμ, κ° μΈ΅μ μλ¬Ό κ³μ μ κ°κ° μ°κ΅¬νκΈ°μ μ΄λ €μμ΄ μλ€. κ·Έλ¬λ―λ‘, μμ±μμ νμ§ν μ νΈλ₯Ό νκ°νκ³ , μμμ μ리νμ , ꡬ쑰μ λ³νλ₯Ό λ³΄λ€ μ νν μ΄ν΄νκΈ° μν΄μλ κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
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(Smart Surface Sensing System, 4S-SIF)μ κ°λ°νμλ€. μΌμλ λμ νν°λ€κ³Ό ν¬ν λ€μ΄μ€λκ° κ²°ν©λμ΄ μμΌλ©°, μ보 λͺ¨ν°λ₯Ό μ¬μ©νμ¬ λμ νν° λ° κ΄μΈ‘ λ°©ν₯μ μλμ μΌλ‘ λ³κ²½ν¨μΌλ‘μ¨, ν κ°μ ν¬ν λ€μ΄μ€λκ° 3κ°μ νμ₯ λ²μ(757, 760, 770 nm)μ λΉ λ° νμμΌλ‘λΆν° μ
μ¬λλ κ΄λκ³Ό μμμΌλ‘ λ°μ¬/λ°©μΆλ κ΄λμ κ΄μΈ‘ν μ μλλ‘ κ³ μλμλ€. ν¬ν λ€μ΄μ€λλ‘λΆν° μΈμλ λμ§νΈ μμΉ κ°μ μμ
μ μΌλ‘ ν맀λλ μ΄κ³ ν΄μλ λΆκ΄κ³(QE Pro, Ocean Insight)μ λλ ·ν μ ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμλ€ (R2 > 0.99). μΆκ°μ μΌλ‘, 4S-SIFμμ κ΄μΈ‘λ SIFμ μ΄κ³ ν΄μλ λΆκ΄κ³λ₯Ό μ΄μ©νμ¬ μΆμΆν SIFκ° μ νμ μΈ κ΄κ³λ₯Ό μ΄λ£¨λ κ²μ νμΈνμλ€. μμμ μ리νμ λ³νλ₯Ό μΌμΌν€λ νν λ¬Όμ§μΈ DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)μ μ²λ¦¬νμμλ λΆκ΅¬νκ³ , μ°μΆλ SIFλ€μ μ ν κ΄κ³λ₯Ό 보μλ€. λ³Έ μΌμλ κΈ°μ‘΄ μμ€ν
λ€μ λΉν΄ μ¬μ©νκΈ° μ½κ³ κ°λ¨νλ©°, μ λ ΄νκΈ° λλ¬Έμ λ€μν μ곡κ°μ μ€μΌμΌμ SIFλ₯Ό κ΄μΈ‘ν μ μλ€λ μ₯μ μ΄ μλ€.
μμ± κΈ°λ°μ SIFλ₯Ό μ΄μ©νμ¬ μ΄μΌμ°¨μμ°μ±(gross primary productivity, GPP)μ μΆμ νλ μ°κ΅¬λ μ΅κ·Ό νμ μν μ°κ΅¬ λΆμΌμμ κ°κ΄λ°κ³ μλ μ°κ΅¬ μ£Όμ μ΄λ€. κ·Έλ¬λ, SIFμ GPPμ κ΄κ³λ μ¬μ ν λ§μ λΆνμ€μ±μ μ§λκ³ μλλ°, μ΄λ SIF-GPP κ΄κ³λ₯Ό μ‘°μ νλ μμμ ꡬ쑰μ λ° μ리νμ μμΈμ λ°λ‘ λΆλ¦¬νμ¬ κ³ μ°°ν μ°κ΅¬λ€μ΄ λΆμ‘±νκΈ° λλ¬Έμ΄λ€. λ³Έ νμ λ
Όλ¬Έμ Chapter 3μμλ μ§μμ μΌλ‘ SIF, GPP, ν‘μλ κ΄ν©μ±μ ν¨λ³΅μ¬λ (absorbed photosynthetically active radiation, APAR), κ·Έλ¦¬κ³ ν΄λ‘λ‘νκ³Ό μΉ΄λ‘ν°λ
Έμ΄λμ λΉμ¨ μΈμ (chlorophyll: carotenoid index, CCI)λ₯Ό μ¨λμΉ¨μ½μλ¦Όμμ μ°μμ μΌλ‘ κ΄μΈ‘νμλ€. SIF-GPP κ΄κ³μ ꡬ체μ μΈ λ©μ»€λμ¦ κ΄κ³λ₯Ό λ°νκΈ° μνμ¬, κ΄ μ΄μ©ν¨μ¨ (light use efficiency, LUE_p), μ½λ‘μ νκ΄ μλλ₯ (chlorophyll fluorescence yield, Ξ¦_F) κ·Έλ¦¬κ³ SIF κ΄μκ° κ΅°λ½μΌλ‘λΆν° λ°©μΆλλ λΉμ¨ (escape fraction, f_esc)μ κ°κ° λμΆνκ³ νꡬνμλ€. SIFμ GPPμ κ΄κ³λ λλ ·ν λΉ μ νμ μΈ κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμΌλ©°(R2 = 0.91 with a hyperbolic regression function, daily), μΌμ£ΌκΈ° λ¨μμμ SIFλ APARμ λν΄ μ νμ μ΄μμ§λ§ GPPλ APARμ λν΄ λλ ·ν ν¬ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμλ€. μΆκ°μ μΌλ‘ LUE_p μ Ξ¦_F κ° λκΈ° μ¨λμ λ°λΌ λ°μνλ μ λκ° λ€λ₯Έ κ²μ νμΈνμλ€. LUE_pλ λμ μ¨λμμ ν¬ν λμμ§λ§, Ξ¦_Fλ ν¬ν ν¨ν΄μ νμΈν μ μμλ€. λν, κ΅°λ½ μμ€μμμ CCIμ Ξ¦_Fκ° λλ ·ν μκ΄ κ΄κ³λ₯Ό 보μλ€(R2 = 0.84). μ΄λ Ξ¦_Fκ° μ½λ‘μ μμμ μν₯μ LUE_pμ λΉν΄ λ κ°ν κ΄κ³κ° μμ μ μμμ μμ¬νλ€. λ§μ§λ§μΌλ‘, f_escκ° νμκ΄μ μ°λλ μ λμ λ°λΌ λ°μμ νμ¬, Ξ¦_Fμ LUE_pμ κ°ν μκ΄ κ΄κ³λ₯Ό νμ±νλλ° κΈ°μ¬νλ κ²μ νμΈνμλ€. μ΄λ¬ν λ°κ²¬μ μ¨λ μΉ¨μ½μλ¦Όμμ κ΅°λ½ μμ€μ SIF-GPPκ΄κ³λ₯Ό μ리νμ λ° κ΅¬μ‘°μ μΈ‘λ©΄μμ μ΄ν΄νκ³ κ·λͺ
νλλ° ν° λμμ΄ λ κ²μ΄λ€.
μλ¬Ό κ³μ μ μμμ΄ μ² μ λ°λΌ μ£ΌκΈ°μ μΌλ‘ λνλ΄λ λ³νλ₯Ό κ΄μΈ‘νλ λ°μμ΄λ€. μλ¬Ό κ³μ μ μ‘μμνκ³μ λκΈ°κΆ μ¬μ΄μ λ¬Όμ§ μνμ μ΄ν΄νλλ° λ§€μ° μ€μνλ€. μμ± κΈ°λ°μ NDVIλ μλ¬Ό κ³μ μ νμ§νκ³ μ°κ΅¬νλλ° κ°μ₯ λμ€μ μΌλ‘ μ¬μ©λλ€. κ·Έλ¬λ, νμ½μλ¦Όμμμ μμ± NDVI κΈ°λ°μ κ°μ½ μκΈ° λ° μ±μ μκΈ°κ° μ€μ μ΄λ μμ μ νμ§νλμ§λ λΆλΆλͺ
νλ€. μ€μ νμ½μλ¦Όμ λ€μΈ΅ μμ ꡬ쑰μ μΌμ°¨μμΌλ‘ μ΄λ£¨μ΄μ Έ μλ λ°λ©΄, μμ± μμμ λ€μΈ΅ μμμ μ νΈκ° μμ¬ μλ μ΄μ°¨μμ κ²°κ³Όλ¬Όμ΄κΈ° λλ¬Έμ΄λ€. λ°λΌμ, μμ± NDVI κΈ°λ°μ μλ¬Ό κ³μ μ΄ λ€μΈ΅ μμ ꡬ쑰λ₯Ό μ΄λ£¨κ³ μλ νμ½μλ¦Όμμ μ€μ νμ₯ κ΄μΈ‘κ³Ό λΉκ΅νμμ λ μ΄λ μμ μ νμ§νλμ§μ λν κΆκΈμ¦μ΄ λ¨λλ€. λ³Έ νμ λ
Όλ¬Έμ Chapter 4μμλ μ§μμ μΌλ‘ 8λ
λμ νμ½μλ¦Όλ΄μ λ€μΈ΅ μμμ μλ¬Ό κ³μ μ κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
μ μ΄μ©νμ¬ κ΄μΈ‘νκ³ , μμ± NDVI κΈ°λ°μ μλ¬Ό κ³μ κ³Ό λΉκ΅νμλ€. λ€μ±λ λΆκ΄κ³λ₯Ό μλΆ μμμ μμ μλμ μ€μΉν¨μΌλ‘μ¨, μλΆ μμκ³Ό νλΆ μμμ μλ¬Ό κ³μ μ κ°κ° μ°μμ μΌλ‘ κ΄μΈ‘νμλ€. μλ¬Ό κ³μ μ νμ§νκΈ° μνμ¬ κ°μ₯ λ§μ΄ μ¬μ©λλ λ°©λ²μΈ 1) μμΉλ₯Ό μ΄μ©νλ λ°©λ²κ³Ό 2) μ΄κ³λν¨μλ₯Ό μ΄μ©νλ λ°©λ²μ μ¬μ©νμ¬ κ°μ½ μκΈ° λ° μ±μ μκΈ°λ₯Ό κ³μ°νκ³ μ΄λ₯Ό λ€μΈ΅ μμμ μλ¬Ό κ³μ κ³Ό λΉκ΅νμλ€. λ³Έ μ°κ΅¬ κ²°κ³Ό, 첫λ²μ§Έλ‘, κ΅°λ½μ μμΈ΅λΆμμ μ€μΈ‘ν NDVIμ μμ± κΈ°λ°μ NDVIκ° κ°ν μ ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνλ€ (R2=0.95 λ MODIS μμλ€ λ° R2= 0.85 λ Landsat8). λλ²μ§Έλ‘, μ΄κ³λν¨μ λ°©λ²κ³Ό 10%μ μμΉ κ°μ μ΄μ©ν λ°©λ²μ΄ λΉμ·ν κ°μ½ μκΈ°λ₯Ό μΆμ νλ κ²μ νμΈνμμΌλ©°, νλΆ μμμ κ°μ½ μκΈ°μ λΉμ·ν μκΈ°μμ νμΈνμλ€. μΈλ²μ§Έλ‘, μ΄κ³λν¨μ λ°©λ²κ³Ό 90%μ μμΉ κ°μ μ΄μ©ν λ°©λ²μ΄ λΉμ·ν μ±μ μκΈ°λ₯Ό μ°μΆνμμΌλ©°, μ΄λ μλΆ μμμ μ±μ μκΈ°μ λΉμ·νμλ€. μΆκ°μ μΌλ‘ μλΆ μμμ κ°μ½ μκΈ°μ νλΆ μμμ κ°μ½ μκΈ°κ° μ¨λμ λ°μνλ μ λκ° λλ ·νκ² μ°¨μ΄κ° λλ κ²μ νμΈν μ μμλ€. μλΆ μμμ κ°μ½ μκΈ°λ μ μ° μμ₯ μ¨λ μΌμ (AGDD)μ κ°ν μκ΄μ±μ 보μκ³ , νλΆ μμμ κ°μ½ μκΈ°λ AGDDμ μ°κ΄μ±μ κ°κ³ μμ λΏλ§ μλλΌ μΆμ μΌμ(NCD)μλ λ―Όκ°νκ² λ°μνλ κ²μ νμΈνμλ€. μ΄λ¬ν κ²°κ³Όλ μμ± NDVI κΈ°λ°μ κ°μ½ μκΈ°λ νλΆ μμμ κ°μ½ μκΈ°μ μ°κ΄μ±μ΄ λκ³ , μ±μ μκΈ°λ μλΆ μμμ μ±μ μκΈ°μ λΉμ·νλ€λ κ²μ μλ―Ένλ€. λν, μλΆ μμκ³Ό νλΆ μμμ΄ μ¨λμ λ€λ₯Έ λ―Όκ°μ±μ κ°κ³ μμ΄, μμ±μμ μ°μΆλ μλ¬Ό κ³μ μ μ΄μ©νμ¬ κΈ°νλ³νλ₯Ό μ΄ν΄νκ³ μ ν λ, μ΄λ€ μΈ΅μ μμμ΄ μμ± μμμ μ£Όλ μν₯μ λ―ΈμΉλμ§ κ³ λ €ν΄μΌ νλ€λ κ²μ μμ¬νλ€.
μμ±μ λμ μ§μμ λ³νλ₯Ό μμ½κ² λͺ¨λν°λ§ν μ μμ΄ λ§μ κ°λ₯μ±μ κ°κ³ μλ λꡬμ΄μ§λ§, λ³΄λ€ μ νν μμ± κ΄μΈ‘ κ°μ μ΄ν΄νκΈ° μν΄μλ νμ₯μμ κ΄μΈ‘λ μλ£λ₯Ό κΈ°λ°μΌλ‘ ν κ²μ¦μ΄ μꡬλλ€. λ³Έ νμ λ
Όλ¬Έμμλ 1) κ·Όμ νλ©΄ μΌμ± μμ€ν
μ κ°λ°, 2) κ·Όμ νλ©΄ μΌμ± μμ€ν
μ νμ©ν μμμ μ리νμ ꡬ쑰μ λ³νμ μ§μμ μΈ κ΄μΈ‘, 3) λ€μΈ΅ μμ ꡬ쑰μμ κ΄μΈ‘λλ μλ¬Ό κ³μ λ° μμ±μμ μΆμ λ μλ¬Ό κ³μ μ μ°κ΄μ± νκ°λ₯Ό μννμλ€. κ°λ°ν κ·Όμ νλ©΄ μΌμλ μμ
μΌμλ€κ³Ό λΉκ΅νμ λ, κ°κ²©μ μΌλ‘ μ λ ΄νκ³ μ μ½κ² μ¬μ©ν μ μμμΌλ©°, μ±λ₯μ μΌλ‘λ λΆμ‘±ν¨μ΄ μμλ€. κ·Όμ νλ©΄ μΌμ± μμ€ν
μ μ΄μ©νμ¬ SIFλ₯Ό μ¨λ μΉ¨μ½μλ¦Όμμ μ§μμ μΌλ‘ κ΄μΈ‘ν κ²°κ³Ό, μ΄μΌμ°¨μμ°μ±κ³Ό SIFλ λΉμ ν κ΄κ³λ₯Ό κ°λ κ²μ νμΈνμλ€. μ΄λ λ§μ μ ν μ°κ΅¬λ€μμ λ°νν μμ± κΈ°λ°μ SIFμ GPPκ° μ νμ μΈ κ΄κ³λ₯Ό 보μΈλ€λ κ²κ³Όλ λ€μ μλ°λ κ²°κ³Όμ΄λ€. λ€μΈ‘ μμμ λ΄μ² μλ¬Ό κ³μ μ μ°μμ μΌλ‘ κ΄μΈ‘νκ³ , μμ± κΈ°λ°μ μλ¬Ό κ³μ κ³Ό λΉκ΅νκ°ν μ°κ΅¬μμλ μμ± κΈ°λ°μ κ°μ½ μκΈ°λ νλΆ μμμ μν₯μ μ£Όλ‘ λ°κ³ , μ±μ μκΈ°λ μλΆ μμμ μκΈ°μ λΉμ·ν κ²μ νμΈνμλ€. μ¦, κ·Όμ νλ©΄ μΌμ± μμ€ν
μ μ΄μ©νμ¬ νμ₯μμ μ€μΈ‘ν κ²°κ³Όλ μμ± μμμ νμ©ν μ°κ΅¬λ€κ³Όλ λ€λ₯Έ κ²°κ³Όλ₯Ό λ³΄μΌ μλ μμΌλ©°, μμ± μμμ νκ° λ° μ΄ν΄νλλ° μ¬μ©λ μ μλ€. λ°λΌμ, λ³΄λ€ μ νν μμμ ꡬ쑰μ , μ리νμ λ©μ»€λμ¦μ μ΄ν΄νκΈ° μν΄μλ κ·Όμ νλ©΄ μΌμ±μ νμ©ν νμ₯μμ ꡬμΆν μλ£ κΈ°λ°μ λ λ§μ μ°κ΅¬λ€μ΄ νμνλ€λ κ²μ μμ¬νλ€.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 115λ°
Evaluating solar-induced fluorescence across spatial and temporal scales to monitor primary productivity
Solar-induced chlorophyll fluorescence (SIF) has been widely cited in carbon cycling studies as a proxy for photosynthesis, and SIF data are commonly incorporated into terrestrial primary productivity models. Though satellite-based SIF products show close relationships with gross primary productivity (GPP), this is not universally true at intermediate scales. A meta-analysis of the tower-based and airborne SIF literature revealed that mean SIF retrievals from unstressed vegetation span three orders of magnitude. While reporting on spectrometer calibration procedures, hardware characterizations, and associated corrections is inconsistent, laboratory and field experiments show that these factors may contribute to significant uncertainty in SIF retrievals. Additionally, there remain ongoing questions regarding the interpretation of SIF data made across spatial scales and the link between satellite SIF retrievals and primary productivity on the ground. Chlorophyll fluorescence originates from dynamic energy partitioning at the leaf level and does not exhibit a uniformly linear relationship with photosynthesis at finer scales. As a standalone metric, SIF measured at the tower scale was not found to track changes in carbon assimilation following stomatal closure induced in deciduous woody tree branches. This lack of relationship may be explained by alternative energy partitioning pathways, such as thermal energy dissipation mediated by xanthophyll cycle pigments; the activity of these pigments can be tracked using the photochemical reflectance index (PRI). Gradual, phenological changes in energy partitioning are observed as changes in the slope of the SIF-PRI relationship over the course of a season. Along with high frequency effects such as wind-mediated changes in leaf orientation and reflectance, and rapid changes in sky condition due to clouds, PRI offers crucial insights needed to link SIF to leaf physiology. While SIF offers tremendous promise for improving the characterization of terrestrial carbon exchange, and a fuller understanding of the boundaries on its utility and interpretation as a biophysical phenomenon will help to create more reliable models of global productivity
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Diurnal and Seasonal Proximally Sensed Photochemical Reflectance Index (PRI) in a High-Stress Semi-Arid Mixed Conifer Forest
A lack of accurate, reliable data on coupled carbon and water fluxes for Earthβs expansive ecosystems remains a major barrier to a complete understanding of the terrestrial carbon cycle. The remotely sensed Photochemical Reflectance Index (PRI) measures deepoxidation of the xanthophyll cycle at wavelength 531nm and is one of the few pigment-based vegetation indices sensitive to rapid plant physiological responses. PRI presents new opportunities to study ecosystems on a diurnal time scale, as well as seasonal processes in evergreen systems where complex vegetation dynamics are not reflected by small annual changes in chlorophyll content or leaf structure. However, systematic PRI acquisition in conjunction with leaf and ecosystem flux measurements are needed in natural, diverse ecosystems. The growing field of proximal remote sensing affords the opportunity to bridge leaf, canopy and ecosystem scales, for a physiological inspection of whole ecosystem dynamics. The Southwest U.S. provides a natural setting for examining the influence of environmental drivers on the productivity of drought-sensitive forests, as well as for evaluating our ability to track these relationships using optical methods. We studied PRI in a semi-arid, sub-alpine mixed conifer forest, in order to assess its ability to serve as a proxy for dynamic photoprotection. We combined canopy spectral measurements with eddy covariance flux and sap flow methods to determine the sensitivity of PRI to seasonal changes in gross primary productivity (GPP) and the ecohydrological variability of a high stress environment. In addition, we combined top-of-canopy leaf-level gas exchange, chlorophyll fluorescence, and hyperspectral measurements to determine the sensitivity of PRI to diurnal changes in needle photosynthetic function, and confirm the extent to which canopy diurnal patterns reflect leaf physiology. At the canopy scale we found that the relationship between PRI and GPP was inconsistent over the course of the monsoon season, shifting from a negative relationship in July and August (R2=.62), to a positive relationship in September (R2=.48). Multiple linear regression with soil moisture and air temperature showed that PRI responded to dynamic water and energy limitations of this system (R2=.41). We report for the first time a relationship between seasonal PRI and sap flow in a natural forest (R2=.55). These results suggest that on a seasonal scale PRI is an effective indicator of photosynthetic response to ecohydrological constraints. On a diurnal scale we found that PRI remained constant throughout the day at both leaf and canopy scales, and we suggest that saturated light conditions drive retention of xanthophylls in a de-epoxidized state. We contribute evidence that remotely sensed PRI has potential to fill a major gap in our ability to distinguish how water availability influences forest productivity and associated carbon dynamics