This article was downloaded by: [85.58.31.116]
On: 05 November 2014, At: 11:20
Publisher: Taylor & Francis
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered
office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
International Journal of Remote
Sensing
Publication details, including instructions for authors and
subscription information:
http://www.tandfonline.com/loi/tres20
Remote sensing of atmospheric
biogenic volatile organic compounds
(BVOCs) via satellite-based
formaldehyde vertical column
assessments
ab
Shawn C. Kefauver , Iolanda Filella
ab
ab
& Josep Peñuelas
a
CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Universitat
Autónoma de Barcelona, 08193 Barcelona, Catalonia, Spain
b
CREAF, 08193 Barcelona, Catalonia, Spain
Published online: 03 Nov 2014.
To cite this article: Shawn C. Kefauver, Iolanda Filella & Josep Peñuelas (2014) Remote sensing
of atmospheric biogenic volatile organic compounds (BVOCs) via satellite-based formaldehyde
vertical column assessments, International Journal of Remote Sensing, 35:21, 7519-7542, DOI:
10.1080/01431161.2014.968690
To link to this article: http://dx.doi.org/10.1080/01431161.2014.968690
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the
“Content”) contained in the publications on our platform. However, Taylor & Francis,
our agents, and our licensors make no representations or warranties whatsoever as to
the accuracy, completeness, or suitability for any purpose of the Content. Any opinions
and views expressed in this publication are the opinions and views of the authors,
and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content
should not be relied upon and should be independently verified with primary sources
of information. Taylor and Francis shall not be liable for any losses, actions, claims,
proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or
howsoever caused arising directly or indirectly in connection with, in relation to or arising
out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Any
substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,
systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Downloaded by [85.58.31.116] at 11:20 05 November 2014
Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
International Journal of Remote Sensing, 2014
Vol. 35, No. 21, 7519–7542, http://dx.doi.org/10.1080/01431161.2014.968690
REVIEW ARTICLE
Remote sensing of atmospheric biogenic volatile organic compounds
(BVOCs) via satellite-based formaldehyde vertical column assessments
Shawn C. Kefauvera,b*, Iolanda Filellaa,b, and Josep Peñuelasa,b
a
CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Universitat Autónoma de Barcelona, 08193
Barcelona, Catalonia, Spain; bCREAF, 08193 Barcelona, Catalonia, Spain
Downloaded by [85.58.31.116] at 11:20 05 November 2014
(Received 29 June 2014; accepted 2 September 2014)
Global vegetation is intrinsically linked to atmospheric chemistry and climate, and
better understanding vegetation–atmosphere interactions can allow scientists to not
only predict future change patterns, but also to suggest future policies and adaptations
to mediate vegetation feedbacks with atmospheric chemistry and climate. Improving
global and regional estimates of biogenic volatile organic compound (BVOCs) emissions is of great interest for their biological and environmental effects and possible
positive and negative feedbacks related to climate change and other vectors of global
change. Multiple studies indicate that BVOCs are on the rise, and with near 20 years of
global remote sensing of formaldehyde (HCHO), the immediate and dominant BVOC
atmospheric oxidation product, the accurate and quantitative linkage of BVOCs with
plant ecology, atmospheric chemistry, and climate change is of increasing relevance.
The remote sensing of BVOCs, via HCHO in a three step process, suffers from an
additive modelling error, but improvements in each of the steps have reduced this error
by over a multiplication factor improvement compared to estimates without remote
sensing. Differential optical absorption spectroscopy (DOAS) measurement of the
HCHO slant columns from spectral absorption properties has been adapted to include
the correction of numerous spectral artefacts and intricately refined for each of a series
of sensors of increasing spectral and spatial resolution. Conversion of HCHO slant to
HCHO vertical columns using air mass factors (AMFs) has been improved with the
launch of new sensors and the incorporation of radiative transfer and chemical transport models (CTM). The critical process of linking HCHO to BVOC emissions and
filtering non-biogenic emissions to explicitly quantify biogenic emissions has also
greatly improved. This critical last step in down-scaling from global satellite coverage
to local biogenic emissions now benefits from the increasing precision and nearexplicitness of available CTMs as well as the increasing availability of global
remote-sensing data sets needed to proportionally assign the HCHO column to different related biogenic (global plant functional type and land cover classifications),
atmospheric (dust, aerosols, clouds, other trace gases), climate (temperature, wind,
precipitation), and anthropogenic (fire, biomass burning) factors.
1. Introduction
Biogenic volatile organic compounds (BVOCs) are the largest source of non-methane
hydrocarbons (NMHCs), contributing 1150 TgC/year, composed of 44% isoprene, 11%
monoterpenes, 22.5% other reactive VOCs, and 22.5% other VOCs, estimated globally
(Guenther et al. 1995, 2000), accounting for approximately 85% of total NMVOCs, with
the remainder 13% attributed to anthropogenic sources (Olivier et al. 2003) and 3% to
fires (Stavrakou et al. 2009a). BVOCs play several important roles in tropospheric
*Corresponding author. Email: [email protected]
© 2014 Taylor & Francis
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7520
S.C. Kefauver et al.
chemical composition, including significant impacts on the chemistry of the hydroxyl
radical (OH) (Fehsenfeld et al. 1992), contribution to the production of tropospheric ozone
(Houweling, Dentener, and Lelieveld 1998; Poisson, Kanakidou, and Crutzen 2000), and
influence on climate in their role as precursors of secondary organic aerosols (Veefkind
et al. 2011) and increases in the lifetime of methane (Poisson, Kanakidou, and Crutzen
2000). To better understand the intricate interconnectedness of these processes, BVOCs
must be assessed in space and time, and the tool to do so is remote sensing. The remote
sensing of BVOCs relies on the spectral detection of tropospheric formaldehyde (HCHO)
as the principal intermediate product of the oxidation of BVOCs, which, with its short
lifetime and as primary continental BVOC origin, may be used as a proxy or top-down
constraint on local BVOC emissions (Abbot et al. 2003; Barkley et al. 2012, 2008; Millet
et al. 2008; Palmer et al. 2006, 2003; Shim et al. 2005). This article focuses on the
distinction between the remote sensing of BVOCs and that of isoprene by including
information on additional biogenic emissions and models required to separate biogenic
and anthropogenic sources of isoprene and other sources of HCHO.
Formaldehyde (HCHO), the dominant immediate product of most BVOCs that can be
measured by remote sensing, can be emitted directly itself (Seco, Peñuelas, and Filella
2007), but the most significant sources of HCHO are formed via oxidation of VOCs by
the hydroxyl radical (OH), usually within an hour, and has a lifetime of a few hours
(Palmer et al. 2003). Photolysis is the dominant sink of HCHO, producing a strong
correlation between HCHO and the 24 hour OH concentrations, allowing HCHO to be
used as a proxy of tropospheric OH (Staffelbach et al. 1991). Although the largest global
source of HCHO in the remote troposphere is actually from the oxidation of methane,
HCHO from the oxidation of non-methane volatile organic compounds (NMVOCs)
normally dominates in the continental boundary layer (Lee et al. 1998; Abbot et al.
2003; Munger et al. 1995) and has often been linked to isoprene, the dominant BVOC
emission (Abbot et al. 2003; Barkley et al. 2013; Palmer et al. 2006, 2003; Shim et al.
2005). For this reason, studies of VOCs within the continental boundary layers often focus
on the term NMVOCs to identify the more variable contribution to the total VOC
atmospheric vertical column.
The remote sensing of BVOCs first requires a three-step process, including the
estimation of the HCHO slant column using satellite-based spectral measurements, the
conversion of the HCHO measured slant column to vertical column densities, and finally
linking the HCHO vertical column with local BVOC emissions (Figure 1). The slant
columns are derived by fitting a model of the spectral absorption features of HCHO and
other related atmospheric components concentrations from satellite-measured Earth
albedo spectral properties (quantities of reflected light). This spectral absorption model
fit then provides the HCHO slant column, which represents the total amount of HCHO
along the path between the satellite sensor and the surface of the Earth (Figure 2). These
slant columns are then converted to vertical columns using air mass factors (AMF), which
are based on radiative transfer models (RTMs) and general geometric assumptions about
the target compound’s vertical profile. Vertical columns then contain the information
about the vertical concentration of the target atmospheric trace gas over a specific area
of the Earth’s surface. Coupling the top-down remote sensing vertical columns of HCHO
with a RTM and a chemical transport model (CTM) then provides the final critical link
between the remote sensing-derived HCHO vertical columns and actual BVOC emissions
from plants (Figure 1).
This link from satellite to BVOCs emissions can then be used to correlate with BVOC
emissions or constrain vegetation–environment-based BVOC global models such as the
International Journal of Remote Sensing
Spectral Fitting Technique
(LS or DOAS)
Satellite
Data
7521
(Marais et al. 2013
Non-biogenic screening)
Biomass
Burning
(exclude)
Instrument corrections
Sensor-based
spectral window
optimization
BrO
Prefit
Radiative transfer model
(LIDORT, DISORT):
provides scattering weights
Downloaded by [85.58.31.116] at 11:20 05 November 2014
GFED Fire
Screening
(IMAGESv2)
Biomass
Burning
(exclude)
Chemical Transport Model
(GEOS-CHEM, IMAGES, MCM)
provides AMF shape factors,
HCHO-BVOCs Links
Cross-validation with
bottom-up emissions
(MEGAN, NASA-CASA,
GEIA, WRF), Field Data
HCHO
Spectral Fit
Slant
Column HCHO
Air Mass Factor (AMF)
Vertical
Column HCHO
Biogenic
VOCs
MODIS
Fire
Screening
OMI AAOD
Smoke
Absorption
OMI AAOD
Dust
Absorption
AATST Fire
Screening
Dust
Influence
(exclude)
Anthropogenic
Influence
(exclude)
Low NOx Smearing
Correction
Figure 1. Top-down three-step process used for the remote sensing of BVOCS. Satellite sensor
spectral data is first converted to HCHO slant columns using a multi-phase DOAS or other spectral
fitting technique. Next, the HCHO slant columns must be converted to HCHO vertical columns
using AMF. Either incorporated into the AMF process or as separate filter process, possible other
non-biogenic sources of formaldehyde may be filtered. AMFs are now largely produced using
geometric calculations in combination with a RTM or a CTM. Finally, the HCHO vertical columns
can be linked to BVOC emissions by pairing with a CTM and cross-validated with field data or used
as constraints to bottom-up inventory models such as MEGAN or NASA-CASA.
Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al.
2006). This method is applied to HCHO for BVOC estimates, but is similarly applied for
vertical column retrievals of bromine oxide (BrO), ozone (O3), NO2, SO2, and OClO
(Levelt et al. 2006). The expansion of the techniques used to measure HCHO were soon
applied in similar studies focused on glyoxal (CHOCHO), another BVOC proxy (Kurosu,
Chance, and Volkamer 2005; Wittrock et al. 2006; Kurosu et al. 2007), potentially of
greater relevance for urban studies (Volkamer et al. 2005) and global studies of BVOCs,
especially with regards to quantifying and understanding the causes of the variability of its
ratio to HCHO (Vrekoussis et al. 2010).
The accuracy of BVOC estimates suffers from the summation of errors from each of
the phases in the inherently step-wise process, but has also offered multiple opportunities
for improvements. Improving global and regional estimates of BVOC emissions is of
great interest for their importance in understanding plant ecology, atmospheric chemistry,
and possible positive and negative feedbacks related to climate change and other related
aspects of global change. There still remains some uncertainty regarding global levels of
BVOC emissions, but clear seasonal and geographic patterns support our current understanding of factors controlling BVOC emissions. This review aims to summarize the stateof-the-science related to the sources of error, recent improvements, and available tools for
the remote sensing of BVOCs and related products for applications in regional,
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7522
S.C. Kefauver et al.
Figure 2. Graphical representation of the remote sensing of BVOCs using satellite sensors,
indicating the various components of BVOC detection and the difference between the effective
slant column and vertical column density. The satellite sensor measures a signal of light reflected off
of atmospheric constituents deemed the effective slant column, which must then be converted to the
vertical column density.
continental, and global studies of plant ecophysiology, biosphere–atmosphere interactions,
and climate change.
2. Satellites for the study of atmospheric chemistry
Formaldehyde (HCHO) vertical column concentrations using satellite data were first
measured using the Global Ozone Monitoring Experiment (GOME) over Asia in 1997
(Thomas et al. 1998) and later applied extensively to North America (Chance et al. 2000;
Millet et al. 2008, 2006; Palmer et al. 2006), Africa (Marais et al. 2012), South America
(Barkley et al. 2013, 2012, 2008), and globally (De Smedt et al. 2008; Kurosu et al. 2007;
Marbach et al. 2010; Perner et al. 1997; Stavrakou et al. 2009b; Wittrock et al. 2000)
using, at least in part, the detailed atmospheric chemistry information provided by the
following sensors.
2.1. GOME
GOME was the first operational sensor capable of the global mapping of BVOCs. It was
launched in 1995 on board the second European Remote Sensing Satellite (ERS-2) in a
sun-synchronous orbit with an equator crossing time of 10:30 (descending mode). GOME
had an operational focus on global ozone distributions, but also measured a range of
atmospheric trace constituents including NO2, OClO, volcanic SO2, BrO, and HCHO.
GOME is a nadir-viewing spectrometer that measures atmospheric scattering of solar
International Journal of Remote Sensing
Table 1.
7523
Atmospheric chemistry satellites used for measuring BVOCs (via formaldehyde).
Instrument
GOME1
SCIAMACHY2
OMI3
GOME-24
Temporal
coverage
Satellite
1995–2011
2002–2012
2004–
2006–
ERS-2
ENVI-SAT
EOS AURA
MetOp-A
Spatial res.
(km × km)*
40 ×
30 ×
13 ×
10 ×
320
60
24
40
Global
coverage
Spectral res.
Spectral
(nm)
range (nm)
Three days
Three days
Daily
Daily
0.2 to 0.4
240 to 790
0.22 to 1.48 240 to 2380
0.45 to 1.0
270 to 500
0.2 to 0.4
240 to 790
Downloaded by [85.58.31.116] at 11:20 05 November 2014
Note: *Spatial resolution at nadir; 1Burrows et al. (1999), https://.esa.int/; 2Bogumil et al. (2003), http://www.
sciamachy.org/; 3Ahmad et al. (2003), Ahmad et al. (2003), Ahmad et al. (2003) and Ahmad et al. (2003), http://
www.nasa.gov/; 4Munro et al. (2006), http://www.esa.int/, http://www.doas-bremen.de/gome-2.htm.
radiation from 240 to 790 nm with a spectral resolution of 0.2 to 0.4 nm (Table 1). The
field of view could be varied in size from 320 km × 40 km to 960 km × 80 km, achieving
global coverage in three days using the full maximum swath width (Burrows et al. 1999).
In 2004, GOME Level 2 data were released with the GOME Data Processor (GDP)
version 4.0, including several improvements to the archived data. The same improved
GDP version applied retroactively to the archived GOME data is also the current operational GDP for GOME-2, providing a near 25 year continuity of the GDP, even though the
sensor specifics have greatly improved with the new generation GOME-2 sensor. Specific
details of the GDP v4.0 are available online in the Algorithm Theoretical Basis Document
(ATBD, http://wdc.dlr.de/sensors/gome/gdp4/ATBD.pdf). Due to the failure of the tape
recorder of ERS-2 on 22 June 2003, GOME products became limited to the north
hemispheric receiving stations of the European Space Agency (ESA), and on 5
September 2011 the whole ERS-2 satellite platform of GOME was retired.
2.2. SCIAMACHY
SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric
Cartography) was launched in March of 2002 aboard the European satellite ENVISAT
(Environmental Satellite), also in a sun-synchronous orbit with a local equator crossing
time of 10:00 (descending) (Table 1). It measured atmospheric absorption in spectral
bands from 240–2380 nm (Figure 3), providing atmospheric scattering information
relevant for the measurement of atmospheric composition and dynamics, aerosols, cloud
properties, and surface reflection. SCIAMACHY was an imaging spectrometer with a
very different design compared to GOME, comprising a mirror system, sun diffusor,
telescope, spectrometer, calibration unit and thermal and electronic subsystems, and
performing measurements in nadir, limb, and solar/lunar occultation geometry. From
these measurements total column amounts and stratospheric profiles of a multitude of
atmospheric constituents were retrieved simultaneously and globally every three days,
including O3, BrO, OClO, ClO, SO2, H2CO, NO, NO2, NO3, CO, CO2, CH4, H2O, N2O,
aerosols, radiation, and cloud properties. On the night side of the orbit (eclipse mode),
when a lack of sun illumination means no reflected and backscattered solar radiation could
be measured, a sequence of eclipse observations (e.g. air glow, biomass burning) were
achieved. It was a joint project of Germany, The Netherlands and Belgium. Contact with
the ENVISAT satellite was lost in April of 2012, though efforts are underway to reestablish communications and the satellite continues in a stable orbit after 10 years of
operation, far exceeding its planned five year lifespan. Data are available via ESA’s
7524
S.C. Kefauver et al.
Trace Gas\nm 200
300
400
500
600 700
800
900
1000
1500
2000
300
400
500
600 700
800
900
1000
1500
2000
HCHO
CHOCHO
BrO
ClO
OClO
(O2)2
O3
O2
NO2
Aerosols
Satellite\nm
200
Downloaded by [85.58.31.116] at 11:20 05 November 2014
GOME
SCIAMACHY
OMI
GOME-2
Figure 3. Absorption regions of various trace gases measured by the DOAS method and confounding atmospheric components, overlaid on a corresponding graph of the spectral measurement
range of the four major sensors data available for the spectral-based remote sensing of BVOCs.
Spectral regions indicate the regions where absorption features are commonly used to identify and
quantify each atmospheric constituent using spectral absorption analyses.
Earthnet Online (https://earth.esa.int) and The Netherlands SCIAMACHY Data Centre
(http://neonet.knmi.nl/)
2.3. Ozone Monitoring Instrument
The Ozone Monitoring Instrument (OMI) was launched on the EOS AURA satellite in
2004. OMI on EOS AURA also has sun-synchronous orbit but differs from GOME,
SCIAMACHY, and GOME-2 in another significant aspect; it has a local afternoon equator
crossing time of 13:45 (ascending node) (Levelt et al. 2006), which makes its HCHO
retrieval complimentary but not directly comparable to the mid-morning equator crossing
times of the other three major satellites used for BVOCs estimation (De Smedt et al.
2012). It is a nadir-viewing, wide-field-imaging, UV and visible spectrometer designed to
monitor key atmospheric pollutants identified by the US Environmental Protection
Agency as posing serious threats to human health and agricultural productivity such as:
tropospheric and stratospheric O3, NO2, SO2, BrO, OClO, HCHO, chlorofluorocarbons,
dust, smoke, volcanic ash, and other aerosols. OMI was designed with the specific goal of
improving the spatial resolution of atmospheric trace gas measurements compared to
GOME (13 km × 25 km for OMI vs. 40 km × 320 km for GOME), though it measures
in a much more limited spectral range compared to SCIAMACHY (270–500 nm for OMI
compared to 240–2380 nm for SCIAMACHY, Table 1). It is a joint contribution of
The Netherland’s agency for aerospace programs (NIVR) and Finland’s Finnish
Meteorological Institute. Although some row anomalies were found in the L1B and L2
data products (http://www.temis.nl/docs/omi_warning.html), the sensor is still considered
to be fully and optimally operational. Data is available at the GES DISC Mirador Search
International Journal of Remote Sensing
7525
Interface (http://mirador.gsfc.nasa.gov/), the EOS Warehouse Inventory Search Tool
TEMIS (http://www.temis.nl/airpollution/no2col/no2regioomi_col3.php), and the
GIOVANNI OMI/Aura Online Visualization and Analysis tool (http://disc.sci.gsfc.nasa.
gov/Giovanni), which not only provides daily level 2G and 3 global gridded products, but
also a suite of built in analysis tools.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
2.4. GOME-2
GOME-2 was launched in October 2006 on the MetOp-A satellite. The GOME-2 instrument uses a scanning mirror with a scan-width of 1920 km to achieve 10 km × 40 km
spatial resolution covering a spectral range of 240 to 790 nm (Table 1). GOME-2 observes
ground pixels four times smaller (80 km × 40 km) than GOME on ERS-2 and has
improved polarization and calibration capabilities and also provides global coverage
within one day, compared to three by GOME. It has a sun-synchronous polar orbit with
an early morning equator crossing time of 9:30 (descending node). GOME-2 was
designed to map concentrations of atmospheric O3, NO2, SO2, BrO, OClO, HCHO, and
other trace gases as well as cloud properties and intensities of ultraviolet radiation crucial
for the monitoring of atmospheric composition and the detection of pollutants. GOME-2
uses the same current operational GDP v4.0 as the first GOME archived data (http://wdc.
dlr.de/sensors/gome/gdp4/ATBD.pdf). GOME-2 level 2 total column products of ozone,
minor trace gases, and cloud properties, level 3 (composites), and level 4 (assimilated
products) are generated at DLR in the framework of the Satellite Application Facility on
Ozone and Atmospheric Chemistry Monitoring (http://o3msaf.fmi.fi/), and GOME-2
Level 2 off-line products can be ordered on-line via the EUMETSAT Earth Observation
Portal (http://www.eumetsat.int/Home/index.htm).
2.5. Satellites for bottom-up modelling approaches
Although BVOCs are not directly detectable using remote-sensing methods, BVOCs are
generally short lived once they enter the atmosphere and the aforementioned satellites are
capable of the detection and quantification of the primary products of the dominant
BVOC tropospheric chemistry processes. This is an indirect measurement of BVOCs,
which then requires a model of atmospheric chemistry processes to make the linkage
between the BVOC emissions from vegetation to by-products detected in the surrounding
atmosphere. This depends on a great number of atmospheric variables, but also on the
type of vegetation on the ground where the atmospheric BVOC product concentrations are
calculated. In this way, many of the final steps in linking satellite-based remote-sensing
estimates of BVOC products to the actual emissions of BVOCs benefit from and rely on
analyses provided by other remote-sensing platforms. Here we cover some of these
multisensor synergies that have contributed to the improvement of the remote sensing
of BVOCs.
Although large area studies of biogenic emissions first relied on the Biogenic
Emissions Land-cover Database at a 1 km resolution (Kinnee, Geron, and Pierce 1997),
Diem and Comrie (2002) used Landsat to further define the composition of urban and
surround natural areas at a 30 m resolution (Diem 2002). They then used literature-based
values of biogenic emissions (isoprene, monoterpenes, and other VOC) based on plant
class properties (species, leaf biomass, and foliar density), according to the Biogenic
Emissions Inventory System (BIES) (Geron, Guenther, and Pierce 1994), to estimate
BVOC emissions at a 30 m resolution with increased accuracy. The Advanced Very
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7526
S.C. Kefauver et al.
High Resolution radiometer (AVHRR) was used to produce global BVOC emission
estimates as part of the Global Emissions Inventories Activity (GEIA, http://www.igacproject.org/GEIA) of the International Global Atmospheric Chemistry Project (IGAC)
(Guenther et al. 1995). The MEGAN, with a 1 km resolution, was later developed as an
update and replacement of both the BIES and GEIA canopy-environment emissions
models (Guenther et al. 2006). MEGAN more specifically incorporated remote-sensingbased global observations of leaf area index, plant functional type, and photosynthetic
photon flux density (PPFD) on multiple time scales from a large combination of sensors
and global remote-sensing databases, including AVHRR, Moderate Resolution Imaging
Spectroradiometer (MODIS), and Satellite Pour l’Observation de la Terre vegetation
products, and has recently been updated and extended as MEGAN2.1 and made available
online (Guenther et al. 2012).
VOC emission algorithms have also been incorporated into models focused on
ecosystem carbon fluxes since BVOCs can represent a significant percentage loss of
carbon from plants (Monson and Fall 1989) and directly and indirectly affect climate
at local and landscape scales (Hayden 1998). The NASA-CASA (Carnegie-AmesStanford Approach) integrated generalized isoprene emissions algorithms into their
global carbon flux model to account for the BVOC contribution to atmospheric CO2.
They used satellite-derived vegetation canopy properties of leaf area coverage,
‘greenness’ cover, and foliar biomass density, calculated as a combination of global
net primary productivity and fraction of photosynthetically active radiation from
AVHRR (Potter et al. 2001). This was similar to the approaches outlined by IGAC
more directly focused on emissions, but related terrestrial ecosystem production with
satellite ecosystem classifications and vegetation indices to drive the BVOC
emissions.
3. HCHO slant column calculations
3.1. BrO and O3 prefits
Because of similarities in the spectral absorption regions (Figure 3) and spectral
absorption features (Figure 4) between BrO and HCHO, using a pre-fitting technique
to counteract the impacts of BrO can help limit the presence of false positives in the
HCHO slant column calculations (Hewson et al. 2013). The BrO prefit was employed
on both the GOME-1 and GOME-2 standardized HCHO retrievals (Chance et al.
2000; De Smedt et al. 2008). By default, the BrO slant column calculations are
retrieved from the 328.5–359 nm spectral range (Hewson et al. 2013). A slightly
narrower BrO prefit spectral range has also been tested (Theys et al. 2011) from 332
to 359 nm, both with and without the inclusion of the OClO additional absorber effect.
The narrowing of the BrO prefit and the inclusion of the OClO both resulted in an
increase of the HCHO slant column compared to the default, with increases in
0.06 × 1016 molecules cm−2 and 0.15 × 1016 molecules cm−2, respectively (Hewson
et al. 2013). A prefit technique with both BrO and O3 simultaneously was also
developed (Chance 1998) and tested (Chance et al. 2000), with O3 fit in the 325–
335 nm range, resulting in slight increases in both HCHO slant column and chi-square
fit (χ2) and as such was deemed not operational considering the extra processing
requirements and potential skewing effects on the HCHO retrieval.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7527
Figure 4. DOAS absorption cross sections for formaldehyde (HCHO) and three atmospheric trace
gases with local absorptions of possible conflict: BrO, O4, and OClO. Data were provided by the
MolSpec Group, Institute of Environmental Physics, University of Bremen (http://www.iup.physik.
uni-bremen.de/gruppen/molspec/databases/index.html).
3.2. Spectral fit for HCHO retrievals
The HCHO absorption signature, despite its relatively high atmospheric abundance, is
relatively weak, but manageable when fit simultaneously using DOAS, a multilinear
regression using multiple trace gas reference spectra. Apart from HCHO, the atmospheric
trace gases BrO, O4, and OClO also have absorption features that fall in the wavelength
range of the GOME, SCIAMACHY, OMI, and GOME-2 satellites (Figures 3 and 4). Of
principal concern are the accurate retrievals of O3 in the lower UV ranges of the fitting
window and reduction of any false positive detection related to BrO. Generally optimized
spectral fits have been calculated for reduced global error for each of the four main
sensors and are often provided with their standard pre-processed products. However, with
several different confounding absorption profiles and a selection of different sensor data
sets to choose from, not to mention any specifics to any particular research project (such
as a focus on one particular geographic region, continental coverage, or marine coverage),
some researchers have preferred to optimize the spectral fit characteristics to their needs
(see examples below).
Earlier work used direct least-squares fitting techniques (Chance et al. 2000), with
reasonable accuracy, but because of the hyperspectral capacities of these satellites, gas
concentrations are more reliably retrieved using differential optical absorption spectroscopy (DOAS, first applied to HCHO (Platt, Perner, and Pätz 1979; Thomas et al. 1998),
coded into WinDOAS (Van Roozendael et al. 1999), modified (De Smedt et al. 2012;
Theys et al. 2011), and reviewed extensively (Platt and Stutz 2008)). DOAS algorithms
have been successfully employed for both satellite and validation studies using groundbased measurements with good accuracies for both HCHO and CHOCHO (Lee et al.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7528
S.C. Kefauver et al.
2005; Volkamer et al. 2005; Vigouroux et al. 2012) and is the current gold standard for
absorption profile retrievals.
The DOAS fit provides the trace gas total column amounts along the sensor’s viewing
path, the ‘slant columns’ (Platt, Perner, and Pätz 1979). Fitting retrieval accuracies have
been steadily improved from the first slant column calculations from the GOME sensor
(Burrows et al. 1999; Thomas et al. 1998) and improved to 4–5 times more accurate by
optimizing the fitting window and technique (Chance et al. 2000), but were also optimized
regionally (Palmer et al. 2006) and sensor-specific when making comparisons with
SCIAMACHY (De Smedt et al. 2008). The first retrievals of the HCHO slant column
from the GOME sensor used a spectral fitting window of 336.5–357.5 nm (Perner et al.
1997; Thomas et al. 1998). A minor variation of this fitting window, 337.35–356.12 nm,
was soon used after, representing an optimization between the intensity of the HCHO
spectral fit and spectral interference (Chance et al. 2000). A range of 325.5–350 (Marbach
et al. 2010) was chosen to include both the largest HCHO absorption features and other
minor absorption features to help distinguish HCHO from BrO, though it added noise
from the O3 scattering in the UV. Other slight adjustments using GOME were also made
to 337.5–359 nm with more of a focus on improving the fit compared to reducing
interference (Wittrock et al. 2000). This may have been due to the global mapping goal
of that particular study compared to the continental USA focus of the previous studies.
More variations of spectral fit range were tested in comparison to the significance of
including or excluding both HCHO absorption peaks, as well as major absorptions by
other confounding molecules. These were tested compared to a reference fit window of
328.5–346 (Hewson et al. 2013), based on De Smedt et al. (2008), with good results.
3.3. Other absorber effects
The spectral fitting using DOAS must be done while also accounting for spectral
anomalies due to rotational Raman scattering (RRS or the Ring effect), which cause
significant distortions in the measured spectral signal. This is actually a fairly straightforward process in application as the modelling of the Ring effect simply involves the
inclusion of Ring spectra as pseudo-absorbers, with either one (Chance and Spurr 1997)
or a combination of Fraunhofer and molecular pseudo-absorbers (Vountas, Rozanov, and
Burrows 1998). The general recommended process is the use of only a single pseudoabsorber, but by fitting intensity in the DOAS rather than differential optical depth
(Hewson et al. 2013). Since the total ring effect is related to light interactions with
variable atmospheric properties such as albedo, aerosol loading, and clouds, this is best
modelled using a RTM such as SCIATRAN (Rozanov et al. 2005).
Including fitting HCHO in the upper region of its absorption region around 328.5 to
346 nm can result in an improved overall fit, but must also contend with the inclusion of
O4, also denoted (O2)2, absorption minimums (Hewson et al. 2013) (Figure 4). The
utilization of a fifth order polynomial appears to account for this absorption and significantly nullifies O4 interference (De Smedt et al. 2008) and effectively allows for the
use of a wider fit window and even possibly a better quantification of otherwise unquantified atmospheric and instrumental artefacts accounted for with the use of a third degree
polynomial fit (Hewson et al. 2013).
Although OClO does not present the same level of inclusion on a global scale as BrO
or O4, it is globally inhomogeneous and can have significant effects, particularly in
stratospheric polar regions during springtime (Oetjen et al. 2011). Applying a filter to
discard satellite scans with a solar zenith angle of greater than 60 degrees should, for the
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7529
most part, eliminate the possibility of major OClO inclusions, but signal contamination
cannot be entirely discounted with this approach (Hewson et al. 2013) and, as such, OClO
absorptions have been accurately characterized pre-launch for possible inclusion (Figure
4), depending on the geographic region of focus (Bogumil et al. 2003). OClO absorption
inclusion may be necessary at high latitudes to achieve accurate HCHO retrievals, but
may, on the other hand, result in false OClO detection in the tropical troposphere of lower
latitudes (Hewson et al. 2013).
The Io effect is a result of the interference of the spectral absorption structures with the
highly structured solar spectrum, resulting basically from atmospheric interference similar
to the Ring effect and sensor under-sampling. A thorough investigation on the possible
improvements of the HCHO fit by the inclusion of the Io correction methodology supports
its application to all fitted trace gas absorbers (Hewson et al. 2013). This is especially
important in the case of having to calculate a fit for O3 to remove its influence before
subsequently making calculations for lesser absorbing trace gases, such as was found with
BrO (Aliwell et al. 2002) and has been deemed worthwhile for all trace gas column
retrievals (Hewson et al. 2013).
Fitting the sensor-measured Earth shine spectrum with a polynomial fit is a commonly
employed method as it effectively removes any broadband spectral components of the
Raleigh and Mie scattering before applying a spectral fit (either DOAS or direct fitting) to
the trace gas absorbers. Although a third degree polynomial fit was found to be sufficient
for use with the earlier GOME sensor (Wittrock et al. 2000), a later shift of the total
spectral fit window towards the inclusion of more UV, which allows for a better overall fit
but also greater interference by O3, led towards the application of a fifth degree polynomial as the optimal method (De Smedt et al. 2012). Trials with an intermediate fourth
degree polynomial applications had an intermediate correction effect (Hewson et al.
2013).
4. Slant to vertical column conversion using AMF
The slant columns derived from the DOAS spectral fitting of the absorption spectra
represent a calculation of the total integration of HCHO abundance across the entire
viewing path (Figure 2). They must be converted to vertical columns using basic
geometric calculations based on a few general assumptions of the measured constituent’s
vertical profile and RTM. The AMF includes the trace gas vertical distribution column
information such that the multiplication of the slant column by the AMF provides the
vertical column. As the vertical distributions and light scatter properties of each trace gas
are unique, the AMF is unique for each specific atmospheric species. Despite several
major improvements in the calculations of the AMF, the calculation of the AMF and
conversion from slant columns to vertical columns remains one of the largest sources of
error in the remote sensing of BVOCs (Millet et al. 2006; Palmer et al. 2001) because of
the complexity of factors, especially could cover, which influence the vertical distributions
of atmospheric trace gases across large spatial scales.
4.1. Basic geometric calculations
In the first uses of remote sensing for the estimation of HCHO, with the initial focus on
HCHO as a result of biomass burning instead of BVOCs (Thomas et al. 1998), geometricbased calculations of the AMF were deemed sufficient since they were not as strongly
absorbing as other atmospheric species such as ozone. Thomas et al. (Thomas et al. 1998)
7530
S.C. Kefauver et al.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
acknowledged that AMFs are usually produced using full RTMs, but were not warranted
with the specific target species and the tropical conditions of the study. This calculation is
the sum of the solar zenith angle and the line-of-sight secants (Figure 2). Soon after,
estimates of the error owing to the geometric AMF factors were deemed large enough to
warrant an improved AMF factor calculation, including the full multiscattering effects in a
separate radiative transport model coupled with a CTM during the first campaign to
observe HCHO concentrations over North America using GOME (Chance et al. 2000;
Palmer et al. 2001).
4.2. Radiative transport models
Palmer and others (Palmer et al. 2001) used a new formulation for the calculation of the
AMFs for application to satellite retrievals of formaldehyde from the GOME sensor (it
was the only one in operation at that time). They estimated that their new AMF values
were 20–40% less over continents compared to oceans and approximately half of the
previously used geometric AMF calculations. The novelty of their approach went beyond
the use of RTM to adequately characterize the multiple scattering coefficients of the target
trace gas (in this case it was HCHO), in that they also coupled their RTM with a 3D CTM,
which was used to independently calculate the vertical profile shape factors of each
atmospheric species. The vertical profile shape factors have significant impacts on the
multiple scattering of trace gases and are variable in space and time, but cannot be directly
derived from the satellite sensor spectral absorption information. Chance et al. (Chance
et al. 2000) estimated that this change in the calculation of the AMF resulted in a factor of
four or five improvement in the satellite retrievals of HCHO columns, with the total
contribution of the AMF to the HCHO vertical column retrieval process calculated by
Palmer et al. (2001) to be as little as 10%. This method can also be applied to any
atmospheric trace gas, which meets the single requirement of being optically thin and
slant columns from either direct spectral fitting (Chance et al. 2000) or DOAS (Platt,
Perner, and Pätz 1979; Thomas et al. 1998).
4.3. Linearized discrete ordinate radiative transfer (coupled with Goddard Earth
Observing System Chemistry)
The use of a 3D CTM to provide a separate source for determining the vertical distribution
of HCHO allows for an improvement in the calculation of the multiple scattering
coefficients of the atmospheric trace gas, which themselves are vertically variable. The
decoupling of the vertical profile of HCHO concentration estimates (from the CTM) from
the vertical dependence of the observational sensitivity data (from the RTM) prevents
model contamination of the satellite observations. For this first hybrid approach to AMF
calculation, the Goddard Earth Observing System Chemistry (GEOS-Chem) global 3D
CTM model of tropospheric chemistry (Bey et al. 2001) was employed along with the
Linearized Discrete Ordinate Radiative Transfer (LIDORT) of the Smithsonian
Astrophysical Observatory (Spurr, Kurosu, and Chance 2001). GEOS-Chem (http://
acmg.seas.harvard.edu/geos/index.html) is a major collaborative research project in constant evolution made available for testing by the scientific community, and likewise,
LIDORT (Spurr 2004, 2008) also has multiple updated versions.
International Journal of Remote Sensing
7531
Downloaded by [85.58.31.116] at 11:20 05 November 2014
4.4. DISORT (coupled with IMAGESv2)
The combination of RTM and CTM is not limited to LIDORT and GEOS-Chem; the
RTM Discrete Ordinates Radiative Transfer Program for a Multi-Layered PlaneParallel Medium (DISORT; Kylling, Stamnes, and Tsay 1995) and CTM IMAGESv2
(Müller and Stavrakou 2005) have also been employed in the calculation of the AMF
for the remote-sensing retrieval of HCHO. LIDORT is described as a reliable and
efficient two-stream algorithm for spherical radiative transfer. The specific differences
between LIDORT and DISORT are beyond the scope of this review, but LIDORT can
be seen as an improved, but increased in complexity version of the older DISORT
model. Again, the IMAGESv2 CTM is used in this step of the top-down BVOC
emissions estimates for the calculation of the vertical profiles of HCHO concentrations. The IMAGESv2 CTM, similar to GEOS-Chem, is also continuously being
updated with the incorporation of new features (De Smedt et al. 2008; Stavrakou
et al. 2009b, 2009a). IMAGESv2 combined with LIDORT is the standard operating
combination for GOME-2 products on the MetOp-A satellite (http://atmos.caf.dlr.de/
gome/product_hcho.html), while GEOS-Chem coupled with LIDORT is the current
processing used for OMI.
5. Isoprene-HCHO linkage via CTMs
Satellite retrievals of HCHO columns were first directed towards the detection of major
biomass burning where HCHO and other atmospheric emission products specific to
biomass burning were expected to temporarily dominate tropospheric chemistry in the
area (Thomas et al. 1998). As such, only the general potential for identifying sources of
HCHO was a focus and the link between HCHO and BVOCs was not the primary goal.
Subsequently, HCHO vertical column variability, though not necessarily quantitative
vertical column concentrations, was considered as a proxy for the hydroxyl radical, and,
in particular, integrated NMHC emissions over continental areas (Chance et al. 2000).
This was conjectured because of the dominance of combined NMHC emissions compared
to other potential sources of HCHO, with HCHO also being the dominant most immediate
product of NMHC oxidation, for, in particular, isoprene in the southeastern USA (Lee
et al. 1998).
The use of GEOS-Chem first focused on the identification of the atmospheric
vertical column profiles of HCHO for the calculation of the AMF, since that had
been identified as a major source of error in the calculation of the HCHO vertical
profile retrievals (Palmer et al. 2001). The groundwork for making the full linkage
from BVOC emissions to HCHO column retrievals was set in place by Palmer and
others shortly thereafter in 2003 (Palmer et al. 2003), by including emissions, lifetimes, HCHO yield, and HCHO production potential for numerous BVOCs, as well
as explanations of their pathways and validation from both literature and experimental data. They identified, specifically, a local linear relationship between HCHO
and isoprene emissions, where the intercept is based on a background from methane
oxidation, and the slope is determined by the HCHO yield from isoprene oxidation
and also the HCHO lifetime (Palmer et al. 2003). Since many environmental factors
affect this slope, it varies by location (Millet et al. 2006; Palmer et al. 2003),
especially with latitude (Barkley et al. 2008; Marais et al. 2012), and seasonally
(Barkley et al. 2008; Abbot et al. 2003), and is usually defined as part of a
corresponding field campaign.
7532
S.C. Kefauver et al.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
5.1. GEOS-Chem
The GEOS-Chem global 3D CTM uses the GEOS-4 assimilated meteorological data from
the NASA Goddard Earth Observing System of the Data Assimilation Office, including
winds, convective mass fluxes, mixing depths, temperature, precipitation, and other surface properties with a 6-hour temporal resolution, 1° by 1.25° horizontal resolution, and
55 vertical layers. The model incorporates these input environmental variables to control
every aspect of an integrated ozone-NOx-VOC chemistry, coupled to aerosols, and
predicting global ozone within approximately 10 ppb in different regions and altitudes
(Bey et al. 2001; Martin et al. 2002). The GEOS-Chem CTM is used in the remote
sensing of BVOCs not only to independently describe trace gas vertical profiles (Palmer
et al. 2001) for improved AMF calculations, but also to make the important final link
between BVOC emissions and the HCHO column satellite-based retrievals. This is a very
important final step since BVOCs are not directly detectable from satellite sensors, but
rather HCHO, the immediate dominant product of BVOCS, which can be measured using
remote-sensing techniques. GEOS-Chem utilizes modified versions of the GEIA database
(Guenther et al. 1995) or the newer MEGAN (Guenther et al. 2006) for linkages between
BVOC emissions and measured HCHO columns.
5.2. IMAGESv2 CTM
The IMAGES v2 CTM, an updated version of the original IMAGES model (Müller and
Brasseur 1999), originally developed for the three-dimensional study of the hydroxyl
radical, now incorporates the global distributions of 20 short-lived and 48 long-lived
atmospheric chemical compounds at a resolution of 5 degrees with 40 vertical layers
(Saunders et al. 2003). The chemical mechanisms for NMVOCs, specifically, were derived
and optimized for HCHO production from isoprene and pyrogenic emissions from studies
of the near-explicit Master Chemical Mechanism (MCM) (De Smedt et al. 2008; Stavrakou
et al. 2009b; Saunders et al. 2003). IMAGESv2 is driven by the meteorological data
provided by the European Centre for Medium-Range Weather Forecasts (ECMWF),
which in turn link to GEIA and MEGAN for biogenic emissions sources and the Global
Fore Emissions Database (GFED) versions 1 or 2 for pyrogenic (biomass burning) sources
of atmospheric compounds (Stavrakou et al. 2009a). Specifically, for BVOCs, the MEGANECMWF combination integrated into IMAGESv2 links emissions, based on the MEGAN
model, coupled with a detailed canopy environment model, the Model for Hydrocarbon
Emissions by the Canopy (MOHYCAN), for an improved calculation of leaf temperature
from the ECMWF data, represents a significant step forward towards a near-explicit linkage
between vertical column HCHO satellite retrievals and BVOC emissions. IMAGESv2 and
MCM yields were estimated to be approximately 20% higher than those predicted from
GEOS-Chem under high NOx conditions (Palmer et al. 2006), which had been validated
with a major aircraft measurement campaign in North America (Millet et al. 2006). GEOSChem, on the other hand, was found to have significantly biased yields under low NOx
conditions (Palmer et al. 2006).
6. Bottom-up satellite-aided emissions inventories
As mentioned in Section 1.5, there are several bottom-up methods developed for remotesensing-aided studies of BVOCs emissions. Rather than measure atmospheric total vertical column HCHO directly with a satellite and then make the link from atmospheric
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7533
HCHO to terrestrial BVOC emissions using a CTM, the bottom-up inventories use
remote-sensing data to improve land-cover databases, which are then linked with estimates of emissions per land-cover class. Apart from emissions based on vegetation type or
land-cover class, there are also remote-sensing methods that can accurately assess plant
physiological status and provide improved indirect estimates of BVOC emissions
(Peñuelas et al. 2013) while changing depending on environmental factors such as pests
(Pichersky and Gershenzon 2002), light (Peñuelas and Munné-Bosch 2005), drought, and
temperature (Sharkey and Singsaas 1995; Loreto et al. 1998; Tingey, Turner, and Weber
1991; Llusia and Penuelas 2000; Sharkey et al. 1996; Singsaas 2000). Peñuelas et al.
(2013) propose the use of the photochemical reflectance index used as a proxy of light use
efficiency (Gamon, Penuelas, and Field 1992; Peñuelas, Filella, and Gamon 1995;
Peñuelas, Garbulsky, and Filella 2011; Garbulsky et al. 2011) and also as an indirect
estimator of isoprenoid emissions, since they increase with increasing excess of reducing
power resulting from reduced photosynthetic sink strength. The top-down and the bottomup BVOC methods can be used independently for comparisons and validation, or the topdown can be considered a constraint of the bottom-up inventories (Marais et al. 2012;
Palmer et al. 2003; Shim et al. 2005). However, they can also function together, as shown
with the incorporation of GEIA and MEGAN BVOC vegetation land-cover class inventories being incorporated into the IMAGESv2 CTM (De Smedt et al. 2008, 2012; Müller
and Stavrakou 2005; Stavrakou et al. 2009b).
7. Filtering non-biogenic sources of atmospheric formaldehyde
While 60% of the global troposphere background HCHO levels is due to the oxidation
of methane, the long lifetime of methane results in a near uniform distribution, and
locally emitted NMVOCs dominate patterns of variability over continental areas
(Stavrakou et al. 2009a). Over continental areas, it is estimated that 85% is due to
biogenic sources, while the remaining 15% is split between anthropogenic activities and
vegetation fires (Guenther et al. 1995, 2006). Still, even with the impacts of methane
oxidation aside, this represents a significant source of error in correlating satellite
HCHO retrievals with BVOCs. Two major approaches have so far been developed for
the separation of biogenic sources of atmospheric HCHO from other potential contributions (Figure 1). Marais et al. (2012) used a separate series of HCHO filters, at varying
scales, over the extent of Africa, to discard potential point sources and modify tropospheric oxidation processes, especially for low NOx situations – as before mentioned a
weakness in the GEOS-Chem atmospheric-vegetation linkage – to provide a series of
estimates of non-biogenic VOC contributions. After a standard HCHO vertical column
retrieval from OMI, MODIS day and night fire counts and OMI smoke aerosol absorption optical depth (AAOD) data were used to screen isoprene sources from urban
biomass burning. Then gas fire anthropogenic contributions were removed using the
Advanced Along Track Scanning Radiometer satellite thermal band data at 1.6 µm.
Finally, the OMI AAOD dust product was used to remove any potential influence for
local dust (of significance in Africa in particular), leaving the remainder attributed to
biogenic contributions to the calculated isoprene totals. In contrast, Stavrakou et al.
(2009a) investigated the potential for separating methanogenic, anthropogenic, pyrogenic, and biogenic contributions to total HCHO columns with the incorporation of
several ancillary databases into the IMAGESv2 CTM (detailed in Section 6.2). Two
biomass burning databases, GFEDv1 and GFEDv2, and two biogenic inventories,
GEIA and MEGAN, were tested in combination with IMAGESv2, with ancillary
7534
S.C. Kefauver et al.
climatological data provided by the ECMWF. While the linked MEGAN-ECMWF
combination was seen as an improvement over GEIA in most regions, neither GFED
version showed consistent global improvements over the other, with GFEDv2 stronger
over parts of Africa and Indonesia and GFEDv1 managing better over the Amazon
basin.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
8. Discussion
Improvements in the quantitative remote-sensing-retrievals HCHO vertical column and
the subsequent linkage to BVOCs have come from fairly recent advances in both processing techniques and sensor technology (Chance et al. 2000; Abbot et al. 2003; Shim et al.
2005; Fu et al. 2007; Marais et al. 2012). The first results were produced using the GOME
sensor with only geometric AMFs (no RTM incorporation) and a newly developed version
of the Goddard Earth Observatory System Chemistry (GEOS-Chem) CTM. It was noted
that the large pixel sizes of GOME were a major source of error (cloud contamination was
ubiquitous), and, along with detector issues (diffuser plate artefacts) and AMF inaccuracies, lead to a ~40% error in HCHO estimates from GOME. The launching of new and
improved satellites and sensors such as SCIAMACHY, OMI, and GOME-2 has helped
lower the HCHO errors in regards to some of the first sensor-specific issues in GOME. All
three offered improved spatial and spectral resolutions compared to GOME (Table 1),
which improved both the quality of the DOAS spectral fit and the ability to avoid clouded
or partially clouded pixels, resulting in an much lower error overall (Millet et al. 2008).
Comparison of the current operational sensors, OMI and GOME-2, found HCHO retrievals to be comparable and consistent with OMI 2–14% lower, on average, as might be
expected with a later overpass time. Fine tuning of the DOAS spectral fit to take
advantage of the increased spectral resolution and signal to noise of newer sensors has
significantly reduced errors in the slant column retrieval in the first phase (Hewson et al.
2013). Development of improvements to the AMF calculations for the second phase,
including the direct incorporation of both RTMs and CTMs, has greatly reduced the
uncertainties in the final HCHO vertical columns (Barkley et al. 2012; Palmer et al. 2001).
Finally, improvements and new developments in CTMs (Bey et al. 2001; Potter et al.
2001; Müller and Stavrakou 2005; Müller and Brasseur 1999) and the separation of
AVOCs from BVOCs (Stavrakou et al. 2009a; Marais et al. 2012) have improved the
key final HCHO-BVOC linkages in the third and final phase.
Separation of pyrogenic and anthropogenic from BVOC sources of atmospheric
formaldehyde, an important step assessing biogenic emissions, has been implemented
and tested at the continent and global scales both as stepwise processes and via direct
integration with vegetation–atmosphere interaction and CTMs (Marais et al. 2012;
Stavrakou et al. 2009a). In low NOx situations, spatial smearing may occur because of
an increase in the lifetime of pre-HCHO intermediates (Palmer et al. 2003). While the low
NOx smearing can be corrected and the alternative chemical pathways are generally wellunderstood, it can present complications on a number of levels (Barkley et al. 2009, 2008;
Palmer et al. 2003). For example, a detailed analysis of short-lived atmospheric species
effects on modelled column concentrations found that achieving a 90% accurate column
estimate required taking into account a minimal geographic range of at least 500 km
(Turner et al. 2012), with HCHO specifically estimated to require an even larger range.
NOx-related complications are reduced in industrialized regions, but BVOCs emissions
predominately derive from the tropics, making this a major issue for producing accurate
global BVOC (or isoprene) inventories.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7535
Not only have national and international space agencies greatly improved the pre-processing and availability of satellite data related to the remote sensing of BVOCs via formaldehyde, but excellent scientific research, both theoretical and applied, has been produced from
this data, especially in the recent 6 to 8 years since the launching of the second-generation
atmospheric chemistry satellites OMI and GOME-2 (De Smedt et al. 2012; Millet et al. 2008;
Hewson et al. 2013). Comparisons and cross-calibrations between first and second generation
sensors has extended insights gained from the second generation to improvements of the
historical record going back nearly 20 years (De Smedt et al. 2008, 2012).
There are abundant available tools and data access portals for the remote sensing of
BVOCs that are made available freely by the various space agencies which have supported these satellites and sensors. Both ESA and NASA have several web portals
providing data download (ESA: MyEarthNet http://earth.esa.int; NASA: Mirador: http://
mirador.gsfc.nasa.gov/), visualization, and even online analysis resources GIOVANNI:
http://disc.sci.gsfc.nasa.gov/Giovanni). Freely provided and open-source analysis tools
offered by these agencies and the scientific community for promoting the remote sensing
of atmospheric chemistry, including BVOCs, such as the Basic EVISAT Atmospheric
Toolbox (BEAT: http://www.stcorp.nl/beat/), the NASA Goddard Earth Sciences Data and
Information Center Aura tools repository (NASA GES DISC: http://disc.sci.gsfc.nasa.gov/
Aura/additional/tools.shtml), the Hierarchical Data Format – Earth Observation Science
(HDF-EOS) Tools and Information Center (http://hdfeos.org/index.php), and the
Atmospheric Parameters Measured by in-Orbit Spectroscopy server (ATMOS) of the
(DLR) Department of Atmospheric Processors (AP) of the German Space Agency
(DLR) Remote Sensing Technology Institute (IMF), which provides additional data access
and support for GOME, SCIAMACHY, and GOME-2 (http://atmos.caf.dlr.de/).
9. Future directions
Recent advancements in our understanding of BVOCs from an eco-physiological perspective related plant stress, as well their roles in air quality and climate change,
demonstrates a greater need for increased research more than ever before (Peñuelas and
Staudt 2010). Increasingly explicit vegetation–atmosphere and vegetation–environment
models build off of our increased understanding of the eco-physiology of BVOCs and
bring new meaning to remotely sensed components of the atmosphere such as formaldehyde (Guenther et al. 2012). Research suggests increasing biogenic emissions in coming
years with predicted climate change scenarios, increasing possible implications and
impacts of BVOCs on the surrounding environment (Penuelas and Llusià 2003). Other
research suggests that the increased BVOCs in natural vegetation may be offset by
decreases in agroforestry and invasive species’ volatile plant emissions due to increased
CO2 levels (Rosenstiel et al. 2003). Still, the potential for both positive or negative
feedbacks under different scenarios exists between plant BVOCs emissions and both
global air quality and climate change (Peñuelas and Llusià 2003; Peñuelas and Staudt
2010). The role of BVOCs in the atmosphere varies greatly between NOx-enriched and
NOx-limited environments (Peeters, Nguyen, and Vereecken 2009), providing evidence
that more directly connects a variety of anthropogenic activities to their role in atmospheric chemistry.
Glyoxal (CHOCHO), another oxygenated VOC measureable directly via satellite
observation, also shows a marked increase in column amounts over areas with increased
BVOC emissions, including tropical oceans, as measured by the second generation
atmospheric chemistry satellites OMI (Kurosu et al. 2007) and GOME-2 (Vrekoussis
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7536
S.C. Kefauver et al.
et al. 2010). Furthermore, the ratio between glyoxal and formaldehyde column amounts
(Rgf) has been shown to show potential for aiding in the discrimination between anthropogenic and biogenic sources, with an Rgf between 0.040 and 0.060 indicative of a greater
level of BVOCs emissions and Rgf below 0.040 indicative of high NOx and anthropogenic
emissions activity (Vrekoussis et al. 2010). These new satellite observation capabilities
and correlation between two vertical columns and different emission sources provides
another promising angle with which to approach global BVOCs assessments.
Investigating the diurnal variations of BVOCs is also of interest and is planned as part
of NASA’s Geostationary Coastal and Air Pollution Events Mission in response to the US
National Resource Council’s Earth Science Decadal Survey (Fishman et al. 2012). Related
to the remote sensing of BVOCS, in particular, the temporal and spatial variations of
emissions of gases and aerosols important for air quality and climate, including the natural
and anthropogenic precursors of tropospheric ozone and aerosol precursors, was identified
as one of the four major objectives of the decadal survey. Specifically, the atmospheric
science traceability matrix detailed a goal of thrice daily formaldehyde and twice daily
glyoxal measurements. The current state of the science allows for daily global coverage at
optimal times of day, but, indeed, comparisons between some sensors has been hindered
by the strong diurnal variations in BVOCS due not only to variations in emissions from
terrestrial vegetation, but also the interconnectedness of BVOCs with the strong diurnal
cycles of hydroxyl radicals and the production of tropospheric ozone.
Key improvements strengthen our ability to extrapolate BVOCs from satellite-based
formaldehyde vertical columns, allowing for the production of data accurate enough to be
relevant for testing models potential changes related to estimated global ecological and
climate change, linking IPCC scenarios with vegetation feedback mechanisms and air
quality implications (Young et al. 2009). Corresponding improvements in other atmospheric components that contribute to the production of photochemical smog allows for
the identification of tropospheric ozone limiting factors on a global scale (Young et al.
2009; Fishman et al. 2008). New evidence increasingly links both the emission levels and
environmental impacts of BVOCs to climate change and global anthropogenic activities
and much is still unknown (Peñuelas and Llusià 2003; Peñuelas and Staudt 2010).
Funding
This work was supported by the Spanish Government [grant number CGL2010-17172/BOS] and
Consolider-Ingenio Montes [grant number CSD2008-00040]; and by the Catalan Government
project SGR 2009-458.
Disclosures
The authors state no conflicts of interest.
References
Abbot, D. S., P. I. Palmer, R. V. Martin, K. V. Chance, D. J. Jacob, and A. Guenther. 2003.
“Seasonal and Interannual Variability of North American Isoprene Emissions as Determined by
Formaldehyde Column Measurements from Space.” Geophysical Research Letters 30 (17):
1886. doi:10.1029/2003GL017336.
Ahmad, S. P., P. F. Levelt, P. K. Bhartia, E. Hilsenrath, G. W. Leppelmeier, and J. E. Johnson. 2003.
“Atmospheric Products from the Ozone Monitoring Instrument (OMI).” Paper presented at the
Optical Science and Technology, SPIE’s 48th annual meeting, San Diego, CA, August 3–8.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7537
Aliwell, S. R., M. Van Roozendael, P. V. Johnston, A. Richter, T. Wagner, D. W. Arlander, J. P.
Burrows, D. J. Fish, R. L. Jones, and K. K. Tørnkvist. 2002. “Analysis for Bro in Zenith‐Sky
Spectra: An Intercomparison Exercise for Analysis Improvement.” Journal of Geophysical
Research: Atmospheres (1984–2012) 107 (D14): ACH10-1-ACH-20. doi:10.1029/
2001JD000329.
Barkley, M. P., I. De Smedt, M. Van Roozendael, T. P. Kurosu, K. Chance, A. Arneth, D. Hagberg,
A. Guenther, F. Paulot, and E. Marais. 2013. “Top‐Down Isoprene Emissions over Tropical
South America Inferred from SCIAMACHY and OMI Formaldehyde Columns.” Journal of
Geophysical Research: Atmospheres D13304: 1–13.
Barkley, M. P., T. P. Kurosu, K. Chance, I. De Smedt, M. Van Roozendael, A. Arneth, D. Hagberg,
and A. Guenther. 2012. “Assessing Sources of Uncertainty in Formaldehyde Air Mass Factors
over Tropical South America: Implications for Top‐Down Isoprene Emission Estimates.”
Journal of Geophysical Research: Atmospheres (1984–2012) 117 (D13304): 1–13.
doi:10.1029/2011JD016827.
Barkley, M. P., P. I. Palmer, I. De Smedt, T. Karl, A. Guenther, and M. Van Roozendael. 2009.
“Regulated Large-Scale Annual Shutdown of Amazonian Isoprene Emissions?” Geophysical
Research Letters 36 (4): L04803. doi:10.1029/2008GL036843.
Barkley, M. P., P. I. Palmer, U. Kuhn, J. Kesselmeier, K. Chance, T. P. Kurosu, R. V. Martin, D.
Helmig, and A. Guenther. 2008. “Net Ecosystem Fluxes of Isoprene over Tropical South
America Inferred from Global Ozone Monitoring Experiment (GOME) Observations of
HCHO Columns.” Journal of Geophysical Research: Atmospheres (1984–2012) 113 (D20).
doi:10.1029/2008JD009863.
Bey, I., D. J. Jacob, R. M. Yantosca, J. A. Logan, B. D. Field, A. M. Fiore, Q. Li, H. Y. Liu, L. J.
Mickley, and M. G. Schultz. 2001. “Global Modeling of Tropospheric Chemistry with
Assimilated Meteorology: Model Description and Evaluation.” Journal of Geophysical
Research 106 (D19): 23073–23095. doi:10.1029/2001JD000807.
Bogumil, K., J. Orphal, T. Homann, S. Voigt, P. Spietz, O. C. Fleischmann, A. Vogel, M. Hartmann,
H. Kromminga, H. Bovensmann, J. Frerick, and J. P. Burrows. 2003. “Measurements of
Molecular Absorption Spectra with the SCIAMACHY Pre-Flight Model: Instrument
Characterization and Reference Data for Atmospheric Remote-Sensing in the 230–2380 Nm
Region.” Journal of Photochemistry and Photobiology A: Chemistry 157 (2–3): 167–184.
doi:10.1016/S1010-6030(03)00062-5.
Burrows, J. P., M. Weber, M. Buchwitz, V. Rozanov, A. Ladstätter-Weißenmayer, A. Richter, R.
DeBeek, R. Hoogen, K. Bramstedt, K.-U. Eichmann, M. Eisinger, and D. Perner. 1999. “The
Global Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific Results.”
Journal of the Atmospheric Sciences 56 (2): 151–175. doi:10.1175/1520-0469(1999)056<0151:
TGOMEG>2.0.CO;2.
Chance, K. 1998. “Analysis of Bro Measurements from the Global Ozone Monitoring Experiment.”
Geophysical Research Letters 25 (17): 3335–3338. doi:10.1029/98GL52359.
Chance, K., P. I. Palmer, R. J. D. Spurr, R. V. Martin, T. P. Kurosu, and D. J. Jacob. 2000. “Satellite
Observations of Formaldehyde over North America from GOME.” Geophysical Research
Letters 27 (21): 3461–3464. doi:10.1029/2000GL011857.
Chance, K. V., and R. J. D. Spurr. 1997. “Ring Effect Studies: Rayleigh Scattering, Including
Molecular Parameters for Rotational Raman Scattering, and the Fraunhofer Spectrum.” Applied
Optics 36 (21): 5224–5230. doi:10.1364/AO.36.005224.
De Smedt, I., J.-F. Müller, T. Stavrakou, R. van der A, H. Eskes, and M. Van Roozendael. 2008.
“Twelve Years of Global Observations of Formaldehyde in the Troposphere Using GOME and
SCIAMACHY Sensors.” Atmospheric Chemistry and Physics 8 (16): 4947–4963. doi:10.5194/
acp-8-4947-2008.
De Smedt, I., M. Van Roozendael, T. Stavrakou, J.-F. Müller, C. Lerot, N. Theys, P. Valks, N. Hao, and
R. Van Der A. 2012. “Improved Retrieval of Global Tropospheric Formaldehyde Columns from
GOME-2/MetOp-A Addressing Noise Reduction and Instrumental Degradation Issues.”
Atmospheric Measurement Techniques Discussions 5: 5571–5616. doi:10.5194/amtd-5-5571-2012.
Diem, J. E. 2002. “Remote Assessment of Forest Health in Southern Arizona, USA: Evidence for
Ozone-Induced Foliar Injury.” Environmental Management 29 (3): 373–384. doi:10.1007/
s00267-001-0011-5.
Diem, J. E., and A. C. Comrie. 2002. “Predictive Mapping of Air Pollution Involving Sparse Spatial
Observations.” Environmental Pollution 119 (1): 99–117. doi:10.1016/S0269-7491(01)00308-6.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7538
S.C. Kefauver et al.
Fehsenfeld, F., J. Calvert, R. Fall, P. Goldan, A. B. Guenther, C. Nicholas Hewitt, B. Lamb, S. Liu,
M. Trainer, H. Westberg, and P. Zimmerman. 1992. “Emissions of Volatile Organic Compounds
from Vegetation and the Implications for Atmospheric Chemistry.” Global Biogeochemical
Cycles 6 (4): 389–430. doi:10.1029/92GB02125.
Fishman, J., J. A. Al-Saadi, J. K. Creilson, K. W. Bowman, J. P. Burrows, A. Richter, K. V. Chance,
D. P. Edwards, R. V. Martin, G. A. Morris, R. B. Pierce, J. R. Ziemke, T. K. Schaack, and A. M.
Thompson. 2008. “Remote Sensing of Tropospheric Pollution from Space.” Bulletin of the
American Meteorological Society 89 (6): 805–821. doi:10.1175/2008bams2526.1.
Fishman, J., L. T. Iraci, J. Al-Saadi, K. Chance, F. Chavez, M. Chin, P. Coble, C. Davis, P. M.
DiGiacomo, D. Edwards, A. Eldering, J. Goes, J. Herman, C. Hu, D. J. Jacob, C. Jordan, S. R.
Kawa, R. Key, X. Liu, S. Lohrenz, A. Mannino, V. Natraj, D. Neil, J. Neu, M. Newchurch, K.
Pickering, J. Salisbury, H. Sosik, A. Subramaniam, M. Tzortziou, J. Wang, and M. Wang. 2012.
“The United States’ Next Generation of Atmospheric Composition and Coastal Ecosystem
Measurements: Nasa’s Geostationary Coastal and Air Pollution Events (GEO-CAPE)
Mission.” Bulletin of the American Meteorological Society 93 (10): 1547–1566. doi:10.1175/
BAMS-D-11-00201.1.
Fu, T.-M., D. J. Jacob, P. I. Palmer, K. Chance, Y. X. Wang, B. Barletta, D. R. Blake, J. C. Stanton,
and M. J. Pilling. 2007. “Space‐Based Formaldehyde Measurements as Constraints on Volatile
Organic Compound Emissions in East and South Asia and Implications for Ozone.” Journal of
Geophysical Research: Atmospheres (1984–2012) 112 (D6). doi:10.1029/2006JD007853.
Gamon, J. A., J. Penuelas, and C. B. Field. 1992. “A Narrow-Waveband Spectral Index that Tracks
Diurnal Changes in Photosynthetic Efficiency.” Remote Sensing of Environment 41 (1): 35–44.
doi:10.1016/0034-4257(92)90059-S.
Garbulsky, M. F., J. Peñuelas, J. Gamon, Y. Inoue, and I. Filella. 2011. “The Photochemical
Reflectance Index (PRI) and the Remote Sensing of Leaf, Canopy and Ecosystem Radiation
Use Efficiencies: A Review and Meta-Analysis.” Remote Sensing of Environment 115 (2): 281–
297. doi:10.1016/j.rse.2010.08.023.
Geron, C. D., A. B. Guenther, and T. E. Pierce. 1994. “An Improved Model for Estimating
Emissions of Volatile Organic Compounds from Forests in the Eastern United States.”
Journal of Geophysical Research: Atmospheres (1984–2012) 99 (D6): 12773–12791.
doi:10.1029/94JD00246.
Guenther, A., C. Geron, T. Pierce, B. Lamb, P. Harley, and R. Fall. 2000. “Natural Emissions of
Non-Methane Volatile Organic Compounds, Carbon Monoxide, and Oxides of Nitrogen from
North America.” Atmospheric Environment 34 (12–14): 2205–2230. doi:10.1016/S1352-2310
(99)00465-3.
Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron. 2006. “Estimates of
Global Terrestrial Isoprene Emissions Using MEGAN (Model of Emissions of Gases and
Aerosols from Nature).” Atmospheric Chemistry and Physics 6 (11): 3181–3210. doi:10.5194/
acp-6-3181-2006.
Guenther, A., C. Nicholas Hewitt, D. Erickson, R. Fall, C. Geron, T. Graedel, P. Harley, L. Klinger,
M. Lerdau, W. A. McKay, T. Pierce, B. Scholes, R. Steinbrecher, R. Tallamraju, J. Taylor, and P.
Zimmerman. 1995. “A Global Model of Natural Volatile Organic Compound Emissions.”
Journal of Geophysical Research 100 (D5): 8873–8892. doi:10.1029/94JD02950.
Guenther, A. B., X. Jiang, C. L. Heald, T. Sakulyanontvittaya, T. Duhl, L. K. Emmons, and X. Wang.
2012. “The Model of Emissions of Gases and Aerosols from Nature Version 2.1 (MEGAN2. 1):
An Extended and Updated Framework for Modeling Biogenic Emissions.” Geoscientific Model
Development Discussions 5 (2): 1503–1560. doi:10.5194/gmdd-5-1503-2012.
Hayden, B. P.. 1998. “Ecosystem Feedbacks on Climate at the Landscape Scale.” Philosophical
Transactions of the Royal Society B: Biological Sciences 353 (1365): 5–18. doi:10.1098/
rstb.1998.0186.
Hewson, W., H. Bösch, M. P. Barkley, and I. De Smedt. 2013. “Characterisation of GOME-2
Formaldehyde Retrieval Sensitivity.” Atmospheric Measurement Techniques 6 (2): 371–386.
doi:10.5194/amt-6-371-2013.
Houweling, S., F. Dentener, and J. Lelieveld. 1998. “The Impact of Nonmethane Hydrocarbon
Compounds on Tropospheric Photochemistry.” Journal of Geophysical Research 103 (D9):
10673–10696. doi:10.1029/97JD03582.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7539
Kinnee, E., C. Geron, and T. Pierce. 1997. “United States Land Use Inventory for Estimating
Biogenic Ozone Precursor Emissions.” Ecological Applications 7 (1): 46–58. doi:10.1890/10510761(1997)007[0046:USLUIF]2.0.CO;2.
Kurosu, T. P., K. Chance, X. Liu, R. Volkamer, F. Tzung-May, D. Millet, D. J. Jacob, P. Levelt, J. C.
N. Epiphanio, and L. S. Galvão. 2007. “Seasonally Resolved Global Distributions of Glyoxal
and Formaldehyde Observed from the Ozone Monitoring Instrument on EOS Aura.” Simpósio
Brasileiro de Sensoriamento Remoto 13 (SBSR): 6461–6464.
Kurosu, T. P., K. Chance, and R. Volkamer. 2005. “Global measurements of BrO, HCHO, and
CHOCHO from the Ozone Monitoring Instrument on EOS Aura.” Paper presented at the AGU
Fall Meeting Abstracts, San Francisco, CA, December 5–9.
Kylling, A., K. Stamnes, and S. C. Tsay. 1995. “A Reliable and Efficient Two-Stream Algorithm for
Spherical Radiative Transfer: Documentation of Accuracy in Realistic Layered Media.” Journal
of Atmospheric Chemistry 21 (2): 115–150. doi:10.1007/BF00696577.
Lambert, J. C., and M. Koukouli. 2012. “TN-IASB-GOME-GDP5-VR.” European Space Agency.
Accessed October 7, 2014. http://atmos.eoc.dlr.de/gome/docs/DLR_GOME_GDP5_RFM.pdf
Lee, C., Y. J. Kim, S.-B. Hong, H. Lee, J. Jung, Y.-J. Choi, J. Park, K.-H. Kim, J.-H. Lee, K.-J.
Chun, and -H.-H. Kim. 2005. “Measurement of Atmospheric Formaldehyde and Monoaromatic
Hydrocarbons Using Differential Optical Absorption Spectroscopy during Winter and Summer
Intensive Periods in Seoul, Korea.” Water, Air, and Soil Pollution 166 (1–4): 181–195.
doi:10.1007/s11270-005-7308-6.
Lee, Y.-N., X. Zhou, L. I. Kleinman, L. J. Nunnermacker, S. R. Springston, P. H. Daum, L.
Newman, W. G. Keigley, M. W. Holdren, C. W. Spicer, V. Young, B. Fu, D. D. Parrish, J.
Holloway, J. Williams, J. M. Roberts, T. B. Ryerson, and F. C. Fehsenfeld. 1998. “Atmospheric
Chemistry and Distribution of Formaldehyde and Several Multioxygenated Carbonyl
Compounds during the 1995 Nashville/Middle Tennessee Ozone Study.” Journal of
Geophysical Research: Atmospheres (1984–2012) 103 (D17): 22449–22462. doi:10.1029/
98JD01251.
Levelt, P. F., G. H. J. Van Den Oord, M. R. Dobber, A. Malkki, H. Visser, J. De Vries, P. Stammes,
J. O. V. Lundell, and H. Saari. 2006. “The Ozone Monitoring Instrument.” IEEE Transactions
on Geoscience and Remote Sensing 44 (5): 1093–1101. doi:10.1109/TGRS.2006.872333.
Llusia, J., and J. Penuelas. 2000. “Seasonal Patterns of Terpene Content and Emission from Seven
Mediterranean Woody Species in Field Conditions.” American Journal of Botany 87 (1): 133–
140. doi:10.2307/2656691.
Loreto, F., A. Forster, M. Durr, O. Csiky, and G. Seufert. 1998. “On the Monoterpene Emission
under Heat Stress and on the Increased Thermotolerance of Leaves of Quercus ilex L.
Fumigated with Selected Monoterpenes.” Plant, Cell & Environment 21 (1): 101–107.
doi:10.1046/j.1365-3040.1998.00268.x.
Marais, E. A., D. J. Jacob, T. P. Kurosu, K. Chance, J. G. Murphy, C. Reeves, G. Mills, S. Casadio,
D. B. Millet, M. P. Barkley, F. Paulot, and J. Mao. 2012. “Isoprene Emissions in Africa Inferred
from OMI Observations of Formaldehyde Columns.” Atmospheric Chemistry and Physics 12
(14): 6219–6235. doi:10.5194/acp-12-6219-2012.
Marbach, T., S. Beirle, M. J. Penning De Vries, and T. Wagner. 2010. “Sources and Trends of
Tropospheric Formaldehyde (HCHO) Derived from GOME-1 And-2.” Paper presented at the
AGU Fall Meeting Abstracts, San Francisco, CA, December 13–17.
Martin, R. V., K. Chance, D. J. Jacob, T. P. Kurosu, R. J. D. Spurr, E. Bucsela, J. F. Gleason, P. I.
Palmer, I. Bey, and A. M. Fiore. 2002. “An Improved Retrieval of Tropospheric Nitrogen
Dioxide from GOME.” Journal of Geophysical Research: Atmospheres (1984–2012) 107
(D20): ACH 9-1-ACH 9-21. doi:10.1029/2001JD001027.
Millet, D. B., D. J. Jacob, K. Folkert Boersma, T.-M. Fu, T. P. Kurosu, K. Chance, C. L. Heald, and
A. Guenther. 2008. “Spatial Distribution of Isoprene Emissions from North America Derived
from Formaldehyde Column Measurements by the OMI Satellite Sensor.” Journal of
Geophysical Research: Atmospheres (1984–2012) 113 (D2). doi:10.1029/2007JD008950.
Millet, D. B., D. J. Jacob, S. Turquety, R. C. Hudman, S. Wu, A. Fried, J. Walega, B. G. Heikes, D.
R. Blake, H. B. Singh, B. E. Anderson, and A. D. Clarke. 2006. “Formaldehyde Distribution
over North America: Implications for Satellite Retrievals of Formaldehyde Columns and
Isoprene Emission.” Journal of Geophysical Research 111 (D24). doi:10.1029/2005jd006853.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7540
S.C. Kefauver et al.
Monson, R. K., and R. Fall. 1989. “Isoprene Emission from Aspen Leaves : Influence of
Environment and Relation to Photosynthesis and Photorespiration.” Plant Physiology 90 (1):
267–274. doi:10.1104/pp.90.1.267.
Müller, J.-F., and G. Brasseur. 1999. “Sources of Upper Tropospheric HO X: A Three-Dimensional
Study.” Journal of Geophysical Research 104 (D1): 1705–1715. doi:10.1029/1998JD100005.
Müller, J.-F., and T. Stavrakou. 2005. “Inversion of CO and Nox Emissions Using the Adjoint of the
IMAGES Model.” Atmospheric Chemistry and Physics 5 (5): 1157–1186. doi:10.5194/acp-51157-2005.
Munger, J. W., D. J. Jacob, B. C. Daube, L. W. Horowitz, W. C. Keene, and B. G. Heikes. 1995.
“Formaldehyde, Glyoxal, and Methylglyoxal in Air and Cloudwater at a Rural Mountain Site in
Central Virginia.” Journal of Geophysical Research: Atmospheres (1984–2012) 100 (D5):
9325–9333. doi:10.1029/95JD00508.
Munro, R., M. Eisinger, C. Anderson, J. Callies, E. Corpaccioli, R. Lang, A. Lefebvre, Y. Livschitz,
and A. P. Albiñana. 2006. “GOME-2 on MetOp.” Paper presented at the proceedings of the
2006 EUMETSAT Meteorological Satellite Conference, Helsinki, June 12–16.
Oetjen, H., F. Wittrock, A. Richter, M. P. Chipperfield, T. Medeke, N. Sheode, B.-M. Sinnhuber, M.
Sinnhuber, and J. P. Burrows. 2011. “Evaluation of Stratospheric Chlorine Chemistry for the
Arctic Spring 2005 Using Modelled and Measured Oclo Column Densities.” Atmospheric
Chemistry and Physics 11: 689–703. doi:10.5194/acp-11-689-2011.
Olivier, J., J. Peters, C. Granier, G. Pétron, J. F. Müller, and S. Wallens 2003. Present and Future
Surface Emissions of Atmospheric Compounds. Report #2, Eu Project Evk2-1999-00011, POET
Emission Inventory Report. http://www.aero.jussieu.fr/projet/ACCENT/POET.php
Palmer, P. I., D. S. Abbot, T.-M. Fu, D. J. Jacob, K. Chance, T. P. Kurosu, A. Guenther, C.
Wiedinmyer, J. C. Stanton, M. J. Pilling, S. N. Pressley, B. Lamb, and A. L. Sumner. 2006.
“Quantifying the Seasonal and Interannual Variability of North American Isoprene Emissions
Using Satellite Observations of the Formaldehyde Column.” Journal of Geophysical Research
111 (D12). doi:10.1029/2005jd006689.
Palmer, P. I., D. J. Jacob, A. M. Fiore, R. V. Martin, K. Chance, and T. P. Kurosu. 2003. “Mapping
Isoprene Emissions over North America Using Formaldehyde Column Observations from
Space.” Journal of Geophysical Research: Atmospheres (1984–2012) 108 (D6). doi:10.1029/
2002JD002153.
Palmer, P. I., D. J. Jacob, K. Chance, R. V. Martin, R. J. D. Spurr, T. P. Kurosu, I. Bey, R. Yantosca,
A. Fiore, and Q. Li. 2001. “Air Mass Factor Formulation for Spectroscopic Measurements from
Satellites: Application to Formaldehyde Retrievals from the Global Ozone Monitoring
Experiment.” Journal of Geophysical Research: Atmospheres (1984–2012) 106 (D13):
14539–14550. doi:10.1029/2000JD900772.
Peeters, J., T. L. Nguyen, and L. Vereecken. 2009. “Hox Radical Regeneration in the Oxidation of
Isoprene.” Physical Chemistry Chemical Physics 11 (28): 5935–5939. doi:10.1039/b908511d.
Peñuelas, J., I. Filella, and J. A. Gamon. 1995. “Assessment of Photosynthetic Radiation‐Use
Efficiency with Spectral Reflectance.” New Phytologist 131 (3): 291–296. doi:10.1111/j.14698137.1995.tb03064.x.
Peñuelas, J., M. F. Garbulsky, and I. Filella. 2011. “Photochemical Reflectance Index (PRI) and
Remote Sensing of Plant CO2 Uptake.” New Phytologist 191 (3): 596–599. doi:10.1111/j.14698137.2011.03791.x.
Peñuelas, J., and J. Llusià. 2003. “Bvocs: Plant Defense against Climate Warming?” Trends in Plant
Science 8 (3): 105–109. doi:10.1016/S1360-1385(03)00008-6.
Peñuelas, J., G. Marino, J. LLusia, C. Morfopoulos, G. Farré-Armengol, and I. Filella. 2013.
“Photochemical Reflectance Index as an Indirect Estimator of Foliar Isoprenoid Emissions at
the Ecosystem Level.” Nature Communications 4. doi:10.1038/ncomms3604.
Penuelas, J., and S. Munnebosch. 2005. “Isoprenoids: An Evolutionary Pool for Photoprotection.”
Trends in Plant Science 10 (4): 166–169. doi:10.1016/j.tplants.2005.02.005.
Peñuelas, J., and M. Staudt. 2010. “Bvocs and Global Change.” Trends in Plant Science 15 (3):
133–144. doi:10.1016/j.tplants.2009.12.005.
Perner, D., T. Kluptel, E. Hegels, P. J. Crutzen, and J. P. Burrows. 1997. “First Results on
Tropospheric Observations by the Global Ozone Monitoring Experiment, GOME, on ERS 2.”
Paper presented at the ESA SP, Florence, March 14–21.
Downloaded by [85.58.31.116] at 11:20 05 November 2014
International Journal of Remote Sensing
7541
Pichersky, E., and J. Gershenzon. 2002. “The Formation and Function of Plant Volatiles: Perfumes
for Pollinator Attraction and Defense.” Current Opinion in Plant Biology 5 (3): 237–243.
doi:10.1016/S1369-5266(02)00251-0.
Platt, U., D. Perner, and H. W. Pätz. 1979. “Simultaneous Measurement of Atmospheric CH2O, O3,
and NO2 by Differential Optical Absorption.” Journal of Geophysical Research: Oceans
(1978–2012) 84 (C10): 6329–6335. doi:10.1029/JC084iC10p06329.
Platt, U., and J. Stutz. 2008. Differential Absorption Spectroscopy, 597 pp. Heidelberg: Springer.
ISBN 978-3540211938.
Poisson, N., M. Kanakidou, and P. J. Crutzen. 2000. “Impact of Non-Methane Hydrocarbons on
Tropospheric Chemistry and the Oxidizing Power of the Global Troposphere: 3-Dimensional
Modelling Results.” Journal of Atmospheric Chemistry 36 (2): 157–230. doi:10.1023/
A:1006300616544.
Potter, C. S., S. E. Alexander, J. C. Coughlan, and S. A. Klooster. 2001. “Modeling Biogenic
Emissions of Isoprene: Exploration of Model Drivers, Climate Control Algorithms, and Use of
Global Satellite Observations.” Atmospheric Environment 35 (35): 6151–6165. doi:10.1016/
S1352-2310(01)00390-9.
Rosenstiel, T. N., M. J. Potosnak, K. L. Griffin, R. Fall, and R. K. Monson. 2003. “Increased CO2
Uncouples Growth from Isoprene Emission in an Agriforest Ecosystem.” Nature 421 (6920):
256–259.
Rozanov, A., V. Rozanov, M. Buchwitz, A. Kokhanovsky, and J. P. Burrows. 2005. “SCIATRAN
2.0–A New Radiative Transfer Model for Geophysical Applications in the 175–2400 nm
Spectral Region.” Advances in Space Research 36 (5): 1015–1019. doi:10.1016/j.
asr.2005.03.012.
Saunders, S. M., M. E. Jenkin, R. G. Derwent, and M. J. Pilling. 2003. “Protocol for the
Development of the Master Chemical Mechanism, MCM V3 (Part A): Tropospheric
Degradation of Nonaromatic Volatile Organic Compounds.” Atmospheric Chemistry and
Physics 3 (1): 161–180. doi:10.5194/acp-3-161-2003.
Seco, R., J. Peñuelas, and I. Filella. 2007. “Short-Chain Oxygenated Vocs: Emission and Uptake by
Plants and Atmospheric Sources, Sinks, and Concentrations.” Atmospheric Environment 41
(12): 2477–2499. doi:10.1016/j.atmosenv.2006.11.029.
Sharkey, T. D., and E. L. Singsaas. 1995. “Why Plants Emit Isoprene.” Nature 374: 769.
Sharkey, T. D., E. L. Singsaas, P. J. Vanderveer, and C. Geron. 1996. “Field Measurements of
Isoprene Emission from Trees in Response to Temperature and Light.” Tree Physiology 16 (7):
649–654. doi:10.1093/treephys/16.7.649.
Shim, C., Y. Wang, Y. Choi, P. I. Palmer, D. S. Abbot, and K. Chance. 2005. “Constraining Global
Isoprene Emissions with Global Ozone Monitoring Experiment (GOME) Formaldehyde
Column Measurements.” Journal of Geophysical Research: Atmospheres (1984–2012) 110
(D24). doi:10.1029/2004JD005629.
Singsaas, E. L. 2000. “Terpenes and the Thermotolerance of Photosynthesis.” New Phytologist 146
(1): 1–2. doi:10.1046/j.1469-8137.2000.00626.x.
Spurr, R. 2008. “LIDORT and VLIDORT: Linearized Pseudo-Spherical Scalar and Vector Discrete
Ordinate Radiative Transfer Models for Use in Remote Sensing Retrieval Problems.” In Light
Scattering Reviews 3, edited by A. Kokhanovsky, 229–275. Chichester: Springer-Praxis.
Spurr, R. J. D. 2004. “LIDORT V2PLUS: A Comprehensive Radiative Transfer Package for UV/
VIS/NIR Nadir Remote Sensing.” Paper presented at International Society for Optics and
Photonics Remote Sensing, Amsterdam, September 22–25.
Spurr, R. J. D., T. P. Kurosu, and K. V. Chance. 2001. “A Linearized Discrete Ordinate Radiative
Transfer Model for Atmospheric Remote-Sensing Retrieval.” Journal of Quantitative
Spectroscopy and Radiative Transfer 68 (6): 689–735. doi:10.1016/S0022-4073(00)00055-8.
Staffelbach, T., A. Neftel, B. Stauffer, and D. Jacob. 1991. “A Record of the Atmospheric Methane
Sink from Formaldehyde in Polar Ice Cores.” Nature 349 (6310): 603–605. doi:10.1038/
349603a0.
Stavrakou, T., J.-F. Müller, I. De Smedt, M. Van Roozendael, G. R. Van Der Werf, L. Giglio, and A.
Guenther. 2009a. “Evaluating the Performance of Pyrogenic and Biogenic Emission Inventories
against One Decade of Space-Based Formaldehyde Columns.” Atmospheric Chemistry and
Physics 9: 1037–1060. doi:10.5194/acp-9-1037-2009.
Stavrakou, T., J.-F. Müller, I. De Smedt, M. Van Roozendael, G. R. Van Der Werf, L. Giglio, and A.
Guenther. 2009b. “Global Emissions of Non-Methane Hydrocarbons Deduced from
Downloaded by [85.58.31.116] at 11:20 05 November 2014
7542
S.C. Kefauver et al.
SCIAMACHY Formaldehyde Columns through 2003–2006.” Atmospheric Chemistry and
Physics 9 (11): 3663–3679. doi:10.5194/acp-9-3663-2009.
Theys, N., M. Van Roozendael, F. Hendrick, X. Yang, I. De Smedt, A. Richter, M. Begoin, Q.
Errera, P. V. Johnston, K. Kreher, and M. De Mazière. 2011. “Global Observations of
Tropospheric Bro Columns Using GOME-2 Satellite Data.” Atmospheric Chemistry and
Physics 11 (4): 1791–1811. doi:10.5194/acp-11-1791-2011.
Thomas, W., E. Hegels, S. Slijkhuis, R. Spurr, and K. Chance. 1998. “Detection of Biomass Burning
Combustion Products in Southeast Asia from Backscatter Data Taken by the GOME
Spectrometer.” Geophysical Research Letters 25 (9): 1317–1320. doi:10.1029/98GL01087.
Tingey, D. T., D. P. Turner, and J. A. Weber. 1991. “Factors Controlling the Emissions of
Monoterpenes and Other Volatile Organic Compounds.” Trace Gas Emissions by Plants 65.
doi:10.1016/B978-0-12-639010-0.50009-1.
Turner, A. J., D. K. Henze, R. V. Martin, and A. Hakami. 2012. “The Spatial Extent of Source
Influences on Modeled Column Concentrations of Short‐Lived Species.” Geophysical Research
Letters 39 (12). doi:10.1029/2012GL051832.
Van Roozendael, M., C. Fayt, J.-C. Lambert, I. Pundt, T. Wagner, A. Richter, and K. Chance. 1999.
“Development of a Bromine Oxide Product from GOME.” Paper presented at the proceedings
ESAMS, Noordwijk, January 18–22.
Veefkind, J. P., K. F. Boersma, J. Wang, T. P. Kurosu, N. Krotkov, K. Chance, and P. F. Levelt. 2011.
“Global Satellite Analysis of the Relation between Aerosols and Short-Lived Trace Gases.”
Atmospheric Chemistry and Physics 11 (3): 1255–1267. doi:10.5194/acp-11-1255-2011.
Vigouroux, C., T. Stavrakou, C. Whaley, B. Dils, V. Duflot, C. Hermans, N. Kumps, J.-M. Metzger,
F. Scolas, G. Vanhaelewyn, J.-F. Müller, D. B. A. Jones, Q. Li, and M. De Mazière. 2012.
“FTIR Time-Series of Biomass Burning Products (HCN, C 2 H 6, C 2 H 2, CH 3 OH, and
HCOOH) at Reunion Island (21° S, 55° E) and Comparisons with Model Data.” Atmospheric
Chemistry and Physics 12 (21): 10367–10385. doi:10.5194/acp-12-10367-2012.
Volkamer, R., L. T. Molina, M. J. Molina, T. Shirley, and W. H. Brune. 2005. “DOAS Measurement
of Glyoxal as an Indicator for Fast VOC Chemistry in Urban Air.” Geophysical Research
Letters 32 (8). doi:10.1029/2005GL022616.
Vountas, M., V. V. Rozanov, and J. P. Burrows. 1998. “Ring Effect: Impact of Rotational Raman
Scattering on Radiative Transfer in Earth’s Atmosphere.” Journal of Quantitative Spectroscopy
and Radiative Transfer 60 (6): 943–961. doi:10.1016/S0022-4073(97)00186-6.
Vrekoussis, M., F. Wittrock, A. Richter, and J. P. Burrows. 2010. “GOME-2 Observations of
Oxygenated Vocs: What Can We Learn from the Ratio Glyoxal to Formaldehyde on a Global
Scale?” Atmospheric Chemistry and Physics 10 (21): 10145–10160. doi:10.5194/acp-10-101452010.
Wittrock, F., A. Richter, H. Hilke Oetjen, J. P. Burrows, M. Kanakidou, S. Myriokefalitakis, R.
Volkamer, S. Beirle, U. Platt, and T. Wagner. 2006. “Simultaneous Global Observations of
Glyoxal and Formaldehyde from Space.” Geophysical Research Letters 33 (16). doi:10.1029/
2006gl026310.
Wittrock, F., A. Richter, A. Ladstätter-Weißenmayer, and J. P. Burrows. 2000. “Global Observations
of Formaldehyde.” Paper presented at the proceedings ERS-ENVISAT Symposium, October
16–20. ESA-Publ. SP-461.
Young, P. J., A. Arneth, G. Schurgers, G. Zeng, and J. A. Pyle. 2009. “The CO 2 Inhibition of
Terrestrial Isoprene Emission Significantly Affects Future Ozone Projections.” Atmospheric
Chemistry and Physics 9 (8): 2793–2803. doi:10.5194/acp-9-2793-2009.