20 research outputs found
Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?
Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four siteyears soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sitesโ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m-2 s-1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous
Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations
Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions.
In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate
๊ทผ์ ํ๋ฉด ์๊ฒฉ ์ผ์ฑ ์์คํ ๋ค์ ์ด์ฉํ ์ง์์ ์๋ฌผ ๊ณ์ ๋ฐ ํ์ ์ ๋ ์ฝ๋ก์ ํ๊ด๋ฌผ์ง ๊ด์ธก
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ํ๊ฒฝ๋ํ์ ํ๋๊ณผ์ ์กฐ๊ฒฝํ, 2022.2. ๋ฅ์๋ 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 ๊ณ์ ๋ฐ ์์์ ์๋ฆฌํ์ , ๊ตฌ์กฐ์ ์ธ ๋ณํ๋ฅผ ์ง์์ ์ผ๋ก ๋ชจ๋ํฐ๋ง ํ๋ ๊ฒ์ ์ก์์ํ๊ณ์ ๋๊ธฐ๊ถ ์ฌ์ด์ ์๋์ง, ํ์ ์ํ ๋ฑ์ ํผ๋๋ฐฑ์ ์ดํดํ๋๋ฐ ํ์์ ์ด๋ค. ์ด๋ฅผ ๊ด์ธกํ๊ธฐ ์ํ์ฌ ์์ฑ์์ ๊ด์ธก๋ ์ ๊ทํ ์์ ์ง์ (NDVI) ํ์ ์ ๋ ์ฝ๋ก์ ํ๊ด๋ฌผ์ง (SIF)๋ ๋์ค์ ์ผ๋ก ์ฌ์ฉ๋๊ณ ์๋ค. ๊ทธ๋ฌ๋, ์ฐ์ฃผ ์์ฑ ๊ธฐ๋ฐ์ ์๋ฃ๋ ๋ค์๊ณผ ๊ฐ์ ํ๊ณ์ ๋ค์ด ์กด์ฌํ๋ค. 1) ์์ง๊น์ง ๊ณ ํด์๋์ ์์ฑ ๊ธฐ๋ฐ SIF ์๋ฃ๋ ์๊ณ , 2) ์์ฑ ์๋ฃ๋ค์ ๋๊ธฐ์ ์ง (์, ๊ตฌ๋ฆ)์ ์ํฅ์ ๋ฐ์, ํ๋ฆฐ ๋ ์ ์์์ ์๋ฆฌํ์ , ๊ตฌ์กฐ์ ๋ณํ๋ฅผ ํ์งํ๊ธฐ ํ๋ค๋ค. ๋ํ, 3) ์์ฑ ์ด๋ฏธ์ง๋ ์๋ถ ์์๊ณผ ํ๋ถ ์์์ด ํผํฉ๋์ด ์์ธ ์ ํธ๋ฅผ ํ์งํ๊ธฐ ๋๋ฌธ์, ๊ฐ ์ธต์ ์๋ฌผ ๊ณ์ ์ ๊ฐ๊ฐ ์ฐ๊ตฌํ๊ธฐ์ ์ด๋ ค์์ด ์๋ค. ๊ทธ๋ฌ๋ฏ๋ก, ์์ฑ์์ ํ์งํ ์ ํธ๋ฅผ ํ๊ฐํ๊ณ , ์์์ ์๋ฆฌํ์ , ๊ตฌ์กฐ์ ๋ณํ๋ฅผ ๋ณด๋ค ์ ํํ ์ดํดํ๊ธฐ ์ํด์๋ ๊ทผ์ ํ๋ฉด ์๊ฒฉ ์ผ์ฑ ์์คํ
์ ์ด์ฉํ ์ค์ธก ์๋ฃ ๊ธฐ๋ฐ์ ์ฐ๊ตฌ๋ค์ด ์๊ตฌ๋๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์ ์ฃผ ๋ชฉ์ ์ ๊ทผ์ ํ๋ฉด ์๊ฒฉ ์ผ์ฑ ์์คํ
์ ์ด์ฉํ์ฌ ์๋ฌผ ๊ณ์ ๋ฐ SIF๋ฅผ ํ์ฅ์์ ์ง์์ ์ผ๋ก ์ค์ธกํ๊ณ , ์์ฑ ์์ ๊ธฐ๋ฐ์ ์ฐ๊ตฌ๊ฐ ๊ฐ๊ณ ์๋ ํ๊ณ์ ๋ฐ ๊ถ๊ธ์ฆ๋ค์ ํด๊ฒฐ ๋ฐ ๋ณด์ํ๋ ๊ฒ์ด๋ค. ์ด ๋ชฉ์ ์ ๋ฌ์ฑํ๊ธฐ ์ํ์ฌ, ์๋์ ๊ฐ์ ์ธ๊ฐ์ง Chapter: 1) SIF๋ฅผ ๊ด์ธกํ๊ธฐ ์ํ ํํฐ ๊ธฐ๋ฐ์ ์ ๋ ดํ ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
๊ฐ๋ฐ, 2)์จ๋ ์นจ์ฝ์๋ฆผ์์์ ์ฐ์์ ์ธ SIF ๊ด์ธก, 3)์์ฑ ๊ธฐ๋ฐ์ ์๋ฌผ ๊ณ์ ๊ณผ ์ค์ธกํ ๋ค์ธต ์์์ ์๋ฌผ ๊ณ์ ๋น๊ต๋ก ๊ตฌ์ฑํ๊ณ , ์ด๋ฅผ ์งํํ์๋ค.
SIF๋ ์์์ ๊ตฌ์กฐ์ , ์๋ฆฌํ์ ๋ณํ๋ฅผ ์ดํดํ๊ณ , ์ถ์ ํ๋ ์ธ์๋ก ์ฌ์ฉ๋ ์ ์์ด, SIF๋ฅผ ํ์ฅ์์ ๊ด์ธกํ๊ธฐ ์ํ ๋ค์ํ ๊ทผ์ ํ๋ฉด ์๊ฒฉ ์ผ์ฑ ์์คํ
๋ค์ด ์ต๊ทผ ์ ์๋์ด ์ค๊ณ ์๋ค. ๊ทธ๋ฌ๋, ์์ง๊น์ง SIF๋ฅผ ๊ด์ธกํ๊ธฐ ์ํ ์์
์ ์ผ๋ก ์ ํต๋๋ ๊ด์ธก ์์คํ
์ ํ์ ํ ๋ถ์กฑํ๋ฉฐ, ๋ถ๊ด๊ณ์ ๊ตฌ์กฐ์ ํน์ฑ์ ํ์ฅ์์ ๊ด์ธก ์์คํ
์ ๋ณด์ ๋ฐ ๊ด๋ฆฌํ๊ธฐ๊ฐ ์ด๋ ค์ ๋์ ์ง์ SIF๋ฅผ ์ทจ๋ํ๋ ๊ฒ์ ๋งค์ฐ ๋์ ์ ์ธ ๋ถ์ผ์ด๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์ Chapter 2์์๋ SIF๋ฅผ ํ์ฅ์์ ๋ณด๋ค ์์ฝ๊ฒ ๊ด์ธกํ๊ธฐ ์ํ ํํฐ ๊ธฐ๋ฐ์ ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
(Smart Surface Sensing System, 4S-SIF)์ ๊ฐ๋ฐํ์๋ค. ์ผ์๋ ๋์ญ ํํฐ๋ค๊ณผ ํฌํ ๋ค์ด์ค๋๊ฐ ๊ฒฐํฉ๋์ด ์์ผ๋ฉฐ, ์๋ณด ๋ชจํฐ๋ฅผ ์ฌ์ฉํ์ฌ ๋์ญ ํํฐ ๋ฐ ๊ด์ธก ๋ฐฉํฅ์ ์๋์ ์ผ๋ก ๋ณ๊ฒฝํจ์ผ๋ก์จ, ํ ๊ฐ์ ํฌํ ๋ค์ด์ค๋๊ฐ 3๊ฐ์ ํ์ฅ ๋ฒ์(757, 760, 770 nm)์ ๋น ๋ฐ ํ์์ผ๋ก๋ถํฐ ์
์ฌ๋๋ ๊ด๋๊ณผ ์์์ผ๋ก ๋ฐ์ฌ/๋ฐฉ์ถ๋ ๊ด๋์ ๊ด์ธกํ ์ ์๋๋ก ๊ณ ์๋์๋ค. ํฌํ ๋ค์ด์ค๋๋ก๋ถํฐ ์ธ์๋ ๋์งํธ ์์น ๊ฐ์ ์์
์ ์ผ๋ก ํ๋งค๋๋ ์ด๊ณ ํด์๋ ๋ถ๊ด๊ณ(QE Pro, Ocean Insight)์ ๋๋ ทํ ์ ํ ๊ด๊ณ๋ฅผ ๋ณด์ด๋ ๊ฒ์ ํ์ธํ์๋ค (R2 > 0.99). ์ถ๊ฐ์ ์ผ๋ก, 4S-SIF์์ ๊ด์ธก๋ SIF์ ์ด๊ณ ํด์๋ ๋ถ๊ด๊ณ๋ฅผ ์ด์ฉํ์ฌ ์ถ์ถํ SIF๊ฐ ์ ํ์ ์ธ ๊ด๊ณ๋ฅผ ์ด๋ฃจ๋ ๊ฒ์ ํ์ธํ์๋ค. ์์์ ์๋ฆฌํ์ ๋ณํ๋ฅผ ์ผ์ผํค๋ ํํ ๋ฌผ์ง์ธ DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)์ ์ฒ๋ฆฌํ์์๋ ๋ถ๊ตฌํ๊ณ , ์ฐ์ถ๋ SIF๋ค์ ์ ํ ๊ด๊ณ๋ฅผ ๋ณด์๋ค. ๋ณธ ์ผ์๋ ๊ธฐ์กด ์์คํ
๋ค์ ๋นํด ์ฌ์ฉํ๊ธฐ ์ฝ๊ณ ๊ฐ๋จํ๋ฉฐ, ์ ๋ ดํ๊ธฐ ๋๋ฌธ์ ๋ค์ํ ์๊ณต๊ฐ์ ์ค์ผ์ผ์ SIF๋ฅผ ๊ด์ธกํ ์ ์๋ค๋ ์ฅ์ ์ด ์๋ค.
์์ฑ ๊ธฐ๋ฐ์ SIF๋ฅผ ์ด์ฉํ์ฌ ์ด์ผ์ฐจ์์ฐ์ฑ(gross primary productivity, GPP)์ ์ถ์ ํ๋ ์ฐ๊ตฌ๋ ์ต๊ทผ ํ์ ์ํ ์ฐ๊ตฌ ๋ถ์ผ์์ ๊ฐ๊ด๋ฐ๊ณ ์๋ ์ฐ๊ตฌ ์ฃผ์ ์ด๋ค. ๊ทธ๋ฌ๋, SIF์ GPP์ ๊ด๊ณ๋ ์ฌ์ ํ ๋ง์ ๋ถํ์ค์ฑ์ ์ง๋๊ณ ์๋๋ฐ, ์ด๋ SIF-GPP ๊ด๊ณ๋ฅผ ์กฐ์ ํ๋ ์์์ ๊ตฌ์กฐ์ ๋ฐ ์๋ฆฌํ์ ์์ธ์ ๋ฐ๋ก ๋ถ๋ฆฌํ์ฌ ๊ณ ์ฐฐํ ์ฐ๊ตฌ๋ค์ด ๋ถ์กฑํ๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์ Chapter 3์์๋ ์ง์์ ์ผ๋ก SIF, GPP, ํก์๋ ๊ดํฉ์ฑ์ ํจ๋ณต์ฌ๋ (absorbed photosynthetically active radiation, APAR), ๊ทธ๋ฆฌ๊ณ ํด๋ก๋กํ๊ณผ ์นด๋กํฐ๋
ธ์ด๋์ ๋น์จ ์ธ์ (chlorophyll: carotenoid index, CCI)๋ฅผ ์จ๋์นจ์ฝ์๋ฆผ์์ ์ฐ์์ ์ผ๋ก ๊ด์ธกํ์๋ค. SIF-GPP ๊ด๊ณ์ ๊ตฌ์ฒด์ ์ธ ๋ฉ์ปค๋์ฆ ๊ด๊ณ๋ฅผ ๋ฐํ๊ธฐ ์ํ์ฌ, ๊ด ์ด์ฉํจ์จ (light use efficiency, LUE_p), ์ฝ๋ก์ ํ๊ด ์๋๋ฅ (chlorophyll fluorescence yield, ฮฆ_F) ๊ทธ๋ฆฌ๊ณ SIF ๊ด์๊ฐ ๊ตฐ๋ฝ์ผ๋ก๋ถํฐ ๋ฐฉ์ถ๋๋ ๋น์จ (escape fraction, f_esc)์ ๊ฐ๊ฐ ๋์ถํ๊ณ ํ๊ตฌํ์๋ค. SIF์ GPP์ ๊ด๊ณ๋ ๋๋ ทํ ๋น ์ ํ์ ์ธ ๊ด๊ณ๋ฅผ ๋ณด์ด๋ ๊ฒ์ ํ์ธํ์ผ๋ฉฐ(R2 = 0.91 with a hyperbolic regression function, daily), ์ผ์ฃผ๊ธฐ ๋จ์์์ SIF๋ APAR์ ๋ํด ์ ํ์ ์ด์์ง๋ง GPP๋ APAR์ ๋ํด ๋๋ ทํ ํฌํ ๊ด๊ณ๋ฅผ ๋ณด์ด๋ ๊ฒ์ ํ์ธํ์๋ค. ์ถ๊ฐ์ ์ผ๋ก LUE_p ์ ฮฆ_F ๊ฐ ๋๊ธฐ ์จ๋์ ๋ฐ๋ผ ๋ฐ์ํ๋ ์ ๋๊ฐ ๋ค๋ฅธ ๊ฒ์ ํ์ธํ์๋ค. LUE_p๋ ๋์ ์จ๋์์ ํฌํ ๋์์ง๋ง, ฮฆ_F๋ ํฌํ ํจํด์ ํ์ธํ ์ ์์๋ค. ๋ํ, ๊ตฐ๋ฝ ์์ค์์์ CCI์ ฮฆ_F๊ฐ ๋๋ ทํ ์๊ด ๊ด๊ณ๋ฅผ ๋ณด์๋ค(R2 = 0.84). ์ด๋ ฮฆ_F๊ฐ ์ฝ๋ก์ ์์์ ์ํฅ์ LUE_p์ ๋นํด ๋ ๊ฐํ ๊ด๊ณ๊ฐ ์์ ์ ์์์ ์์ฌํ๋ค. ๋ง์ง๋ง์ผ๋ก, f_esc๊ฐ ํ์๊ด์ ์ฐ๋๋ ์ ๋์ ๋ฐ๋ผ ๋ฐ์์ ํ์ฌ, ฮฆ_F์ LUE_p์ ๊ฐํ ์๊ด ๊ด๊ณ๋ฅผ ํ์ฑํ๋๋ฐ ๊ธฐ์ฌํ๋ ๊ฒ์ ํ์ธํ์๋ค. ์ด๋ฌํ ๋ฐ๊ฒฌ์ ์จ๋ ์นจ์ฝ์๋ฆผ์์ ๊ตฐ๋ฝ ์์ค์ SIF-GPP๊ด๊ณ๋ฅผ ์๋ฆฌํ์ ๋ฐ ๊ตฌ์กฐ์ ์ธก๋ฉด์์ ์ดํดํ๊ณ ๊ท๋ช
ํ๋๋ฐ ํฐ ๋์์ด ๋ ๊ฒ์ด๋ค.
์๋ฌผ ๊ณ์ ์ ์์์ด ์ฒ ์ ๋ฐ๋ผ ์ฃผ๊ธฐ์ ์ผ๋ก ๋ํ๋ด๋ ๋ณํ๋ฅผ ๊ด์ธกํ๋ ๋ฐ์์ด๋ค. ์๋ฌผ ๊ณ์ ์ ์ก์์ํ๊ณ์ ๋๊ธฐ๊ถ ์ฌ์ด์ ๋ฌผ์ง ์ํ์ ์ดํดํ๋๋ฐ ๋งค์ฐ ์ค์ํ๋ค. ์์ฑ ๊ธฐ๋ฐ์ NDVI๋ ์๋ฌผ ๊ณ์ ์ ํ์งํ๊ณ ์ฐ๊ตฌํ๋๋ฐ ๊ฐ์ฅ ๋์ค์ ์ผ๋ก ์ฌ์ฉ๋๋ค. ๊ทธ๋ฌ๋, ํ์ฝ์๋ฆผ์์์ ์์ฑ NDVI ๊ธฐ๋ฐ์ ๊ฐ์ฝ ์๊ธฐ ๋ฐ ์ฑ์ ์๊ธฐ๊ฐ ์ค์ ์ด๋ ์์ ์ ํ์งํ๋์ง๋ ๋ถ๋ถ๋ช
ํ๋ค. ์ค์ ํ์ฝ์๋ฆผ์ ๋ค์ธต ์์ ๊ตฌ์กฐ์ ์ผ์ฐจ์์ผ๋ก ์ด๋ฃจ์ด์ ธ ์๋ ๋ฐ๋ฉด, ์์ฑ ์์์ ๋ค์ธต ์์์ ์ ํธ๊ฐ ์์ฌ ์๋ ์ด์ฐจ์์ ๊ฒฐ๊ณผ๋ฌผ์ด๊ธฐ ๋๋ฌธ์ด๋ค. ๋ฐ๋ผ์, ์์ฑ NDVI ๊ธฐ๋ฐ์ ์๋ฌผ ๊ณ์ ์ด ๋ค์ธต ์์ ๊ตฌ์กฐ๋ฅผ ์ด๋ฃจ๊ณ ์๋ ํ์ฝ์๋ฆผ์์ ์ค์ ํ์ฅ ๊ด์ธก๊ณผ ๋น๊ตํ์์ ๋ ์ด๋ ์์ ์ ํ์งํ๋์ง์ ๋ํ ๊ถ๊ธ์ฆ์ด ๋จ๋๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์ Chapter 4์์๋ ์ง์์ ์ผ๋ก 8๋
๋์ ํ์ฝ์๋ฆผ๋ด์ ๋ค์ธต ์์์ ์๋ฌผ ๊ณ์ ์ ๊ทผ์ ํ๋ฉด ์๊ฒฉ ์ผ์ฑ ์์คํ
์ ์ด์ฉํ์ฌ ๊ด์ธกํ๊ณ , ์์ฑ NDVI ๊ธฐ๋ฐ์ ์๋ฌผ ๊ณ์ ๊ณผ ๋น๊ตํ์๋ค. ๋ค์ฑ๋ ๋ถ๊ด๊ณ๋ฅผ ์๋ถ ์์์ ์์ ์๋์ ์ค์นํจ์ผ๋ก์จ, ์๋ถ ์์๊ณผ ํ๋ถ ์์์ ์๋ฌผ ๊ณ์ ์ ๊ฐ๊ฐ ์ฐ์์ ์ผ๋ก ๊ด์ธกํ์๋ค. ์๋ฌผ ๊ณ์ ์ ํ์งํ๊ธฐ ์ํ์ฌ ๊ฐ์ฅ ๋ง์ด ์ฌ์ฉ๋๋ ๋ฐฉ๋ฒ์ธ 1) ์ญ์น๋ฅผ ์ด์ฉํ๋ ๋ฐฉ๋ฒ๊ณผ 2) ์ด๊ณ๋ํจ์๋ฅผ ์ด์ฉํ๋ ๋ฐฉ๋ฒ์ ์ฌ์ฉํ์ฌ ๊ฐ์ฝ ์๊ธฐ ๋ฐ ์ฑ์ ์๊ธฐ๋ฅผ ๊ณ์ฐํ๊ณ ์ด๋ฅผ ๋ค์ธต ์์์ ์๋ฌผ ๊ณ์ ๊ณผ ๋น๊ตํ์๋ค. ๋ณธ ์ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ฒซ๋ฒ์งธ๋ก, ๊ตฐ๋ฝ์ ์์ธต๋ถ์์ ์ค์ธกํ NDVI์ ์์ฑ ๊ธฐ๋ฐ์ NDVI๊ฐ ๊ฐํ ์ ํ ๊ด๊ณ๋ฅผ ๋ณด์ด๋ ๊ฒ์ ํ์ธํ๋ค (R2=0.95 ๋ MODIS ์์๋ค ๋ฐ R2= 0.85 ๋ Landsat8). ๋๋ฒ์งธ๋ก, ์ด๊ณ๋ํจ์ ๋ฐฉ๋ฒ๊ณผ 10%์ ์ญ์น ๊ฐ์ ์ด์ฉํ ๋ฐฉ๋ฒ์ด ๋น์ทํ ๊ฐ์ฝ ์๊ธฐ๋ฅผ ์ถ์ ํ๋ ๊ฒ์ ํ์ธํ์์ผ๋ฉฐ, ํ๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ์ ๋น์ทํ ์๊ธฐ์์ ํ์ธํ์๋ค. ์ธ๋ฒ์งธ๋ก, ์ด๊ณ๋ํจ์ ๋ฐฉ๋ฒ๊ณผ 90%์ ์ญ์น ๊ฐ์ ์ด์ฉํ ๋ฐฉ๋ฒ์ด ๋น์ทํ ์ฑ์ ์๊ธฐ๋ฅผ ์ฐ์ถํ์์ผ๋ฉฐ, ์ด๋ ์๋ถ ์์์ ์ฑ์ ์๊ธฐ์ ๋น์ทํ์๋ค. ์ถ๊ฐ์ ์ผ๋ก ์๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ์ ํ๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ๊ฐ ์จ๋์ ๋ฐ์ํ๋ ์ ๋๊ฐ ๋๋ ทํ๊ฒ ์ฐจ์ด๊ฐ ๋๋ ๊ฒ์ ํ์ธํ ์ ์์๋ค. ์๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ๋ ์ ์ฐ ์์ฅ ์จ๋ ์ผ์ (AGDD)์ ๊ฐํ ์๊ด์ฑ์ ๋ณด์๊ณ , ํ๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ๋ AGDD์ ์ฐ๊ด์ฑ์ ๊ฐ๊ณ ์์ ๋ฟ๋ง ์๋๋ผ ์ถ์ ์ผ์(NCD)์๋ ๋ฏผ๊ฐํ๊ฒ ๋ฐ์ํ๋ ๊ฒ์ ํ์ธํ์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ ์์ฑ NDVI ๊ธฐ๋ฐ์ ๊ฐ์ฝ ์๊ธฐ๋ ํ๋ถ ์์์ ๊ฐ์ฝ ์๊ธฐ์ ์ฐ๊ด์ฑ์ด ๋๊ณ , ์ฑ์ ์๊ธฐ๋ ์๋ถ ์์์ ์ฑ์ ์๊ธฐ์ ๋น์ทํ๋ค๋ ๊ฒ์ ์๋ฏธํ๋ค. ๋ํ, ์๋ถ ์์๊ณผ ํ๋ถ ์์์ด ์จ๋์ ๋ค๋ฅธ ๋ฏผ๊ฐ์ฑ์ ๊ฐ๊ณ ์์ด, ์์ฑ์์ ์ฐ์ถ๋ ์๋ฌผ ๊ณ์ ์ ์ด์ฉํ์ฌ ๊ธฐํ๋ณํ๋ฅผ ์ดํดํ๊ณ ์ ํ ๋, ์ด๋ค ์ธต์ ์์์ด ์์ฑ ์์์ ์ฃผ๋ ์ํฅ์ ๋ฏธ์น๋์ง ๊ณ ๋ คํด์ผ ํ๋ค๋ ๊ฒ์ ์์ฌํ๋ค.
์์ฑ์ ๋์ ์ง์ญ์ ๋ณํ๋ฅผ ์์ฝ๊ฒ ๋ชจ๋ํฐ๋งํ ์ ์์ด ๋ง์ ๊ฐ๋ฅ์ฑ์ ๊ฐ๊ณ ์๋ ๋๊ตฌ์ด์ง๋ง, ๋ณด๋ค ์ ํํ ์์ฑ ๊ด์ธก ๊ฐ์ ์ดํดํ๊ธฐ ์ํด์๋ ํ์ฅ์์ ๊ด์ธก๋ ์๋ฃ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํ ๊ฒ์ฆ์ด ์๊ตฌ๋๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ 1) ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
์ ๊ฐ๋ฐ, 2) ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
์ ํ์ฉํ ์์์ ์๋ฆฌํ์ ๊ตฌ์กฐ์ ๋ณํ์ ์ง์์ ์ธ ๊ด์ธก, 3) ๋ค์ธต ์์ ๊ตฌ์กฐ์์ ๊ด์ธก๋๋ ์๋ฌผ ๊ณ์ ๋ฐ ์์ฑ์์ ์ถ์ ๋ ์๋ฌผ ๊ณ์ ์ ์ฐ๊ด์ฑ ํ๊ฐ๋ฅผ ์ํํ์๋ค. ๊ฐ๋ฐํ ๊ทผ์ ํ๋ฉด ์ผ์๋ ์์
์ผ์๋ค๊ณผ ๋น๊ตํ์ ๋, ๊ฐ๊ฒฉ์ ์ผ๋ก ์ ๋ ดํ๊ณ ์ ์ฝ๊ฒ ์ฌ์ฉํ ์ ์์์ผ๋ฉฐ, ์ฑ๋ฅ์ ์ผ๋ก๋ ๋ถ์กฑํจ์ด ์์๋ค. ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
์ ์ด์ฉํ์ฌ SIF๋ฅผ ์จ๋ ์นจ์ฝ์๋ฆผ์์ ์ง์์ ์ผ๋ก ๊ด์ธกํ ๊ฒฐ๊ณผ, ์ด์ผ์ฐจ์์ฐ์ฑ๊ณผ SIF๋ ๋น์ ํ ๊ด๊ณ๋ฅผ ๊ฐ๋ ๊ฒ์ ํ์ธํ์๋ค. ์ด๋ ๋ง์ ์ ํ ์ฐ๊ตฌ๋ค์์ ๋ฐํํ ์์ฑ ๊ธฐ๋ฐ์ SIF์ GPP๊ฐ ์ ํ์ ์ธ ๊ด๊ณ๋ฅผ ๋ณด์ธ๋ค๋ ๊ฒ๊ณผ๋ ๋ค์ ์๋ฐ๋ ๊ฒฐ๊ณผ์ด๋ค. ๋ค์ธก ์์์ ๋ด์ฒ ์๋ฌผ ๊ณ์ ์ ์ฐ์์ ์ผ๋ก ๊ด์ธกํ๊ณ , ์์ฑ ๊ธฐ๋ฐ์ ์๋ฌผ ๊ณ์ ๊ณผ ๋น๊ตํ๊ฐํ ์ฐ๊ตฌ์์๋ ์์ฑ ๊ธฐ๋ฐ์ ๊ฐ์ฝ ์๊ธฐ๋ ํ๋ถ ์์์ ์ํฅ์ ์ฃผ๋ก ๋ฐ๊ณ , ์ฑ์ ์๊ธฐ๋ ์๋ถ ์์์ ์๊ธฐ์ ๋น์ทํ ๊ฒ์ ํ์ธํ์๋ค. ์ฆ, ๊ทผ์ ํ๋ฉด ์ผ์ฑ ์์คํ
์ ์ด์ฉํ์ฌ ํ์ฅ์์ ์ค์ธกํ ๊ฒฐ๊ณผ๋ ์์ฑ ์์์ ํ์ฉํ ์ฐ๊ตฌ๋ค๊ณผ๋ ๋ค๋ฅธ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ผ ์๋ ์์ผ๋ฉฐ, ์์ฑ ์์์ ํ๊ฐ ๋ฐ ์ดํดํ๋๋ฐ ์ฌ์ฉ๋ ์ ์๋ค. ๋ฐ๋ผ์, ๋ณด๋ค ์ ํํ ์์์ ๊ตฌ์กฐ์ , ์๋ฆฌํ์ ๋ฉ์ปค๋์ฆ์ ์ดํดํ๊ธฐ ์ํด์๋ ๊ทผ์ ํ๋ฉด ์ผ์ฑ์ ํ์ฉํ ํ์ฅ์์ ๊ตฌ์ถํ ์๋ฃ ๊ธฐ๋ฐ์ ๋ ๋ง์ ์ฐ๊ตฌ๋ค์ด ํ์ํ๋ค๋ ๊ฒ์ ์์ฌํ๋ค.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
Advances in field-based high-throughput photosynthetic phenotyping
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis
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
Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future
Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future
Emerging approaches to measure photosynthesis from the leaf to the ecosystem
Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- A nd fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem
Remote Sensing of Plant Biodiversity
At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imageryโbut global coverageโof ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally.
This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plantsโprimary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing
instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution.
The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity.
Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely.
Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understandingโthat is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON).
This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earthโjust when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequateโand globalโmeasures of what we are losing
Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence
Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C-3 or C-4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C-3 and one for C-4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 +/- 6.54 PgC/year for the 2015-2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected 'hotspots' of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.Peer reviewe
OCO-3 early mission operations and initial (vEarly) XCOโ and SIF retrievals
NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode.
This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (COโ) dry-air mole fraction (XCOโ) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle.
The first public release of OCO-3 Level 2 (L2) data products, called โvEarlyโ, is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO2 exhibits a root-mean-squared-error (RMSE) of โ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCOโ products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021