Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover general, biome-independent relationships between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. To this end we used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r = 0.56) and a "reference" isoprene model that includes both temperature and GPP (r = 0.49), and far better than either remotely sensed green vegetation cover (r = 0.25) or model-estimated GPP (r = 0.14). GPP in tropical regions was anti-correlated with formaldehyde concentration (r = –0.30), which peaks during the dry season. We conjecture that the positive correlations of isoprene emission with primary production, and with air temperature, found in temperate forest regions arise simply because all three peak during the relatively short growing season. In most tropical regions, where the seasonal cycles of GPP and canopy temperature are very different, isoprene emission is revealed to depend on canopy temperature but not at all on GPP. The lack of a general correlation between GPP and formaldehyde concentration is consistent with experimental evidence that isoprene emission is decoupled from photosynthesis, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress. In contrast, the high correlation between canopy temperature and formaldehyde concentration indicates the importance of including canopy temperature explicitly in large-scale models.

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Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

Biogeosciences ◽

10.5194/bg-11-3437-2014 ◽

2014 ◽

Vol 11 (13) ◽

pp. 3437-3451 ◽

Cited By ~ 8

Author(s):

P. N. Foster ◽

I. C. Prentice ◽

C. Morfopoulos ◽

M. Siddall ◽

M. van Weele

Keyword(s):

Primary Production ◽

Air Temperature ◽

Seasonal Cycle ◽

Gross Primary Production ◽

Canopy Temperature ◽

Remotely Sensed ◽

Formaldehyde Concentration ◽

Isoprene Emission ◽

The Tropics ◽

Better Than

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover generalized relationships applicable across many biomes between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. We used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r= 0.56) and a "reference" isoprene model that combines GPP and an exponential function of temperature (r = 0.49), and far better than either remotely sensed green vegetation cover, fPAR (r = 0.25) or model-estimated GPP (r = 0.14). Gross primary production in tropical regions was anti-correlated with formaldehyde concentration (r = −0.30), which peaks during the dry season. Our results were most reliable in the tropics, where formaldehyde observational errors were the least. The tropics are of particular interest because they are the greatest source of isoprene emission as well as the region where previous modelling attempts have been least successful. We conjecture that positive correlations of isoprene emission with GPP and air temperature (as found in temperate forests) may arise simply because both covary with canopy temperature, peaking during the relatively short growing season. The lack of a general correlation between GPP and formaldehyde concentration in the seasonal cycle is consistent with experimental evidence that isoprene emission rates are largely decoupled from photosynthetic rates, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress.

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A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

Remote Sensing ◽

10.3390/rs12020258 ◽

2020 ◽

Vol 12 (2) ◽

pp. 258 ◽

Cited By ~ 7

Author(s):

Ruonan Qiu ◽

Ge Han ◽

Xin Ma ◽

Hao Xu ◽

Tianqi Shi ◽

...

Keyword(s):

North America ◽

Primary Production ◽

Vegetation Type ◽

Gross Primary Production ◽

Remotely Sensed ◽

Seasonal Cycles ◽

Vegetation Types ◽

Growth Trend ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

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Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China

Environmental Science Processes & Impacts ◽

10.1039/c5em00061k ◽

2015 ◽

Vol 17 (4) ◽

pp. 753-762

Author(s):

Mingquan Wu ◽

Shakir Muhammad ◽

Fang Chen ◽

Zheng Niu ◽

Changyao Wang

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Eddy Covariance ◽

Gross Primary Production ◽

Model Performance ◽

Wetland Ecosystem ◽

Wetland Ecosystems ◽

New Model ◽

Estuarine Wetland ◽

Better Than

A new model performance better than the MODIS GPP product for wetland ecosystems was proposed and validated.

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Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-27-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 27-30 ◽

Cited By ~ 2

Author(s):

H. H. Jaafar ◽

F. A. Ahmad

Keyword(s):

Primary Production ◽

Crop Yield ◽

Vegetation Index ◽

Crop Yields ◽

Gross Primary Production ◽

Remotely Sensed ◽

Early Assessment ◽

Food Shortages ◽

Semi Arid ◽

Summer Crop

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

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A pigment ratio index based on remotely sensed reflectance provides the potential for universal gross primary production estimation

Environmental Research Letters ◽

10.1088/1748-9326/abf3dc ◽

2021 ◽

Author(s):

Wenjin Wu ◽

Howard E. Epstein ◽

Huadong Guo ◽

Xinwu Li ◽

Chen Gong

Keyword(s):

Primary Production ◽

Gross Primary Production ◽

Remotely Sensed ◽

Pigment Ratio

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Consistent large-scale response of hourly extreme precipitation to temperature variability

10.5194/egusphere-egu21-2156 ◽

2021 ◽

Author(s):

Haider Ali ◽

Hayley Fowler ◽

Geert Lenderink

Keyword(s):

Air Temperature ◽

Large Scale ◽

Surface Air Temperature ◽

Absolute Humidity ◽

Tropical Regions ◽

Local Scaling ◽

Hourly Precipitation ◽

Dewpoint Temperature ◽

Scale Response ◽

Temperature Scaling

<p>Hourly precipitation extremes can intensify with higher temperatures at higher rates than theoretically expected from thermodynamic increases explained by the Clausius-Clapeyron (CC) relationship (~6.5%/K), but local scaling with surface air temperature is highly variable. Here, we use daily dewpoint temperature, a direct proxy of absolute humidity, as the scaling variable instead of surface air temperature. Using a global dataset of over 7000 hourly precipitation gauges, we estimate the at-gauge local scaling across six macro-regions; this ranges from CC to 2xCC for more than 60% of gauges. We find positive scaling in subtropical and tropical regions in contrast to previous work. Moreover, regional scaling rates show surprisingly universal behaviour at around CC, with higher scaling rates in Europe. Our results show a much greater consistency of scaling across the globe than previous work, usually at or above the CC rate, suggesting the relevance of dewpoint temperature scaling to understand future changes. &#160;&#160;</p>

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Identification of a general light use efficiency model for gross primary production

Biogeosciences ◽

10.5194/bg-8-999-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 999-1021 ◽

Cited By ~ 23

Author(s):

J. E. Horn ◽

K. Schulz

Keyword(s):

Primary Production ◽

Large Scale ◽

Gross Primary Production ◽

Daily Basis ◽

Climatic Conditions ◽

Environmental Drivers ◽

Light Use Efficiency ◽

Meteorological Forcing ◽

Use Efficiency ◽

Light Use Efficiency Model

Abstract. Non-stationary and non-linear dynamic time series analysis tools are applied to multi-annual eddy covariance and micrometeorological data from 44 FLUXNET sites to derive a light use efficiency model for gross primary production on a daily basis. The extracted typical behaviour of the canopies in response to meteorological forcing leads to a model formulation allowing for a variable influence of the environmental drivers temperature and moisture availability modulating the light use efficiency. Thereby, the model is applicable to a broad range of vegetation types and climatic conditions. The proposed model explains large proportions of the variation of the gross carbon uptake at the study sites while the optimized set of six parameters is well defined. With the parameters showing explainable and meaningful relations to site-specific environmental conditions, the model has the potential to serve as basis for general regionalization strategies for large scale carbon flux predictions.

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