scholarly journals Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations

2018 ◽  
Vol 24 (9) ◽  
pp. 3990-4008 ◽  
Author(s):  
Xing Li ◽  
Jingfeng Xiao ◽  
Binbin He ◽  
M. Altaf Arain ◽  
Jason Beringer ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Global Analysis ◽  
Strongly Correlated ◽  
Flux Tower

scholarly journals Challenges and uncertainty in plot-scale emissivity and surface temperature estimation using flux tower measurements

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Ivonne Trebs ◽  
Mauro Suils ◽  
Kaniska Mallic
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
Strongly Correlated ◽  
Surface Emissivity ◽  
Flux Tower ◽  
Infrared Radiance ◽  
Exchange Processes

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. At the plot-scale, the flux tower recorded longwave radiation components are inverted to retrieve LST. Since the down-welling longwave component was not measured routinely until recently, usually only the up-welling longwave component is used for the plot-scale LST retrieval. However, we found that ignoring reflected down-welling longwave radiation for plot-scale LST estimations can lead to substantial error. This also has important implications for estimating the correct surface emissivity using flux tower measurements, which is needed for plot-scale LST retrievals. The present study proposes a new method for plot-scale emissivity and LST estimation and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both up-welling and down-welling longwave radiation components are 0.5 to 1.5 K lower than estimates using only up-welling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the up-welling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. Landscape-scale day-time LST obtained from satellite data (MODIS TERRA) was strongly correlated with our plot-scale estimates for most of the sites, but higher by several Kelvin at two sites. We also quantified the uncertainty in estimated LST and surface emissivity using the different methods and found that the proposed method does not result in increased uncertainty. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

scholarly journals Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence

2017 ◽  
Vol 9 (6) ◽  
pp. 530 ◽  
Author(s):  
Nima Madani ◽  
John Kimball ◽  
Lucas Jones ◽  
Nicholas Parazoo ◽  
Kaiyu Guan
Keyword(s):  
Chlorophyll Fluorescence ◽  
Global Analysis ◽  
Satellite Observations ◽  
Ecosystem Productivity

scholarly journals Low temperature and hardening effects on photosynthetic apparatus efficiency and survival of forage grass varieties

2014 ◽  
Vol 60 (No. 4) ◽  
pp. 177-183 ◽  
Author(s):  
B. Borawska-Jarmułowicz ◽  
G. Mastalerczuk ◽  
S. Pietkiewicz ◽  
Kalaji MH
Keyword(s):  
Chlorophyll Fluorescence ◽  
Freezing Tolerance ◽  
Positive Impact ◽  
Photosynthetic Apparatus ◽  
Extreme Temperature ◽  
Temperature Treatment ◽  
Developmental Phase ◽  
Strongly Correlated ◽  
Perennial Plants ◽  
Psii Activity

Freezing tolerance is essential for perennial plants and ability to adapt to extreme temperature is crucial for their survival in many environments. Freezing tolerance of hardened and unhardened plants of Dactylis glomerata and Lolium perenne varieties was probed by their quantum photosynthetic efficiency using the chlorophyll fluorescence technique. Quantum yield of photosystem II (PSII) electron transport (&Phi;<sub>PSII</sub>), maximal (F<sub>m</sub>&rsquo;) and steady-state (F<sub>s</sub>) chlorophyll fluorescence yields of light-adapted samples were measured. &Phi;<sub>PSII</sub> depended on developmental phase, temperature and hardening process. A clear decline in PSII activity, especially after &ndash;10&deg;C application was observed. Plant hardening during emergence phase had a positive impact on PSII activity, especially after &ndash;5&deg;C application. After 72 h of &ndash;5&deg;C temperature treatment, hardened plants showed quicker recovery of their photosynthetic apparatus (0.527&ndash;0.697) as compared to unhardened ones (0.224&ndash;0.330). Stress temperature of &ndash;10&deg;C caused irreversible changes of photosynthetic apparatus of hardened and unhardened plants independently of growth phases (0.003&ndash;0.014). &Phi;<sub>PSII</sub> and F<sub>m</sub>&rsquo; parameters were strongly correlated with shoots survival under stress. Our results suggest that perennial plants&rsquo; hardening allows them to survive low temperatures due inter alia enhancing their photosynthetic machinery performance.

scholarly journals Challenges and uncertainty in plot-scale emissivity and surface temperature estimation using flux tower measurements

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stanislaus J. Schymanski ◽  
Ivonne Trebs ◽  
Mauro Sulis ◽  
Kaniska Mallick
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
Strongly Correlated ◽  
Surface Emissivity ◽  
Flux Tower ◽  
Infrared Radiance ◽  
Exchange Processes

Abstract Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. At the plot-scale, the flux tower recorded longwave radiation components are inverted to retrieve LST. Since the down-welling longwave component was not measured routinely until recently, usually only the up-welling longwave component is used for the plot-scale LST retrieval. However, we found that ignoring reflected down-welling longwave radiation for plot-scale LST estimations can lead to substantial error. This also has important implications for estimating the correct surface emissivity using flux tower measurements, which is needed for plot-scale LST retrievals. The present study proposes a new method for plot-scale emissivity and LST estimation and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both up-welling and down-welling longwave radiation components are 0.5 to 1.5 K lower than estimates using only up-welling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the up-welling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) was strongly correlated with our plot-scale estimates for most of the sites, but higher by several Kelvin at two sites. We also quantified the uncertainty in estimated LST and surface emissivity using the different methods and found that the proposed method does not result in increased uncertainty. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

scholarly journals Global Analysis of the Relationship between Reconstructed Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP)

2021 ◽  
Vol 13 (14) ◽  
pp. 2824
Author(s):  
Haiqiang Gao ◽  
Shuguang Liu ◽  
Weizhi Lu ◽  
Andrew R. Smith ◽  
Rubén Valbuena ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Landscape Heterogeneity ◽  
Global Analysis ◽  
Gross Primary Production ◽  
Spatiotemporal Variability ◽  
Spatial And Temporal Scales ◽  
Discriminatory Analysis ◽  
Positive Correlations ◽  
The Relationship

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p < 0.001). The generally linear SIFc–GPP relationships were substantially affected by spatial and temporal scales and SIFc datasets. The GPP/SIFc slope of the evergreen needleleaf forest (ENF) biome was significantly higher than the slopes of several other biomes (p < 0.05), while the other 11 biomes showed no significant differences in the GPP/SIFc slope between each other (p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship.

scholarly journals Estimating Global Gross Primary Production from Sun-Induced Chlorophyll Fluorescence Data and Auxiliary Information Using Machine Learning Methods

2021 ◽  
Vol 13 (5) ◽  
pp. 963
Author(s):  
Yu Bai ◽  
Shunlin Liang ◽  
Wenping Yuan
Keyword(s):  
Machine Learning ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Gross Primary Production ◽  
Global Scale ◽  
In Situ Measurements ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Flux Tower

The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05° spatial resolution for the period 2001–2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R2) = 0.58, root mean square error (RMSE) = 2.74 g C·m−2, bias = –0.34 g C·m−2) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future.

The Concurrent Validity of the Minnesota Child Development Inventory as a Measure of Young Children's Language Development

1989 ◽  
Vol 54 (1) ◽  
pp. 101-105 ◽  
Author(s):  
J. Bruce Tomblin ◽  
Cynthia M. Shonrock ◽  
James C. Hardy
Keyword(s):  
Child Development ◽  
Language Development ◽  
Expressive Language ◽  
Strong Predictor ◽  
Receptive Language ◽  
Strongly Correlated ◽  
Language Measure ◽  
The Mean ◽  
Child Development Inventory ◽  
Mean Length Of Utterance

The extent to which the Minnesota Child Development Inventory (MCDI), could be used to estimate levels of language development in 2-year-old children was examined. Fifty-seven children between 23 and 28 months were given the Sequenced Inventory of Communication Development (SICD), and at the same time a parent completed the MCDI. In addition the mean length of utterance (MLU) was obtained for each child from a spontaneous speech sample. The MCDI Expressive Language scale was found to be a strong predictor of both the SICD Expressive scale and MLU. The MCDI Comprehension-Conceptual scale, presumably a receptive language measure, was moderately correlated with the SICD Receptive scale; however, it was also strongly correlated with the expressive measures. These results demonstrated that the Expressive Language scale of the MCDI was a valid predictor of expressive language for 2-year-old children. The MCDI Comprehension-Conceptual scale appeared to assess both receptive and expressive language, thus complicating its interpretation.

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