AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-4383-2015Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methaneThompsonD. R.david.r.thompson@jpl.nasa.govhttps://orcid.org/0000-0003-1100-7550LeiferI.https://orcid.org/0000-0002-4674-5775BovensmannH.https://orcid.org/0000-0001-8882-4108EastwoodM.FladelandM.FrankenbergC.https://orcid.org/0000-0002-0546-5857GerilowskiK.GreenR. O.KratwurstS.KringsT.LunaB.ThorpeA. K.https://orcid.org/0000-0001-7968-5433Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USABubbleology Research International, Solvang, CA,
USAUniversity of Bremen, Institute of Environmental Physics,
P.O. Box 330440, 28334 Bremen, Germany.NASA Ames Research Center,
Moffett Field, CA, USAD. R. Thompson (david.r.thompson@jpl.nasa.gov)19October20158104383439715March201522June201510September201510September2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/8/4383/2015/amt-8-4383-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/4383/2015/amt-8-4383-2015.pdf
Localized anthropogenic sources of atmospheric CH4 are highly
uncertain and temporally variable. Airborne remote measurement is an
effective method to detect and quantify these emissions. In a campaign
context, the science yield can be dramatically increased by real-time
retrievals that allow operators to coordinate multiple measurements of the
most active areas. This can improve science outcomes for both single- and
multiple-platform missions. We describe a case study of the NASA/ESA
CO2 and MEthane eXperiment (COMEX) campaign in California during June
and August/September 2014. COMEX was a multi-platform campaign to measure
CH4 plumes released from anthropogenic sources including oil and gas
infrastructure. We discuss principles for real-time spectral signature
detection and measurement, and report performance on the NASA Next Generation
Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully
detected CH4 plumes in real-time at Gb s-1 data rates,
characterizing fugitive releases in concert with other in situ and remote
instruments. The teams used these real-time CH4 detections to
coordinate measurements across multiple platforms, including airborne
in situ, airborne non-imaging remote sensing, and ground-based in situ
instruments. To our knowledge this is the first reported use of real-time
trace-gas signature detection in an airborne science campaign, and presages
many future applications. Post-analysis demonstrates matched filter methods
providing noise-equivalent (1σ) detection sensitivity for 1.0 %
CH4 column enhancements equal to 141 ppm m.
Introduction
Airborne imaging spectrometers have been deployed for a wide range of
scientific, regulatory, and disaster response objectives. Traditionally these
campaigns wait for favorable environmental conditions and then fly
pre-arranged survey patterns (typically “mowing the lawn”), recording data
for post-flight radiometric calibration and geolocation. Significant time can
pass before data are analyzed fully, and results often arrive too late for
mid-course corrections during the campaign. However, improvements in
computing power, communication, and telemetry are changing this situation.
Tactical remote measurement generates in-flight calibrated data
products to inform a real-time adaptive survey strategy. This can be
coordinated to direct other platforms in multi-platform campaigns. We use the
term “tactical” to emphasize environmental awareness and real-time
decision making, with no military connotation. Its applications include the
following.
Detection of transient or rare targets – Many airborne missions hunt isolated or nonstationary phenomena. Examples
include trace-gas emissions , algal blooms
, invasive species , isolated
microhabitats , and hurricane intensity
. Aircraft use radar to hunt extreme weather, and lidar to
find cirrus, thunderstorms or biomass burning . In each
case, tactical remote measurement can identify desired features (and equally
importantly, their absence) during flight, permitting flight plan adjustments
to improve coverage . This reveals features' temporal
evolution and improves measurement confidence. During multi-platform
campaigns, real-time environmental awareness can guide teams acquiring
complementary in situ measurements.
Disaster response – Remote measurements play a critical role
in disaster response to oil spills ,
search and rescue , fires
, and earthquakes
. In any disaster, information arrives at the incident
command center from a range of sources of differing reliability. Remote
measurements can contribute repeatable and objective analysis, allowing more
efficient, confident allocation of ground and airborne assets while keeping
responders safe. The immediate risks to human life demand short response
times, for which tactical measurement can provide situational awareness.
Data quality assurance – Tactical remote measurement adds
flexibility and confidence to flight management decisions. Currently,
mid-campaign flight planning often occurs without knowing the quality of data
already collected. This risks wasting resources if, for example, the mission
continues under marginal environmental conditions. On the other hand,
conservative planning can miss opportunities. Tactical science products can
inform flight plans and mid-day scrub decisions to avoid spending flight
hours on low-value or redundant data. For example, it may reveal interference
such as cirrus clouds , sun glint , and
unacceptable aerosol scattering . This also allows
instrument subsystem failures to be recognized and addressed immediately.
Robotic exploration – Real-time analysis can improve autonomous
operations when communication opportunities are rare and bandwidth is
limited, such as in space exploration. Remote spacecraft that are out of
touch with ground control can autonomously detect high-value spectral
signatures that guide prioritized downlink or trigger additional measurements
. Operators can generate compact map products onboard
the spacecraft and downlink them to supplement raw spectra, expanding spatial
coverage at a low bandwidth cost. Onboard cloud screening is one example of
data volume reduction; it can improve yields by a factor of 2 or more for
Earth orbiting instruments .
Hypothesis formation and testing – Real-time data analysis
and visualization in a mapping environment, like Google Earth
, is common in surface and airborne in situ applications.
Many systems allow the scientist to visualize spatial relationships between
measured parameters, forming hypotheses on the fly for immediate testing.
Adaptive surveying can address new hypotheses during the campaign, while
instruments are deployed and environmental conditions are favorable.
Telemetering live data allows remote investigators to observe and participate
in operational decisions .
These techniques require high-performance data telemetry and communication.
As the technologies proliferate, unanticipated applications are likely to
appear – just as instant results from the digital CCD transformed chemical
photography in dramatic and unforeseen ways.
This study demonstrates tactical remote measurement with imaging spectroscopy
during a multi-aircraft, multi-platform campaign, CO2 and MEthane
eXperiment (COMEX). The COMEX campaign was funded by NASA and ESA to explore
synergies between NASA's proposed HyspIRI (Hyperspectral Infrared Imager) mission and ESA's CarbonSat Earth
Explorer 8 candidate mission. Greenhouse gas emissions were measured from a
range of important anthropogenic sources. Investigators surveyed landfills,
husbandry, and fossil-fuel production sites in southern California during
summer and fall, 2014. A multi-scale experimental design combined airborne
and surface measurements to characterize CH4 sources on scales of
meters to tens of kilometers. Ground-validated airborne imaging spectroscopy
identified sources and their heterogeneity. This was followed by downwind
surface surveys together with airborne sounding and in situ observations
transecting plumes at different upwind and downwind distances. Surface mobile
survey teams carried sensors to specific locations of interest. Finally,
repeated surface in situ surveys studied longer term temporal variability and
larger spatial context.
COMEX exploited tactical remote measurements from multiple platforms. We
focus on one participating instrument, the Airborne Visible
Infrared Spectrometer - Next Generation (AVIRIS-NG) , which mapped
CH4 enhancements in real time. A simple detection method based on a
band ratio (BR) was sufficient to detect several sources and enhance the COMEX
campaign. These initial results motivated the development of a more
sophisticated matched filter detection approach, described in this paper,
which was developed after COMEX and has been adopted by subsequent
CH4 monitoring campaigns. Although prior studies quantified
CH4 anomalies using Visible Shortwave Infrared (VSWIR) imaging
spectrometers , we believe the COMEX campaign
to be the first real-time tactical deployment for remote trace-gas imaging.
Section describes the real-time algorithms, system
architecture, and implementation decisions. Section reviews
the campaign results including an AVIRIS-NG sensitivity analysis and
discussion of lessons learned. We conclude with a discussion of future
directions for tactical remote sensing.
Tactical imaging spectroscopy
The visible/shortwave infrared (VSWIR) imaging spectrometers serve diverse
applications including mineralogical mapping ,
characterization of coastal and terrestrial ecosystems , and
atmospheric studies . Imaging spectrometers are valuable for
tactical operations because they can map and localize targets over wide
areas, providing reconnaissance for other instruments along with spatial and
spectral context. Real-time airborne imaging spectroscopy has been deployed
in a few previous instances. For example, demonstrated
real-time cloud screening for future space missions.
calculated reflectance products using the model-based ATREM atmospheric
correction. demonstrated the ARCHER system which provided
real-time processing for search and rescue applications. They performed
matched filter spectral signature detection and change detection using the
chronochrome method . They also demonstrated spectral
anomaly detection using the Reed–Xiaoli (RX) detector , an
anomaly score based on the Mahalanobis distance . These
methods detected artificial objects in wilderness scenes, such as parts of
aircrafts and vehicles near crash sites. Another real-time airborne
investigation used non-imaging spectroscopy for detecting dangerous volcanic
plumes . On the ground, real-time analysis has imaged these
plumes' SO2 and SiF4 absorption ,
enabling subsequent analysis to infer emission rates .
This section describes the real-time system used by AVIRIS-NG during the
COMEX campaign. AVIRIS-NG measures reflected sunlight in the
0.38–2.5 µm range with 0.005 µm spectral resolution.
Its 1 mrad instantaneous field of view (iFOV) provides sub-meter
ground sampling distance (GSD). The real-time system characterizes
CH4 plumes by analyzing absorption features from
2.1–2.4 µm. Its design must balance the
competing needs of speed and algorithm sensitivity, and several guiding
requirements drive our decisions.
First, the system must provide a sensitivity floor sufficient to detect the
phenomena of interest reliably. In other words, it must have a signal to
noise ratio (SNR) sufficient to find sources under relevant wind,
illumination, and substrate conditions. Only then can planners safely act on
a null detection result. For similar reasons, it must minimize false
positives. Prior studies of CH4 with the “Classic” AVIRIS
instrument by detected local enhancements of 1 ppm
within a kilometer-thick atmospheric model layer. Later studies by
using AVIRIS-NG found similar enhancements in the distal
regions of plumes associated with CH4 fluxes of
14.2 m3h-1 (500 standard cubic feet per hour, scfh) under
moderate (5 ms-1) winds. Resolving plumes of this magnitude
under similar conditions should be possible with a sensitivity of
1000 ppm m. Better performance would further reduce ambiguity and improve
the detail of diffuse plumes.
A second requirement is high spatial resolution. The phenomena should subtend
multiple pixels with sufficient resolution for the operator to identify
typical morphologies. The diagnostic shape of atmospheric plumes can be
corroborated with ancillary wind information , while in
the case of oil slicks thickness asymmetry and shape are useful cues
. For plumes, resolution can enhance detection sensitivity
due to non-uniformity: many plumes are initially buoyant, rising abruptly in
a column for tens of meters before dispersing downwind. High spatial
resolution avoids diluting this feature, which may be only a few meters in
diameter. Fine spatial resolution also helps exclude false positives caused
by artificial features with obvious geometric shapes. For these reasons, we
desired that the system would process AVIRIS-NG at native resolution without
subsampling.
A third requirement is speed. Speed follows a “threshold” utility function:
the system must operate at the instrument data rate, but additional
performance provides no extra benefit. Real-time operation avoids a confusing
temporal association puzzle where a detection appears at a location passed
seconds or minutes ago. In addition, keeping pace with data collection
simplifies operations by permitting the system to operate whenever the
instrument collects data. We find it possible to implement many of the most
common detection methods from literature within this requirement, though
speed considerations determine the physical quantities that we retrieve.
Specifically, we focus on measuring plume absorption along the optical path.
This is sufficient to indicate the relative strengths of different sources.
We do not estimate the vertical structure or flux; such products generally
require complex iterative retrievals involving ancillary data such as wind
speed, and are less critical for real-time decisions.
The real-time system first executes the standard AVIRIS-NG ground data
pipeline to create calibrated radiance measurements. It then matches these
spectra to the known gas absorption signature of CH4. The procedure
successfully operates at instrument data rates of approximately
500 Mbs-1 and allows operators to detect CH4 emissions
in real time. The following sections detail specific design choices for the
software architecture and detection algorithms.
Instrument and software architecture
The AVIRIS-NG instrument acquires 598 cross-track spectra at 100 Hz.
Frames are captured with a custom field programmable gate array (FPGA) frame
grabber over a dedicated Camera Link interface at 500 Mbs-1 data
rate. Data are synchronized with an onboard inertial measurement unit
(IMU)/GPS system , and finally stored in a solid-state
RAID array. The AVIRIS-NG console records un-orthorectified raw data and
displays it for the operator. A backup computer records a second copy in
parallel, and the detection system runs on this machine. Real-time analysis
requires that detection algorithms keep pace with the data recording rate,
while leaving enough CPU cycles for the backup data recorder.
Our solution exploits parallelism with multi-core CPUs
(Fig. ). A watchdog process waits for a new image to
appear on disk. As the instrument writes to this file, an executive real-time
process begins reading from the other end and buffering 1000 lines at a time.
The real-time analysis applies radiometric calibrations to each 1000-line
block and partitions the resulting data into spatially independent regions
for multi-thread detection. When all threads have finished, the results are
reassembled and recorded to storage, where they are immediately available on the operator display. The system processes 10 s intervals of data in well
under 10 s, achieving the real-time speed requirement.
The computing architecture for real-time spectral analysis leverages
multi-core parallelism.
Onboard radiance processing
The detection pipeline first transforms the frame (a single cross-track slice
of data) to a calibrated radiance product . We
calculate radiance in Wcm-2nm-1sr-1 at each cross-track
spatial location c and wavelength λ using:
Lm(c,λ)=(R(c,λ)-Rd(c,λ)-Rp(c))r(λ)f(c,λ),
where R(c,λ) are the raw digital numbers from the instrument.
Rd(c,λ) is the electronic dark current estimated from a
closed-shutter segment at the beginning of each flight line. Rp(c)
are electronic “pedestal shift” effects, in which a spatially compact
signal depresses the signal at other spatial locations. The onboard system
estimates the pedestal shift of each spectrum based on the residual dark
current in non-illuminated edges of the detector. r(λ) and
f(c,λ) are the radiometric calibration coefficients and flat field
corrections, respectively. Both are calculated from laboratory calibration
sequences using a known spatially uniform illuminant under fixed imaging
geometry. Appendix discusses wavelength calibration.
Onboard signature detection
A comparison of spectral shapes between the CH4 transmission
spectrum, resampled to AVIRIS-NG wavelengths, and the target signature t
used for detection. The vertical axis plots two different quantities as noted
in the legend. Both signatures were calculated from a 20-layer atmosphere
based on HITRAN 2012 absorption cross sections .
Figure shows a typical CH4 transmission
signature, calculated using a model atmosphere with absorption coefficients
of . The detection algorithm calculates a scalar score to
estimate any local enhancement of this background. We evaluated several
detection algorithms based on their sensitivity and speed. At one extreme, an
iterative nonlinear or “optimal estimation” solution such as iterative
maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS)
is more quantitative, but somewhat slow
for real-time operation. At the other extreme, an absorption band depth score
uses simple arithmetic, but its low SNR can detect only the strongest
signatures. This paper focuses on a third approach: a novel matched filter
variant with a good balance of sensitivity, stability, and speed, and which
also permits a quantitative interpretation.
Our first algorithm uses a continuum interpolated band ratio (CIBR), defined
as the depth of an absorption feature relative to a local linear continuum
. It is written as follows:
CIBR=Lm(c,λcenter)wleftLm(c,λleft)+wrightLm(c,λright),
where λcenter, λleft, and
λright are wavelengths in the middle and either side of the
absorption feature. The weighting coefficients w sum to unity, and make the
denominator the linearly interpolated continuum at the location of the
absorption center λcenter. We find the 2.37 µm
feature provides the best overall contrast. The CIBR method is simple to
implement and fast to execute. For the sources studied during COMEX, its
sensitivity is sufficient to detect strong local CH4 enhancements.
The second detection strategy is a classical matched filter
, a variant of which was used previously for CH4
detection by . The matched filter tests each spectrum
against a target signature t while accounting for the background
covariance. Here t is a vector with one element per wavelength. If
the background spectra are distributed as a multivariate Gaussian
N with mean vector μ and covariance matrix
Σ, the matched filter is equivalent to a hypothesis test
between the case H0 where the target is absent and H1 where it is
present.
H0:Lm∼N(μ,Σ)H1:Lm∼N(μ+tα,Σ)
Here, t is the target signature. The matched filter estimates the
scalar value α, the fraction of the target (potentially larger than
unity) which perturbs the background. Larger values of α signify a
stronger match. The matched filter is written.
α(x)=(t-μ^)TΣ^-1(x-μ^)(t-μ^)TΣ^-1(t-μ^)
The hat symbols indicate that the background mean vector and covariance
matrix are estimated using samples from the scene. One typically draws
samples from a rectangular region near the target. However, most push-broom
sensors have a slightly nonuniform behavior at different cross-track
positions, which violates the Gaussian background assumption. The cross-track
push-broom elements are separate detectors, so it often is better to model
their noise distributions independently. Thus, we apply an independent
matched filter to each column of the (non-orthorectified) image, calculating
a new μ and Σ for each cross-track element.
This columnwise matched filter dramatically reduces the number of samples
available for estimating each Σ. We compensate by
estimating a stable, low-rank approximation of the inverse sample covariance
as in . The covariance matrix Σ
decomposes as product of p column eigenvectors q and p
eigenvalues ϕ:
Σ=∑i=1pϕiqiqiT.
The top d eigenvalues approximate the inverse. With the identity matrix
I and trace operator tr, we have
Σ^-1=1αI-∑i=1dϕi-βϕiqiqiT,β=1p-dtrΣ-∑i=1dϕi.
We typically estimate 30 eigenvalues for vertical blocks consisting of
1000–2000 samples per column.
Target signatures
The signature t should match the spectrum of the target feature. A
reasonable approach is to use the transmission shape itself (the red curve in
Fig. ). However, this is inaccurate when absorption
is strong; further attenuation becomes nonlinear as absorption lines
saturate. The matched filter assumes a linear perturbation, so the Jacobian
of the radiance spectrum is an appropriate signature. We calculate it by
modeling local CH4 enhancement as a uniform cell. The airborne
instrument measures absorption along a path transecting the CH4
cloud. For thin, uniform plumes, the unknown quantities of absorption length
and concentration are interchangeable, so we consider the combined quantity,
the mixing ratio length expressed in ppm m . Our
derivation is similar to that of . Following
Eq. () the matched filter estimates α, which is a
multiplicative scaling of the target signal that perturbs the mean background
radiance μ. This background includes absorption by ambient
CH4. Under hypothesis H1, a local enhancement acts as a
concentration-dependent absorption coefficient κ(λ) and
absorption path length ℓ. For clarity, we write this relation with a
functional form μ(λ) to represent a single wavelength of the mean
vector μ.
H1:Lm(λ)=μ(λ)e-κ(λ)ℓ
For x near zero, the first-order Taylor expansion exp(x)≈1+x
permits
H1:Lm(λ)≈μ(λ)-κ(λ)ℓμ(λ).
Combining all wavelengths using vector notation, and folding unknowns into
α, yields
H1:Lm≈μ+t1α.
This is the form of the matched filter model. The target signature
t1 is the vector of negative absorption coefficients for a
near-surface plume of unit concentration and unit length, multiplied by the
background mean radiance. The resulting matched filter estimates α,
the scaling of the unit concentration path length. The target signature
represents the perturbation, in radiance units, of the background radiance by
an additional unit mixing ratio length of CH4 absorption, which acts
as a thin Beer-Lambert attenuation of the (already attenuated) background
μ. Evaluating the partial derivative of Eq. ()
at ℓ=0 gives
∂Lm(λ)∂l=-μ(λ)e-κ(λ)ℓκ(λ)=-μ(λ)κ(λ).
We can estimate the enhancement of CH4 using the linear scaling of a
target signature that perturbs the mean radiance; that signature is defined
as the negative absorption coefficient scaled by the (wavelength-dependent)
radiance. Figure compares the shape of the Jacobian
target signature to the typical transmission signature of ambient
CH4.
The linearization works for thin plumes even when the background is
saturated, because deviations are small and can be modeled linearly to permit
a fast yet accurate quantitative retrieval . It ignores
scattering effects, which is a reasonable compromise at low flight altitudes;
spectral features caused by actual CH4 enhancements by far exceed
typical retrieval biases that could be induced by atmospheric scattering
. In addition, Rayleigh and aerosol scattering is much
lower in the 2.3 µm region than in the UV and visible spectral
range. The linearized approach is complementary to other more complete
retrieval algorithms, such as the IMAP-DOAS approach
.
Operator display
After the detection stage maps plume intensities, an interface displays this
information to the operator in a more tactically useful format. Specifically,
the display overlays detected plumes on RGB images for visual interpretation
and localization (Fig. ). It supports variable detection
thresholds so the operators can set the cutoff concentration according to
their tolerance for false positives. This is important because source
strength varies and the detection sensitivity changes with solar zenith
angle. Also, it is important that the system preserve the overlay images in
memory until explicitly reset, so that the operator has time to consider
ambiguous detections. Figure was produced by playing back a
June flight line using a recent iteration of the detection software. A simple,
intuitive interface minimizes unnecessary controls. A vertical slider scrolls
the flight line to review previously collected data. During recording, the
system appends data to the end of this image. A horizontal slider adjusts the
detection threshold, allowing the operator to change the overlay sensitivity
based on their tolerance for error. Bright red pixels signify a strong signal
well above the threshold, while dark red pixels signify an ambiguous signal.
Screen shot of the graphical user interface, with an example of
flight data from 13 June (ang20140613t184239). The red plume is displayed
overprinted on RGB wavelengths. Real-time localization was implemented for
use after the COMEX campaign, and we have redacted the precise coordinates in
this image.
Results from the COMEX campaign
COMEX field data collection included the Kern River, Kern Front, and Poso
Creek Oil Fields, located to the north and northwest of Bakersfield, CA in
the San Joaquin Valley (Fig. ). Along with AVIRIS-NG, the
COMEX campaign deployed a second aircraft: the CIRPAS Twin Otter, which
carried the Methane Airborne MAPper (MAMAP)
, a non-imaging spectrometer, and an in situ
Picarro CH4 sensor sponsored by NASA, Ames. The campaign also
deployed the AutoMObile greenhouse Gas Surveyor (AMOG) car-mounted system for in situ CH4 and wind
measurement . These platforms used several real-time
displays and communications links. AMOG used a map overlay
displaying CH4 measurements along with the wind direction. MAMAP was
modified for COMEX to deliver real-time retrieved CH4 information
using a WFM-DOAS algorithm described in . These
data were displayed on the MAMAP instrument scientist aboard the aircraft, and
overlaid on a map for tactical decision making. MAMAP also
transmitted its real-time CH4 measurements together with telemetry
and data from other CIRPAS sensors to the CIRPAS data acquisition and
assimilation system, where they were downlinked by satellite to the command
center. All aircraft were tracked during the mission using the Airborne
Science Mission Tools Suite , the ground segment to the NASA
Airborne Science Sensor Network . This system provided a
web-based service for real-time communications between aircraft operators and
the science team (Fig. ). It also integrated real-time
aircraft position and state information through a common map display.
Kern Oil Fields, Bakersfield, CA . Oil field
locations are from . White circles indicate the locations of
Figs. –.
The three platforms in the COMEX campaign coordinated their
activities through the central command center. The two aircraft also
communicated directly. CIRPAS image from www.cirpas.org. AMOG image by
I. Leifer.
We focus here on 3 days when all sensors and platforms were active in the
field: 13 June, 2 September, and 4 September 2014. During the June
investigation only MAMAP was analyzed in real time; AVIRIS-NG CH4
detection took place offline. We later installed the band ratio algorithm and
used it onboard AVIRIS-NG in September, where it operated successfully.
Finally, the full columnwise matched filter for CH4 was developed and
installed after the campaign, and used in post-analysis.
Operational implementation of tactical remote measurement
AVIRIS-NG flew along six neighboring flight lines. Tactical remote measurement
was implemented during the 2 September data acquisition, and detected many
“hot spots” of high CH4 concentrations. Operators noted strong
plumes on flight lines 2 and 3, on the west side of the study area, and
relatively weak activity on the east side. AVIRIS-NG operators transmitted
plume coordinates to the ground team by text message via the command center.
The MAMAP and AVIRIS-NG aircraft also shared their observations using direct
radio communications. Flight line 2, recorded in flight as having the largest
number of hits, was revealed by post-analysis to contain patches of high
activity (Fig. ). The final data acquisitions focused
on this flight line, which comprised three of the final nine images. Focusing
on the west side significantly increased the total plumes imaged for the day,
and also provided improved data on temporal variability.
Region of high activity in flight line 2 of the 2 September flight
lines. All values in ppm m.
Left: subframe of ang20140904t205356. The insert
is a high-resolution visible image that reveals the source to be a pump jack.
The proceeding panels, from left to right, show repeat overflights at 20:23,
20:45, and 20:53 UTC. Values show local CH4 enhancement in
ppm m.
Operations on 4 September made further use of real-time AVIRIS-NG and MAMAP
data. As before, the flight lines contained many active plumes. However, the
ground team's initial data collection at the Kern River oil field did not
show significant CH4. The ground team coordinator suggested shifting
data collection west to the Kern Front Oil Field. AVIRIS-NG confirmed the
presence of CH4 plumes in this area, and the experiment coordinator
rerouted the surface teams. The surface teams relocated and identified an
exceptionally strong CH4 plume coming from the direction of a
drilling rig, approximately 1 km from the road. Subsequent analysis of
AVIRIS-NG data indicated a plume in this area that originated at a small
structure about 100 m from the road. The real-time MAMAP data also observed
a large scale plume originating from that area .
Thanks to the tactical remote sensing, AVIRIS-NG continued covering this area
with repeat overflights at 10–20 min intervals that revealed the temporal
evolution of many plume features. Figure shows a sequence
of revisits to one of the stronger targets. Commercial satellite imagery from
Google Earth reveals the source, a pump jack . The
AVIRIS-NG data shows strong CH4 absorption near the source, which
would be expected for a vertical rise by a buoyant column. Turbulence causes
the plume structure to become discontinuous as it disperses downwind. The
matched filter resolves the plume at concentrations as low as 500 ppm m,
approximately 3 standard deviations above the background noise.
Example of MAMAP soundings overlaid on an AVIRIS-NG detection
result. Colored pixels indicate CH4 mixing ratio lengths in ppm m
from AVIRIS-NG. The monochrome dots show MAMAP soundings: black signifies
<100 ppm m, grey 100–200 ppm m, and white >200 ppm m. Overlay
courtesy .
Selected flight days in the COMEX campaign. Often AVIRIS-NG overflew
the same plume multiple times. Here the “Plumes” column records the total
number of instances that a plume appears in the data, rather than the number
of physical plumes.
DateReal-time analysisFlight linesPlumes13 Jun 2014No26292 Sep 2014Yes17684 Sep 2014Yes2557
Left: RGB wavelengths of a June 13 AVIRIS-NG overflight
(ang20140613t184239). The insert shows a high-resolution visible image of the
pump jack . Right: retrieved CH4 enhancement in
ppm m, using the Jacobian signature.
Left: band ratio method applied to flight line ang20140613t184239.
Center: classical matched filter with transmission signature. Right:
columnwise matched filter with transmission signature. Values show local
CH4 enhancement in ppm m.
Figure shows the agreement between the different instruments
on 4 September. Colored pixels indicate CH4 mixing ratio lengths from
AVIRIS-NG. The monochrome dots show MAMAP retrievals: black signifies the
background up to <100 ppm m. A grey dot shows CH4 of
100–200 ppm m near the plume, and a single white dot shows CH4
exceeding 200 ppm m within the plume. There are several reasons why
retrievals might differ. First, the two instruments have disparate
spatiotemporal coverage; on 4 September the MAMAP cross track instantaneous
field of view is 86,m (2.9∘), and the down track instantaneous
field of view is 76 m (2.64∘), with additional down track
averaging of 48 m during the integration time. Moreover, the
acquisition time difference of several minutes is significantly longer than
variability in wind speed and gusts due to local atmospheric turbulence at
relevant spatial scales of <500m. Emissions have been documented
to vary on similar timescales. Finally, the optical paths differ so
concentration measurements are not perfectly comparable.
With these caveats in mind, we directly compared the two measurements. We
calculated a spatially weighted average of the AVIRIS-NG estimated
enhancement above background, matching it to the MAMAP response while
simultaneously searching over the sounding's position uncertainty radius of
50 m. The resulting estimates were as high as 103.5 ppm m (white
dot) and 101.3 ppm m (grey dot), of similar magnitude to the MAMAP
retrievals. Despite differences in observing conditions, both data sets
evidence similar-scale enhancements at this site. In other cases, small
plumes visible in AVIRIS-NG were sometimes invisible in MAMAP data.
Table summarizes the total number of plume instances
appearing in each day's data, as revealed in post-analysis of AVIRIS-NG data.
We record only the unambiguous detections with a long axis greater than five
pixels, and exclude compact detections from becalmed image segments – their
bright, concentrated plumes show a strong signal but lack the morphological
cues needed for unambiguous attribution.
CH4 detection sensitivity
We use the 13 June flight lines as a control case to evaluate sensitivity,
because the tactical remote sensing system was not operational; i.e., data
collection was “blind.” Figure shows a typical map of
plume thicknesses in ppm m. Retrieved values approach 2500 ppm m.
A defined plume is evident, along with turbulent structures that disperse
100–200 m downwind of the source. The insert shows a high-resolution
visible image, which reveals the source to be a pump jack.
Figure shows the same scene analyzed with alternative
algorithms: band ratio, matched filter, and columnwise matched filter
detection strategies respectively, with intensities scaled to the maximum
on-plume pixel. The band ratio barely reveals the largest plumes with many
contiguous “hot” pixels. A classical matched filter improves performance
and the columnwise version is cleaner still. One gleans a final SNR benefit
using the Jacobian rather than the transmission as a target signature.
We evaluated detection sensitivity by exhaustively labeling all CH4
plumes in the 13 June flight lines by manual inspection. There were 29
obvious plumes, some of which were repeat overflights of the same physical
location. For each plume, we identified 3–10 on-plume pixels having the
highest estimated concentration. We then calculated SNR using a large
rectangular region of pixels 100 m upwind as the background.
Figure shows the relationship between plume strength
(in units of ppm m) and SNR. The dark lines of best fit are constrained to
intersect the origin. Table reports these slopes α and
the reciprocal, the noise equivalent mixing ratio length (NEMRL), defined
here as 1 standard deviation above the background. The first three rows show
a CIBR method, a classical matched filter based on the transmission spectrum,
but applied columnwise, and a more traditional matched filter with
rectangular support but using the Jacobian spectrum. The fourth row shows the
columnwise Jacobian matched filter. This combination achieves a NEMRL of
140 ppm m the best overall performance of any algorithm.
We have defined sensitivities in terms of ppm m at standard temperature
and pressure (STP), which is a reasonable approximation for the Kern fields.
However, one could also express sensitivity as a fractional enhancement of
the entire atmospheric column. To calculate this value, we average the
seasonal and geographic CH4 profiles of , which
incorporate tropospheric models from and
. The resulting column has an STP-equivalent mixing ratio
length of 14 600 ppm m for which the noise-equivalent enhancement is
1 %. A larger total-column enhancement of 3.4 % would be equivalent
to 500 ppm m, producing a strong >3σ detection.
Plume strength vs. detection SNR for four different methods, applied
to the 29 plumes observed in 13 June flight lines.
The SNRs reported here understate the effective system sensitivity when
paired with a trained operator. SNR estimates assume pixel independence, but
in practice plumes show multiple contiguous pixels, none of which need to be
above the standard 3–5σ detection limit to distinguish the structure
as a whole. Consequently plumes with strengths near the noise floor were
visually apparent. The SNR disregards contaminating clutter, such as painted
structures with similar absorption features, which could cause localized
false positives. However, such features were rare; they occurred at most a
few times per flight line. Moreover, they were generally easy to ignore by eye
because their sizes and morphologies were very distinct from plumes.
Consequently false positives were not a significant problem during actual
use.
Detection sensitivity for band ratio and matched filter (MF)
methods. Columns show the noise-equivalent mixing ratio lengths expressed as
ppm m and as CH4 column enhancements.
Typical ratio of radiances in the plume and out of plume. The ratio
is continuum-removed, with an offset and vertical scaling to highlight the
similar shapes.
We used several methods to verify that detections were actually caused by
CH4, and not false positives due to interfering surface features or
gases. First, a visual assessment verified that the source location of each
plume lay near artificial structures, as expected, and that its main
structure was uncorrelated with visible surface features or changes in
albedo. In other words, the plumes were contiguous phenomena that crossed
rather than followed the boundaries of surface features. Second, for a subset
of plumes, we verified that the ratio of in-plume and background radiances
showed a clear CH4 signature. Figure illustrates
this process for the plume in Fig. . The figure shows the
ratio of the average radiance spectra, further divided by the linear
continuum stretching across the two endpoints of the spectral interval. The
plot compares this ratio to the modeled transmittance of CH4. The
model fits three parameters consisting of a linear continuum and the
absorption given by the mixing ratio length. The retrieved path length of
2438 ppm m is a close match to the linear solution from the matched filter,
and the resulting spectrum is a good fit to the empirical ratio. This
provides additional confidence that the detection was due to the actual
presence of CH4 and not (for example) a false positive artifact.
Motivated by the use of steam injection for enhanced oil recovery at
Bakersfield, we checked the ratios for H2O vapor absorption and
failed to find any excess concentration of this potential interferant.
Finally, corroborative measurements by other COMEX in situ and remote sensing
instruments confirmed the presence of CH4, as illustrated in
Fig. .
Discussion
These experiments underscore the value of merging diverse instruments with
partial overlap in measurement capabilities. This overlap permits the
instruments to cross-check each other, and allows fast mapping platforms to
provide reconnaissance to in situ teams. For example, COMEX used a multi-tier
strategy of nested measurement scales. At the top level, MAMAP provided high
accuracy retrievals over very wide areas, and resolved CH4 plumes on
the scale of hundreds of meters. Airborne in situ measurements provided
validation of remotely observed large scale plumes. AVIRIS-NG provided
unambiguous images of specific plume sources, locating them to within a few
meters. The ground team augmented this remote view with in situ point
samples. The arrangement proved effective, but relied on a relatively
slow-moving ground team to close the loop with unambiguous in situ
measurements. Tactical remote measurement with efficient airborne
reconnaissance makes full use of these in situ instruments.
There are several logical next steps for both hardware and software
development. For hardware, a future instrument designed specifically for the
purposes of CH4 would offer far better detection performance. For
example, an imaging spectrometer that focused just on the
2.1–2.4 µm range would permit much finer spectral resolution for
highly sensitive and accurate retrievals. A slower, low-flying platform would
provide higher spatial resolution and improved SNR. On software, COMEX
revealed a need for a real-time geolocalization capability to display
accurate plume locations. This capability has since been implemented using a
geometric camera model, geometric ray-tracing, and an onboard digital
elevation model (DEM). One could further mitigate clutter-related noise with
more sophisticated background modeling or
explicit outlier rejection . Finally, the
system could be made more general by pairing it with the real-time
reflectance processing to recognize materials at the surface
in addition to gaseous absorbers. This would expand missions to applications
including search and rescue, tracking of spatially variable phytoplankton or
algal blooms for water color studies, fire response, and oil spill response.
During COMEX, communicating tactical science data across platforms improved
science outcomes and produced a robust data set that could be used to validate
interpretations. Advanced communications infrastructure can further
streamline data transfer between ground and flight. The NASA Airborne Science
Data and Telemetry system functions as a flight data
recorder, onboard payload network server, and low bandwidth telemetry system.
A Payload Telemetry Link Module augments this system with higher level data
products and high bandwidth satellite telemetry. Similar systems can be used
to more seamlessly share data across participants. As the number of
real-time data products increases, it becomes important to avoid information
overload. Displaying data as optional map layers provides user control,
while chat capabilities allow different team members to flag important
observations.
Conclusions
We have demonstrated tactical signature detection onboard AVIRIS-NG in
service of a coordinated science campaign, COMEX. Informing the operators
when CH4 plumes were detected improved the science yield of this
investigation, both by confirming data quality in real time and by enabling
more flexible asset deployment. Post-analysis demonstrated that the strongest
detected signatures were related to real sources. The latest iteration of the
system provides 3σ detection sensitivity of 500 ppm m or weaker.
Wavelength calibration
Wavelength calibration: empirical fits to the 0.76 µm
oxygen band and 0.82 µm water vapor band.
Accurate wavelength calibration is critical for detecting narrow spectral
absorption features. Our wavelength calibration uses laser sources to
characterize the center wavelengths and and full width half maximum (FWHM) of
the detector array. This initial wavelength calibration is derived from six
sources (at 0.4067, 0.532, 0.632, 1.064, 1.551, and 2.064 µm). We
refine this result with flight data, optimizing a single uniform shift to
match atmospheric absorption features in the top of atmosphere (TOA)
reflectance spectrum ρ(c,λ). Following , the TOA
reflectance represents the radiance measurements at wavelengths λ,
after normalizing for extra-terrestrial solar irradiance F and solar zenith
θ:
ρ(c,λ)=πLm(c,λ)F(λ)cos(θ).
We model this spectrum as a locally linear continuum attenuated by
gaseous absorption of the 0.74 µm oxygen band and the 0.96
and 1.14 µm water vapor bands. The attenuation is governed by a
Beer-Lambert relation based on the gas absorption coefficient
δ(λ) obtained from a 20 layer model atmosphere:
ρ^(λ)=h(τ1)τ2e-τ3δ(τ4+λ)+τ5[τ4+λ],
where τ represents free parameters we optimize during the fit.
Specifically, h(τ1) is convolution with a Gaussian spectral response
function with a FWHM given by τ1. τ2 is the continuum level at
100 %, τ3 represents the absorption path length, τ4 the
wavelength shift and τ5 a linear slope. We fit these free parameters
using a Nelder–Mead simplex algorithm. Figure (Right) shows
an example from a bright, spectrally smooth playa. Here the model matches the
measured spectrum with residual error under 1 % – within the limits of
the spectrometer's radiometric accuracy. The empirical calibration procedure
is an independent check of cross-track spectral uniformity. Figure
shows the wavelength calibration shift for
different cross-track elements, after averaging 500 downtrack samples in each
column. The average shift is less than 0.1 nm, or 2 % of the full
width at half maximum.
Wavelength calibration: cross-track divergence characterized by the
position of the oxygen A band over Ivanpah Playa,
NV.
Acknowledgements
We acknowledge and thank the AVIRIS-NG team, as well as scientists at JPL and
elsewhere whose counsel was invaluable throughout the system design process:
Andrew Aubrey, Lance Christensen, Dar A. Roberts. Brian Bue (JPL) is the
original author of the real-time infrastructure that formed the foundation of
the new AVIRIS-NG system. A portion of the research described in this paper
was performed by the Jet Propulsion Laboratory, California Institute of
Technology, under a contract with the National Aeronautics and Space
Administration. The author's copyright for this publication has been transferred
to the California Institute of Technology. The
Government sponsorship is acknowledged.
Edited by: U. Platt
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