METHANE PLUME DETECTION USING PASSIVE HYPER-SPECTRAL REMOTE SENSING

Willard D. Barnhouse Jr.

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

December 2005

Committee:

Robert K. Vincent, Advisor

Sheila Roberts

Enrique GomezdelCampo ii

ABSTRACT

Robert K. Vincent, Advisor

The ability to detect and measure atmospheric methane gas plumes is important for a number of reasons. Methane is a significant greenhouse gas, plume detection could help methane source/sink studies. Detecting gas plumes due to leakage is also important for energy resource production and transportation facilities. There have been various techniques developed

to accomplish this task. The work in this thesis used passive hyperspectral remote sensing

analysis with data collected form the high altitude MODIS Airborne Simulator (MAS). The

MAS platform is the only publicly known sensor to operate at an altitude close to orbital

parameters and which is equipped with high spectral resolution bands in the same wavelength

region (3.314µm) as the fundamental C-H spectral absorption feature.

This study examined multiple remote sensing band ratios designed to capitalize on the

3.314µm absorption feature. Other ratios were also developed to detect atmospheric gas changes

associated with possible methane plumes. Much of the analysis utilized datasets covering two

California regions known to contain active oil/gas seeps and production. One study area covered an off-shore environment, while the other area was over land. It was determined that no single

MAS ratio algorithm could be used to confidently detect a methane gas plume. The expected or possible presence of other atmospheric gases has the potential to affect the algorithms and produce complications for interpretation.

The relative uniform spectral and thermal properties of ocean waters provide a good background for passive remote sensing atmospheric studies. However, methane plume detection iii

over marine enviornments is problematic due to the production of water vapor from methane

atmospheric chemical reactions. By using a concurrence of ratio algorithm results, one suspected plume was thought to be detected in one of the off-shore datasets. The analysis for the land datasets found the high degree of surface material and temperature variations dramatically interfered with the ability to interpret the algorithm results with any significant confidence. The results produced many features of which it was not possible to distinguish potential positive results from the false positives. No methane plumes were identified in any of the land datasets, even though oil/gas facilities were targeted. Additional work with multi-temporal datasets could provide a means to address these issues.

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ACKNOWLEDGMENTS

I would like to thank my advisor, Dr Robert Vincent, for all of his support and assistance in conducting this research. His enthusiasm for applying remote sensing techniques to pertinent and difficult studies is contagious. Without his extensive knowledge and willingness to share that knowledge, this work would not have been feasible. Many thanks to Bill Butcher, whose computer expertise and late nights insured I could keep working. I would also like to thank my committee members, Dr Sheila Roberts and Dr Enrique Gomezdelcampo, for their manuscript reviews and suggestions.

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TABLE OF CONTENTS

Page

INTRODUCTION ...... 1

METHANE BACKGROUND...... 2

Methane Sources...... 2

Methane in the Atmosphere ...... 4

Spectral Properties of Methane ...... 6

Methane Detection and Previous Studies ...... 8

THESIS OVERVIEW ...... 11

Using RS data Ratios ...... 11

Viewing Conditions and Geometries...... 14

Selecting Areas of Interest...... 18

Spatial Correlation...... 19

Use of HITRAN, MAS, and Analysis Software...... 20

METHODOLOGY: PRE-ANALYTICAL...... 21

Details of HITRAN...... 21

Details of MAS ...... 22

Combining HITRAN Data and MAS Bands ...... 24

RS Band Ratio Generation and Evaluation...... 26

Expected Sensor Signal Strengths ...... 30

METHODOLOGY: DATA ANALYSIS ...... 34

ERMapper …...... 34

ArcMap ……...... 34 vi

RESULTS ……...... 38

MAS Band Ratios...... 38

RS Ratio Analysis and Images...... 39

GIS Spatial Correlation...... 43

DISCUSSION ……...... 46

RS Ratio Algorithms...... 46

California Off-shore MAS 98031 ...... 48

California Inland MAS 97127 ...... 55

Toledo-Oregon, Ohio MAS 96144 ...... 60

GIS Analysis ...... 62

Quantitative Estimation ...... 65

CONCLUSIONS …...... 67

REFERENCES ...... 70

APPENDIX A. ……...... 74

APPENDIX B. ……...... 92

APPENDIX C. ……...... 98 vii

LIST OF FIGURES

Figure Page

1 Atmospheric gas concentrations for CH4, CO, and OH ...... 6

2 Plot of infrared line list data for various hydrocarbons ...... 7

3 Reflectance and emittance curves of the Earth’s surface

based upon Planck’s blackbody formula ...... 12

4 Illustration for Linear Approximation Concept and Continuum Method...... 12

5 Illustration of band selection for spectral analysis ratio algorithm...... 13

6a Standard viewing conditions and geometries for land...... 15

6b Standard viewing conditions and geometries over water ...... 16

6c Daytime viewing conditions and geometries over land

and water with atmospheric gas...... 17

7 Plot of HITRAN spectral lines for CH4 gas...... 22

8 California mission locations...... 24

9 CH4 spectra and MAS 98-031 Bands...... 25

10 BB Curve plots for solar reflected and 300K surface emission...... 30

11 Ideal spectral profiles...... 31

12 Ideal vs Actual sensor response ...... 32

13 General location of remote sensing images in figures 14 and 15 ...... 40

14 True color and select b/w RS ratio images ...... 41

15 True color and select color scale RS ratio images ...... 42

16 General location of remote sensing image in figure 17 (Toledo-Oregon, Ohio)...... 43

17 True color image of subset region in MAS 96144_13...... 44 viii

18 Image of cloud within MAS 98031_02 ...... 45

19 Subset of cloud GIS data...... 45

20 True color image of MAS 98031_02...... 49

21 Color images of RS ratio results ...... 53

22 Color images of more RS ratio results...... 54

23 Subset of MAS 97127_02...... 56

24 Band 27 RS ratio results for select refineries...... 57

25 Close-up view of fields in MAS 97127_02 ...... 59

26 True color and RS images of Toledo area ...... 61

27 True color image of MAS 97127_02 dataset with yellow GIS highs...... 63

28 Close-up MAS 97127_02 dataset with yellow GIS highs and red agreements ...... 64

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LIST OF TABLES

Table Page

1 Percentages of major gases...... 4

2 Absorption Band Strengths (ABS Values) ...... 26

3 ABS ratio values ...... 28

4 Table for Evaluating RS band ratio based on ABS ratios and Gas concentrations ... 29

5 RS ratios and values using ideal band spectral signal strengths ...... 33

6 GIS Classifications...... 35

7 Summary of GIS Raster Calculator Results...... 37

8 Analysis ABS ratio values ...... 38

1

INTRODUCTION

Atmospheric methane detection is important for a number of reasons. “Methane is the predominant hydrocarbon in Earth’s atmosphere” (Fawcett et al., 2002). It is also a primary greenhouse gas. The abundance of methane gas in the atmosphere has dramatically increased during recent history (Fawcett et al., 2002; Haus et al., 1998; Kleine et al., 2000; Vincent, 1997).

The ability to detect and monitor methane gas concentrations has become an important issue for global climate studies. Detection is also important for reasons other than environmental concerns. The energy industry could use remote sensing to detect pipeline leaks, find methane clathrates, or find active gas seeps due to subsurface oil or coal deposits.

The purpose of this study is to investigate the feasibility of using passive hyper-spectral data to detect atmospheric methane plumes on Earth. Other remote sensing (RS) techniques and studies have already demonstrated some success in detecting methane gas. Methane is a transparent gas in the visible part of the light spectrum. However, the spectral signature of methane does contain a number of spectral features at longer wavelengths. Not all of these features are within an atmospheric window. This study primarily utilizes the methane

absorption/emission feature at 3.314µm. RS spectral datasets collected from MODIS Airborne

Simulator (MAS) flights are used in the analysis. MAS has multiple narrow bands covering the spectrum at and near 3.3 µm (King et al., 1996). Three different ratios of MAS Band data are evaluated for their ability to detect methane plumes. Methane plumes are not isolated in the atmosphere. A number of chemical reactions and gases can be associated with the methane.

Some of these gases are incorporated into the evaluations.

2

METHANE BACKGROUND

Methane sources

There are many sources of methane release to the atmosphere. Some are natural and others have anthropogenic origins. Methane gas can be produced by biogenic organic decay and thermogenic petroleum processes (Fountain and Jacobi, 2000).

Natural sources are of particular interest to geologists. Anaerobic bacterial decomposition, particularly in wetland environments, generates a significant portion of methane released into the atmosphere (Takeuchi et al., 2003). Methane is intimately connected to petroleum and other hydrocarbon resources. Escaping methane may accompany active oil seeps. Or the methane may reach the atmosphere while the liquid hydrocarbons fail to reach the surface. Methane and other light hydrocarbons are commonly found in near surface soils above gas and fluid hydrocarbon reservoirs (Duscherer, 1986; Hughes et al., 1986; McCoy et al., 2001;

Rice, 1986). Studies have found near surface hydrocarbon gases useful for detecting buried faults (Fountain and Jacobi, 2000). Methane emanating from fractures and joints could also indicate out-gassing from a sub-surface coal seam (Rinkenberger et al., 1997).

The recent rise in atmospheric methane content has been attributed primarily to anthropogenic sources (Haus et al., 1998; Vincent, 1997). These include: gas and oil exploration/processing/transportation, industrial emission, other fossil fuel uses, and agricultural

production. Hydrocarbon gases can be released due to oil/gas production (Haus et al., 1998).

These gases may be vented at oil/gas drilling fields and refining plants. Natural gas pipeline

leaks are another source of atmosphere release (Karapuzicov et al., 2000; Ziring et al., 2002).

Methane is the primary component of natural gas (Vincent, 1999). Leaks from natural gas

processing, transmission, and distribution infrastructures are the primary source of fossil-fuel 3

related emissions. The loss has been estimated at 1-2% for developed countries and may be as high as 15% of production in undeveloped countries or countries with lax control mechanisms

(Wuebble and Hayhoe, 2001). In addition to petroleum related emissions, human activities such as agricultural practices and waste management also produce methane. Significant amounts of

methane are produced from cattle herds and from rice paddies. The decay of organic materials

concentrated in landfills can generate significant local methane gas concentrations (Carman,

1996).

Oceanic methane release and production were once thought to be insignificant. It has

recently been recognized that marine environments are a greater source of atmospheric methane

than was originally believed (Judd et al., 1997). Some workers estimate the oceans account for

6% of the natural methane sources (Wuebble and Hayhoe, 2001). One study has suggested that

natural seepages from the seabed around England may account for up to 40% of the methane

release in the United Kingdom (Judd et al., 1997). Coupling the UK findings with knowledge of

other marine environments and oceanic methane studies indicates the oceanic flux rate is much greater than previous estimates (Judd, 2003).

The two primary sources of seabed methane are: 1) microbial activity and associated organic decay in the sediments and 2) deeper thermogenic production with upward migration.

The upper part of the water column is typically an oxidizing environment and the gas is water soluble. Thus, as methane bubbles rise from the seabed, the gas can react with the water and/or dissociate. If the flux rate is low, the methane gas can become completely lost before it reaches the ocean-atmosphere interface (Judd et al., 1997). Seabed release to the atmosphere can occur where natural seeps are concentrated, such as fault zones; or, where the water is shallow enough that the bubbles can quickly reach the sea surface. Coastal marine waters can have significant 4

methane release due to the abundance or organic rich sediments and the shallow water depths

(Judd, 2003)

Sudden eruptions or release of sediment or deeper methane gases have long been known to occur periodically. Some of the methane eruptions have actually been observed. Witnesses report seeing methane bubbles at the sea surface in the Santa Barbara Channel, one of the sites selected for this work (Judd, 2003). Another type of sudden release can occur when solid gas hydrates or methane clathrates become unstable or break up. In high-pressure, low-temperature conditions, the gas can be incorporated into these “ices”. Changes in temperature or pressure can cause the solid ice structures to change phase, abruptly releasing the stored gases. Large pieces may break off and rise toward the surface, releasing the gases at shallower depths or even at the ocean surface (Brewer, 2002; Kvenvolden, 1998)

Oceanic methane production/release is not limited to the depths below the oxidizing waters. Marine surface waters are supersaturated with regards to dissolved methane (Warneck,

2000). This results in a low flux background methane release to the atmosphere.

Methane in the Atmosphere

Table 1 summarizes the major constituents of Earth’s atmosphere (Warneck, 2000).

Dry Air Bulk Composition

Constituent %

N2 78.094

O2 20.936 Ar 0.934

CO2 0.036

N2, O2, and Ar are "permanent gases" with little variability

CO2 may be variable Table1. Percentages of major gases, data summarized from Warneck (2000) 5

Average methane concentration is low enough to be considered a trace gas. It is “the

most abundant organic trace gas in the atmosphere”, and has dramatically increased since pre-

industrial times (Wuebble and Hayhoe, 2001). Recent average atmospheric methane concentrations within the atmosphere have been estimated between 1.6 parts per million by

volume (ppmv) and 1.75 ppmv (Des Marais et al., 2002; Fawcett et al., 2002; Kleine et al., 2000;

Vincent 1997). It is a recognized Greenhouse gas that has the potential to contribute to global

warming (Fawcett et al., 2002; Vincent, 1997). Methane is the most important hydrocarbon gas

contributing to the greenhouse effect (Kleine et al., 2000). However, it is only the third most

abundant greenhouse gas (Wuebble and Hayhoe, 2001). Its importance is not derived from its

abundance or the gas’ ability to absorb solar energy. Rather, the impact of methane is that it

leads to the production of other major greenhouse gasses.

Atmospheric methane (CH4) has an average life of approximately 9-10 years (Warneck,

2000; Wuebble and Hayhoe, 2001). Because of the long atmosphere life, the gas is generally

well mixed in the troposphere. The major sinks are: oxidation with the OH radical, dry soil

oxidation, and transport to the stratosphere. The initial OH oxidation leads to a series of

reactions. The production of CO is prominent. This in turn can lead to the production of CO2.

Other important products include: H2O, temporary production of formaldehyde HCHNO, O3, and

possibly OH (Jacob, 1999; Warneck, 2000; Wuebble and Hayhoe, 2001).

The primary CH4 oxidation reaction is CH4 + OH CH3 + H2O (Jacob, 1999).

Depending upon local atmosphere composition, the net reaction series can be expressed as:

CH4 + 10O2 CO2 + H2O + 5O3 + 2OH

or

CH4 + 3OH +2O2 CO2 + 3 H2O + HO2 6

It should be noted the first reactions consume OH, but later reactions produce OH if other gasses and/or pollutants are present. Another important consideration is that there are many intermediate reactions and products that may persist for an extended period of time. While CO is not a net result product (due to the eventual oxidation to CO2) it is an intermediate product with a two month atmospheric lifespan and is commonly associated with methane gas. Early atmospheric methane reactions produce CO by the following two reactions:

CH4 CH2O then CH2O + O2 CO + HO2 and CH2O + hv CO + H2

The CO gas also accompanies the natural release of methane due to the oxidation that occurs as the methane rises through soil and sediment. Warneck compiled data from three different studies to illustrate the CH4 – CO – OH relationship in Figure 1 (Warneck, 2000).

Figure 1. Atmospheric gas concentrations for CH4, CO, and OH. Modified from Warneck, 2000.

Spectral Properties of Methane

“Spectroscopic knowledge of methane spectrum is required for numerous remote sensing applications” (Brown et al., 2003). Methane gas has many spectral features. The C-H fundamental vibrational transition is contained within the spectral region near 3.3 µm. There are three significant absorption branches in this region: P branch at 3.31 µm, Q branch at 3.28 µm, and R branch at 3.21 µm (Krier and Sherstnev, 2000). Methane spectral features include 7 overtones and combination at shorter wavelengths. At longer wavelengths, methane also has absorption features in the IR spectrum. The C-H fundamental feature is much stronger than the shorter wavelength features (Fawcett et al., 2002). In addition to being relatively strong, the

3.3µm feature is just within an Earth atmospheric window (Vincent, 1997; Zirnig et al., 2002).

Not all of the other spectral features are within an atmospheric window. Many of the features are obscured by water vapor. However, at high methane concentrations, some of the obscured spectral features may be detectable (Des Marais et al., 2002).

The C-H fundamental feature is not unique to methane; it is shared by many of the hydrocarbons. Figure 2 depicts a spectral line graphs for methane (CH4) and a few other hydrocarbons (http://vpl.ipac.caltech.edu/spectra/hydrocarbons/htm). The C-H fundamental vibrational transition is not the only spectral characteristic common to many hydrocarbons.

McCoy et al. (2001) note that hydrocarbons share similar spectral properties in the 0.4-2.5 µm spectrum. The strength of the spectral features is proportional to gaseous abundance and pressure in the atmospheric column (Des Marais et al., 2002).

Figure 2. Plot of infrared line list data for various hydrocarbons. The X-axis for plots are in cm-1. Spectral absorption features at or near 3000 cm-1 or 3.3 µm are shared by many hydrocarbons, (Modified from JPL/VPL spectral database at url;http://vpl.ipac.caltech.edu/spectra/hydrocarbons.htm) 8

Although absorption features are commonly discussed in much of the literature reviewed

for this proposal, it is important to remember gases may both absorb and emit electromagnetic

energy at these spectral lines and features. The impact of shared spectral features, gas

concentration, and viewing conditions will be considered further in the Overview and

Methodology sections.

Methane Detection and Previous Studies

Methane detection is important for geological, economical, and environmental reasons.

Light hydrocarbons anomalies in near surface atmospheres may provide a means to infer

subsurface geologic features. At specific concentrations, methane and other natural gas leaks

can be a major safety issue. Even low concentrations may produce adverse local environmental

effects. Leaks at the well site, processing /storage facility, and transportation pipeline are

financial losses for the company. Monitoring pipeline integrity with current methods can be a

significant operational cost. Using satellite RS has the potential to maximize the use of other

monitoring methods and reduce operational costs while increasing safety.

Direct air sampling and gas chromatograph analysis are the conventional means to

measure atmospheric methane (Wei, 2000). Numerous studies have also addressed the issue of

applying RS techniques to detect methane gases. Workers have tried or proposed using spectral

features in nearly all parts of the spectrum, from ultraviolet to radar. Some of the work in the

VIS/NIR and IR spectrum is briefly discussed in this section. Methane detection on other

terrestrial planets has generated a lot of interest. Much of this interest is due to the fact that

methane is considered to be one of the markers for the current or recent presence of life (Des

Marais et al., 2002; Benilan et al., 2001; Beichman et al., 1999). Many of the stronger VIS/NIR 9 and IR features should work well in this part of the spectrum if the planet has little water vapor present. For atmospheres with significant water vapor content, the methane features at 0.88 µm and 7.7 µm have been proposed (Des Marais et al., 2002).

The difference between active and passive RS methods is that active systems provide an electromagnetic illumination source while passive systems do not generate their own source signals. This study utilizes the data from a passive RS sensor. Active RS methods have been developed and applied to methane detection on Earth. Cavity spectroscopic methods have been successful in laboratory settings with very short optical paths. A study by McCoy et al. (2001) was able to detect and measure hydrocarbon soil gases that had adsorbed to soil/clay particles using four spectral features in the 1-2.5 µm spectrum. Other studies were successful at 1.66 µm and the 3.39 µm, but the cavity techniques may not be robust enough for use outside of the laboratory environment (Fawcett et al., 2002). Technologies currently exist that utilize the 3.3

µm feature for short range RS. A number of companies have developed sensors using LED illumination sources within the 2-5 µm to detect methane (Krier and Sherstnev, 2000; Silveria et al., 1998). These LED techniques have been commercially developed and employed for ranges up to 50 m. Recent studies suggest the range may be extended up to 200 m (Kopica et al., date unknown). Other active sensors currently being studied include the use of lasers for much longer detection paths. Haus et al. (1998) employed a mobile lab and interferometry sensor to detect methane gas concentrations in and near natural gas flares using the 2917.6 cm-1 (3.427µm) spectral line. Another study was able to detect CH4 using two spectral bands in the 3-4 µm wavelength region (Karapuzikov et al., 2000).

Other studies have also used passive hyper-spectral RS methods. Wei (2000) noted how difficult it is to predict the variation in methane density because the variations in methane 10

sources and sinks are not completely understood. In order to add to more knowledge to the

subject, Wei and his group determined to measure the temporal variation of methane at a fixed

location in China. The study used a ground based IR solar spectrometer to measure and analyze

the spectra between 3.425 µm and 3.436 µm with very fine spectral resolution. This part of the

spectral range contains both CH4 and H2O vapor absorption features. The spectrometer allowed

the team to calculate the atmosphere vertical column methane abundance seasonal variation

(Wei, 2000).

Earth is not the only planet upon which passive hyperspectal techniques have been used

to detect and measure methane. The Planetary Fourier Spectrometer (PFS) orbits Mars,

detecting the solar light reflected and emissions from the planet’s surface. With a spectral

resolution of 0.0014 µm, the sensor is able to measure discrete methane absorption lines. The

study determined the 3.3135 µm line would provide the best results for analysis. Formisano and

the other workers were able to combine Mars’ atmospheric models, solar spectra information,

and the PFS spectral line intensities to quantify the amount and distribution of atmospheric

methane on the planet (Formisano et al., 2004).

Both of the last two studies have some similarities to the work in this thesis. All three

seek to detect the fundamental C-H absorption feature. The Mars platform is an orbiting satellite

with the sensor focused on the surface below, as does the airborne sensor used in this study. The

China sensor is on the surface, capturing light that passes down through the Earth’s atmosphere.

The major difference between this work and the other two studies is the spectral resolution of the sensors. The fine spectral resolutions of the other two sensors provide the ability to easily

differentiate between the individual methane spectral lines and other lines, such as water vapor.

MAS bands are much coarser and may encompass many spectral lines of multiple gases. 11

THESIS OVERVIEW

As stated in the introduction, the purpose of this project is to determine if Earth

atmospheric methane plumes can be detected using passive multi-spectral RS techniques. The

following sections review some of the basic concepts that are fundamental to this work. The

overview sections also explore some issues that needed to be considered before the details of the project could be addressed.

Using RS data ratios

Passive RS methods generally measure the electromagnetic radiation or luminescence that reaches the sensor. The signal strength is dependent on a number of variables such as; illumination source, atmospheric transmittance/scatter, background slope, background reflectance/absorption/emittance, shadow, and atmospheric gas absorption/emittance. Planck’s formula can be used to calculate an ideal Blackbody (BB) curve for the illumination source and the background surface. Absent any absorption features in the narrow spectrum of interest, the surface reflectance curve will be proportional to the original illumination source.

Figure 3 shows the surface solar reflection and self-emission curves of the Earth’s surface. The calculus concept of linear approximation can be used in the narrowly defined spectrum near the peak absorption. In this spectral region, the ideal BB emission and reflectance curves do not exhibit a high degree of curvature.

12

Figure 3. Reflectance and emittance curves of the Earth’s surface based upon Planck’s blackbody formula. Note these curves are log plots; the non-log plots for the blackbody curves would also look linear and be much flatter in the 3.3µm range. (Modified from Vincent, 1997)

Linear approximation states that at short enough intervals, any curve can be closely

approximated with a straight line. Thus, the spectral curves can be simplified into straight lines with constant slope (see figure 4). Taking the mid-point and endpoints, the line (curve) can be

analyzed with the formula:

A + C 2 * B The product of this ratio would always be equal to 1 for a true straight line. An ideal BB curve is

not a straight line, but over a very short segment of the curve, the quotient should be close to 1.

This approach is referred to as the continuum method.

Figure 4. Illustration for Linear Approximation Concept and Continuum Method. 13

For selecting a ratio to detect methane and other gases, this study used the gas absorption feature in the denominator. Bands on both sides (Flanks) of the spectral feature (Peak) were ratioed with the spectral feature band (see Figure 5). All three of the PQR branches should be within the Peak. The primary algorithm used for this study is:

(Flank1) + (Flank2) (2 * (Peak))

When the gas is present but much cooler than the Earth’s surface (the background), the

absorption peak will reduce the signal strength at the sensor, while the flank signal strengths

remain relatively unchanged. Hence, the resultant ratio value will be much greater where the gas

is at higher concentrations than where there is a low concentration of gas present.

Figure 5. Illustration of band selection for spectral analysis ratio algorithm. The Peak would be the band with greatest overall absorption potential while the Flanks would be bands with little or no absorption potential. (Modified from Vincent, 1997)

Due to the longer wavelengths used for this study, atmospheric scatter is not a major

concern. Any potential slope and shadow effects are eliminated by the use of ratios. Please note

that the above discussion is a simplified model of an idealized situation. The complexities

associated with applying these techniques are described with more detail in later parts of this

thesis. 14

Viewing Conditions and Geometries

Vincent (1997) examined the spectral absorption/emission characteristics of a gaseous

plume with various viewing parameters of illumination, background temperature, and gas

temperature. Solar illumination and body temperatures are important for passive RS in the

visible (VIS), near infrared (NIR), and infrared (IR) parts of the spectrum. The diagrams in

Figure 6 on the following pages illustrate some of the more common situations for airborne RS

missions. MAS missions are flown during the daylight hours in late morning to late afternoon,

so the night time conditions do not need to be considered.

Much of this project focused on methane detection in offshore environments. Solar

illumination is present on the ocean surface. However, water surfaces are primarily specular

reflection surfaces. This means almost all of the light is reflected in a specific direction dependent upon the incident angle. RS missions typically try to control the viewing geometries so that the sensor does not receive the specular reflection. As seen in Figures 6b and 6c, solar reflection does not reach the sensor. Instead, the illumination source is the emittance of the background, in this case the electromagnetic radiation due to the temperature of the sea waters.

This produces images with generally low signal strengths in the VIS spectrum and higher signal strengths in the IR spectrum. If the atmospheric gases between the ocean surface and the sensor are lower temperatures than the ocean, the gases will absorb some of the NIR and IR radiance and the sensor signal will be lower. If the gases are at higher temperatures, then the emittance from the gas will add to the signal strength. The third condition could be equal temperatures, in which the emittance from the gas is the same as that from the ocean and the gases are effectively invisible for RS purposes. 15

Figure 6a. Standard viewing conditions and geometries for land. 16

Figure 6b. Standard viewing conditions and geometries over water. 17

Figure 6c. Daytime viewing conditions and geometries over land and water with atmospheric gas. 18

Solar illumination dominates scenes over land in the VIS-NIR spectrum. Land surface temperatures have a greater effect in the NIR-IR spectrum. Land surfaces are considered diffuse reflectors and are sometimes generalized as having 20% solar reflection. Because land surface materials and topography are much more diverse than ocean water is, the true reflection is often highly variable even within the same scene. The material diversity also affects the background temperature and the NIR-IR signal strength.

The illumination/background/gas temperature relationships also apply to land scenes. In the VIS spectrum, the solar temperature will always be higher than the gas temperatures, so the gases will always absorb more solar energy than the gas emits. This greatly simplifies RS signal interpretation, but can produce a complication when trying to geo-reference the gas plume.

Figure 6c illustrates that the gas can intercept the radiance between the illumination source and the background reflector, or between the reflection surface and the sensor. At longer wavelengths, the background and gas temperatures can play a greater role in determining the sensor signal strength. The illumination from the sunlight is less than the IR emittance from the warm land surface. This improves the ability to geo-reference a gas plume, but requires more knowledge about the land and air temperature differences. In the NIR spectrum, the solar illumination and background emittance may have similar strengths. This part of the spectrum is referred to as the cross-over point, where the two black body curves intersect. Analyzing RS data in this region of the spectrum has both of the above complications.

Selecting Areas of Interest

Methane is ubiquitous in the lower atmosphere, although at low concentrations. As mentioned above, there are a multitude of atmospheric release sources. Many of these are 19

diffuse sources and/or have low flux rates that are below atmospheric dispersal conditions. The

ideal site would have defined release points/areas with continuous flux rates capable of

producing sustained methane plumes. Short of generating a controlled methane release and

contracting a MAS over flight, it is unlikely such a source will be located for study. The matter

of site selection is further constrained by the limited availability of MAS flight paths and datasets. Therefore, site selection was determined by looking at flight paths that coincide with areas associated with a higher probability of localized methane release. As mentioned in the above sections, these sites include oil/gas production and landfills.

Three primary locations meet the selection criteria. The first is oil/gas offshore production fields located in the Gulf of Mexico. Other oil/gas production sites include onshore

and offshore drilling fields in the Santa Barbara-Longbeach area of California. The third area

includes two landfills in Wood County, Ohio. The landfills were selected because previous

studies by other workers detected measurable amounts of atmospheric methane (Carman, 1996).

The details of specific mission selection are discussed in the Methodology: Pre-Analytical

section.

Spatial Correlation

Because the MAS datasets are not current, the anomalies detected in the data may not be

verifiable by subsequent field measurements. This necessitates the development of other supporting evidence. Topographic and surface information were incorporated into the study.

This may include topographic maps, aerial photography, orthophoto-quads, oil/gas drill site and pipeline information. The spectral anomaly data were correlated to surface features to test

methane plume interpretation. This entails the use of GIS technology. 20

Another means of testing the interpretation is to look for products of methane chemical reactions or other compounds that may accompany methane release. Many other hydrocarbons accompany the release of methane. The light hydrocarbons such as ethane, propane, and butane have low boiling points and are common to natural gas (Krauskopf and Bird, 1995; Olah and

Molnar, 2003). Carbon dioxide (CO2) is typically generated with methane and is also a product of methane reactions (Duscherer, 1986; Hughes et al., 1986; Olah and Molnar, 2003). Another release may be hydrogen sulfide (Hughes et al., 1986). Photochemical reactions with UV light will cause dissociation of atmospheric methane (Benilan et al., 2001). The methane spectral analysis can be spatially correlated with the other compound analysis.

Use of HITRAN, MAS, and Analysis software

Spectral properties of methane and other gases were derived from HITRAN (High

Resolution Transmission), an atmospheric gas spectral database. The RS data used for this study was collected as part of the MODIS Airborne Simulator project. MAS is a 50 band, multi- spectral airborne scanning spectrometer. The bulk of the RS data analysis was performed with

ERMapper software. The ERMapper ratio analysis images were imported into ArcMap for geospatial analysis. In addition to the ERMapper work, initial MAS data processing and later geo-referencing utilized ENVI software.

21

METHODOLOGY: PRE-ANALYTICAL

The methods portion of this report is broken into two separate chapters. This first methodology chapter focuses on the work that had to be accomplished before the RS data analysis and geospatial analysis could begin. It includes; incorporating the individual gas spectral data with the MAS Band configuration, determining the appropriate analysis ratios, and examining the relationship between MAS bands and the blackbody curves.

Details of HITRAN

HITRAN is a database of atmospheric gas spectra. The HITRAN database provides one of the best spectral line lists for low temperature methane (Nasser and Bernath, 2003). It began with work by the United States Airforce and has since been maintained and updated by Harvard

University and the Smithsonian Institute. The database is provided free of charge to registered researchers. It can be accessed directly online or the entire database can be downloaded with its own user interface software. The user software, called JavaHAWKS, allows the user to view plots of the spectral signature for each gas. Figure 7 is a plot of the HITRAN spectral lines for methane (CH4) gas. The spectral lines of six other gases were also used in this study. The spectral line plots for each gas are included in Appendix A. JavaHAWKS also allows the user to export individual data files into the Excel file format for additional manipulation. Data files for the most abundant atmospheric gases, methane, and other pertinent trace gases were exported to

Excel to be combined with MAS band configuration data. 22

CH4 Spectra

2.50E-19

2.00E-19

1.50E-19

1.00E-19

5.00E-20

0.00E+00 0123456789101112131415 wavelength (μm)

Figure 7. Plot of HITRAN spectral lines for CH4 gas.

Details of MAS

The MODIS Airborne Simulator has been active since 1994. The 50 band sensor has bands with spectral resolution fine enough to possibly be considered a hyper-spectral sensor.

The MAS sensor has bands with spectral resolutions that exceed such sensors as Landsat, but the

MAS bands are not as narrowly defined in other true hyperspectral sensors such as Hyperion. At a 20 km flight altitude, the spatial resolution is 50 meters per pixel. The data were pre-processed with geographic registration of the data and minimal atmospheric correction along a 5 meter atmospheric path during calibration. The datasets are selected flight paths from various missions flown during the period of 1995 to early 2004. Each MAS mission may have its own unique band configuration and signal-noise-ratio (SNR). 23

The dataset files were acquired by ftp from NASA Goddard Space Flight Center DAAC.

Before the datasets could be used for analysis, they required some initial processing. First, the

ftp files were decompressed using the Unix uncompress function. ERMapper does not readily

recognize the MAS HDF format. ENVI does have the capacity to open the datasets as generic

HDF files. So, ENVI was used to open and convert the HDF files into the ERMapper format.

ERMapper was then used to calculate the band statistics for each track. The band statistics

provide data that may be required to modify any of the ratio formulas that were generated later.

Dataset selection began with determining potential sites described in “Selecting Areas of

Interest”. Then, each mission description in the Master List of MAS Missions was examined to see if the mission was flown over an area of interest. Only one mission contained a flight path that coincided with Wood County Ohio. A couple of missions were found to have occurred over the Louisiana coast. A number of missions were flown in the California areas.

After identifying relevant missions, a sample track or scene was ordered from each mission. Each scene was examined to see if the mission datasets were candidates for further

analysis. The Wood County track contained a clean dataset, but was positioned too far to the north to image the landfill site. The flight did cover the Toledo area refineries, so the dataset was retained for further examination. Most of the Louisiana off-shore missions had significant noise problems in the band next to the methane 3.314 µm absorption feature, so these missions were eliminated from further consideration. However, one Louisiana coastal mission is included in this work. The California mission samples covered desirable areas and did not exhibit any

excessive noise problems. Two missions were identified and selected for methane analysis,

based upon extensive coverage of oil/gas fields and geographic overlap of flights within

individual mission scenes. Mission MAS 98-031 contains six tracks over California coastal 24 waters at or near an offshore oil field. The other California mission, MAS 97-127, contains two tracks that fly over a land region north of Long Beach near Bakersfield with multiple oil fields.

The approximate spatial coverages of the datasets used from the California missions are indicated on the California map.

Figure 8. General locations of the off-shore and inland MAS missions and datasets. The off-shore mission covers part of the Santa Barbara Channel area. The inland mission covered an area close to Bakersfield, CA.

Combining HITRAN Data and MAS Bands

HITRAN absorption data are discrete lines with spatial resolutions much finer than MAS band spectral resolutions. Hence, a single MAS band may cover a spectral range that encompasses many HITRAN spectral lines. In order to understand how the MAS sensor will

“see” the gas absorption, the HITRAN lines for each gas must be assigned to the appropriate

MAS bands. The relationship between MAS bands and the CH4 gas spectral lines is illustrated 25

in Figure 9. MAS-spectral relationship plots for all gasses used in the study are included in

Appendix A.

CH4 Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm)

Figure 9. The MAS bands are positioned near the top of the plot. The line position and width of the band lines correspond to the band wavelength limits and range of the individual bands. The bands can be correlated to the gas spectral lines to identify which bands contain absorption features.

The intensities for the lines within the bands were summed to quantify the absorption

band strengths (ABS) for each gas. This task was achieved through the use of two Excel macro

subroutines. The macros are included in Appendix A, with an example of each spreadsheet. The

absorption band strengths for all of the gases in this study can be summarized in a rather lengthy

table. Complete tables for both MAS 98031 and MAS 97127 are included in Appendix A.

Table 2 is a partial representation of the ABS table for MAS 98031. The higher the ABS value is, the greater the absorption potential for that band if the gas is present. It should be noted that 26

these ABS values are determined from HITRAN data that are collected at fixed high gas concentrations. Thus, the MAS sensor signal strength is determined by the ABS value and the concentration of the atmospheric gas. In the example below, CO has high absorption potential in bands 23, 24, and 25. However, the CO absorption potential in bands 22 and 26 is essentially non-existent. If a significant amount of CO gas intercepted the energy wave traveling toward the

MAS sensor, the signal strength for band 22 should not be affected while the signal strength for band 24 should be reduced by some extent.

ABS Values

absorption intensity within each MAS 98-031 band for each individual gas

Gas 22 23 24 25 26 27 28 29 CH4 8.1E-20 1.99E-19 2.45E-19 2.28E-19 4.67E-19 7.82E-18 2.53E-18 1.67E-19 N2 O2 H2O 1.9E-23 4.32E-24 2.8E-22 1.23E-21 2.55E-20 2.46E-20 1.75E-21 8.77E-24 CO 1.38E-4 5.09E-2 2.72E-2 CO2 9.94E-26 8.93E-27 1.36E-23 2.51E-25 OH 1.42E-29 1.18E-27 4.2E-26 6.05E-25 1.45E-20 9.1E-23 2.99E-24 7.27E-26 HO2 1.92E-20 H2CO 4.57E-20 5.75E-18 1.39E-17 SO2 2.01E-21

Table 2. Absorption Band Strengths of selected gases for discrete MAS bands. The ABS value is the sum of spectral lines intensities given in energy absorbed per mol per area (cm-1 / (mol cm-2)).

RS Band Ratio Generation and Evaluation

A fundamental method of RS data analysis is the use of band ratios. As discussed in a

previous section, most ratios for this project are based upon positioning the target gas absorption

peak feature in the denominator and one or more flanks in the numerator. For MAS analysis,

this means using a band with a high ABS in the denominator and using one or more low ABS 27 bands in the numerator. Preferably, these are contiguous bands in a narrowly defined wavelength range. The ideal ratio would be:

Band (#-1) + Band (#+1) 2 * Band (#) where Band (#-1) and Band (#+1) both have ABS values of zero and Band (#) has the highest

ABS value for the target gas. The ratios of ABS values can be used to evaluate potential RS analysis ratios. ABS ratios with low values near zero indicate the presence of the gas will have a strong affect on RS band ratio, producing a high RS band ratio value. ABS ratios with values near one indicate the gas has little effect on the RS band ratio. Finally, ABS ratios with values greater than one indicate the gas will reduce the RS band ratio value.

It is important to remember that the atmosphere is not a uniform pure gas. It is comprised of many differing gases with concentrations that have both spatial and temporal variations.

Because the atmosphere is a mixture of gases, the most abundant gases need to be considered when evaluating potential RS band ratios. The ABS ratio should also be applied to all gases that are expected to be present in significant concentrations. A number of various ABS ratios needed to be generated and evaluated to determine which ratios are suitable to use as RS analysis algorithms. The ABS ratio value tables for the California missions are included in Appendix A.

A subset of the MAS 98031 ABS ratio values are included in Table 3. The ideal RS band ratio will have an ABS ratio value near zero for the target gas and ABS ratio values near one for all other gases. If other gases are near zero, then the ratio will be non-unique and additional methods may be required to determine which gas may be responsible for any high RS band ratio values. Gases with ABS ratios greater than one can also be a problem. The higher ABS ratio indicates the gas could cause absorption in one of the numerator bands, thus reducing the RS 28 band ratio value. If the ABS ratio is large enough this may actually mute the pixel brightness of target gas concentrations.

ABS ratio values Gas (26+28)/(27x2) (26+29)/(27+28) 26/27 28/27 CH4 0.191 0.0613 0.0598 0.323 N2 1.00 1.00 1.00 1.00 O2 1.00 1.00 1.00 1.00 H2O 0.553 0.967 1.04 0.0711 CO 1.00 1.00 1.00 1.00 CO2 27.1 54.2 54.2 3.98E-75 OH 79.5 154 159 0.0328 HO2 9.62E+78 9.62E+78 1.92E+79 1.00 H2CO 62.9 2.40 2.19E-80 126 SO2 1.00 1.00E+78 1.00 1.00

Table 3. Select ratios and the ABS ratio values for each gas.

In addition to using the ABS ratio values, the gas concentrations also needed to be considered. The sensor signal strength is dependent on gas absorption strength and gas concentration. RS band ratios that have two gases with similar ABS ratio values may produce viable results if the target gas has a concentration significantly higher than the interference gas.

Or, an interference gas with a seemingly acceptable ABS ratio value may prove to be consequential if it is present at high enough concentration. Table 4 summarizes how the ABS ratios and concentrations can be used to evaluate the RS band ratio. 29

Table for Evaluating RS band ratio based on ABS ratios and Gas concentrations

Target Gas Interference Gas Impact of Interference Evaluation ABS ratio Concentration ABS ratio Concentration on RS band ratio near 0 low near 0 low significant Poor near 0 low near 0 high high Fail near 0 high near 0 low low Good near 0 high near 0 high significant Poor near 0 low between 0-1 low low Good near 0 low between 0-1 high significant Poor near 0 high between 0-1 low very low Excellent near 0 high between 0-1 high low Good near 0 low near 1 low very low Excellent near 0 low near 1 high very low Excellent near 0 high near 1 low very low Excellent near 0 high near 1 high very low Excellent near 0 low greater than 1 low low Good near 0 low greater than 1 high significant Poor near 0 high greater than 1 low very low Excellent near 0 high greater than 1 high low Good near 0 low much greater low high Fail near 0 low much greater high very high Fail near 0 high much greater low significant Poor near 0 high much greater high very high Fail between 0-1 low near 0 low high Fail between 0-1 low near 0 high very high Fail between 0-1 high near 0 low high Fail between 0-1 high near 0 high high Fail between 0-1 low between 0-1 low significant Poor between 0-1 low between 0-1 high high Fail between 0-1 high between 0-1 low significant Poor between 0-1 high between 0-1 high high Fail between 0-1 low near 1 low low Good between 0-1 low near 1 high low Good between 0-1 high near 1 low low Good between 0-1 high near 1 high low Good between 0-1 low greater than 1 low significant Poor between 0-1 low greater than 1 high high Fail between 0-1 high greater than 1 low significant Poor between 0-1 high greater than 1 high high Fail

Table 4. The table is used to help evaluate the prospective ratios by comparing the ABS ratio values and expected concentrations of the gas the ratio is intended to detect and other gases that may also affect the ratio algorithm. 30

Expected Sensor Signal Strengths

Blackbody curves are continuous over a range of wavelengths. MAS bands cover

discrete segments of the spectrum. The bands may be continuous with neighboring bands or the

bands may be separated by gaps of non-detected wavelength ranges. Also, not all bands are

configured with the same bandwidth dimension. If the sensor detected an ideal blackbody with

no intervening signal alteration, the MAS spectral profile would not be the same as the BB curve.

Instead, the spectral profile shows the energy emitted and detected in the parts of the spectrum

covered by each band. The ideal BB curves can be calculated using Planck’s Formula with the blackbody temperature. The curves for solar illumination reflection (6000K) and background surface emission (300K) are presented in figure 10. The cross-over point for the two curves is dependent on the amount of solar reflection and the temperature of the low temperature emission body (background, atmosphere, etc…). If the amount of solar reflection is decreased, the cross- over shifts to the left (shorter wavelength). If the emission body temperature is lower, the cross- over shifts to the right (longer wavelength).

BB Curves Close-up of 2-5µm 6000K @ 20% reflected and 300K emitted 0.05 1.6 0.045

1.4 0.04

1.2 0.035

1 0.03 0.025 0.8 0.02 0.6 0.015 0.4 0.01

0.2 0.005

0 0 0123456789101112131415 2345 wavelength (micrometers) wavelength (micrometers) Figure 10. BB Curve plots for solar reflected (violet) and 300K surface emission (dark blue). The left plot illustrates the dominance of solar reflection at shorter wavelengths while the emission is dominant at longer wavelengths. The right plot is a magnified scale in the 2-5µm range. Near the crossover point, the curve values are similar enough that both reflectance and emission have relative importance.

31

The details for curve calculations, background reflectance, and MAS signal strengths are included in Appendix A. The expected MAS spectral profile or sensor signal strengths were derived from the same formula. The formula was integrated over the wavelength range for each band. This was accomplished through the use of the mathematics software MAPLE9. RS spectral profiles are typical portrayed as plots with the band numbers along the x-axis and signal strength on the y-axis (see figure 11). The plots illustrate ideal spectral profiles with specified emission and reflection parameters with no intervening atmosphere. The 20% reflected signal strength at short wavelengths is much weaker than may be expected because the band widths are narrow in the short wavelength region and wider in the long wavelength region.

a 300k BB Emittance for MAS 98-031 Bands b Emittance with 20% sun reflectance 5.0E-08 5.0E-08

4.5E-08 4.5E-08

4.0E-08 4.0E-08

3.5E-08 3.5E-08

3.0E-08 3.0E-08

2.5E-08 2.5E-08

Integrated 2.0E-08 Integrated 2.0E-08

1.5E-08 1.5E-08

1.0E-08 1.0E-08

5.0E-09 5.0E-09

0.0E+00 0.0E+00 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Band Band c Emittance with 1% sun reflectance d Emittance with 1% sun reflectance (scaled) 5.0E-08 5.0E-09

4.5E-08 4.5E-09

4.0E-08 4.0E-09

3.5E-08 3.5E-09

3.0E-08 3.0E-09

2.5E-08 2.5E-09

Integrated 2.0E-08 Integrated 2.0E-09

1.5E-08 1.5E-09

1.0E-08 1.0E-09

5.0E-09 5.0E-10

0.0E+00 0.0E+00 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Band Band Figure 11. a) Ideal spectral profile for MAS bands detecting 300K BB emission. b) Spectral profile with both 20% solar reflection and 300K BB emission. c) Spectral profile with both 1% solar reflection and 300K BB emission. d) Magnified scale of 1% solar reflection and 300K BB emission. 32

It should be noted that an actual spectral profile would not be an exact match of the calculated spectral profile. The sensor detectors have a spectral response profile with the highest sensitivity near the center of the bandwidth with reduced sensitivity away from the center. For

MAS bands, the band limits are typically defined at the 50% sensitivity wavelength. Figure 12 illustrates what an ideal sensor response would be and what a realistic response looks like.

Figure 12. The ideal sensor would be 100% responsive to any energy detected within the defined band limits. Actual sensors respond more like the right image. MAS band limits are defined based upon the sensor response.

Generating the expected ideal spectral profile was useful for two reasons. Firstly,

ERMapper and ENVI can provide spectral profiles for individual pixels within the MAS datasets. The pixel spectral profile could then be compared with the ideal profile for analysis.

Secondly, the expected signal strengths can be used in the RS ratios. Table 5 lists the various RS ratio algorithms and values generated from using the ideal signal strengths. The tables give the unaltered expected RS ratio values with no intervening absorption. These ratio values were a starting point for determining the analysis values used to classify “high” gas concentrations. 33

Tables for both California missions with appropriate reflection-emission parameters are included in Appendix A.

300K Emittance for MAS 98-031Band Ratios

no solar with 1% solar Ratio reflection reflection (26+28)/(27x2) 1.14 1.12 (26+29)/(27+28) 1.15 1.14 26/27 0.65 0.76 28/27 1.62 1.49 (42+44)/(43*2) 0.85 0.85 (27+30)/(28+29) 1.00 1.00 (25+27)/(26*2) 0.78 0.79 1.5(34+38)/(35+36+37) 0.99 0.99 (33+38)/(34+35+36+37) 0.50 0.50 (41+43)/(42*2) 0.66 0.66 32/33 0.79 0.80 45/46 1.16 1.16

Table 5. RS ratios and values using ideal band spectral signal strengths for 300K background and with background + reflection signal strengths.

34

METHODOLOGY: DATA ANALYSIS

The data analysis methods are described in two parts. The first deals with direct

manipulation of the MAS datasets. The second section uses the results of the band ratios with

GIS to perform spatial analysis within each scene and spatial analysis between select scenes.

ERMapper

ERMapper6.4 was used to create the ratio images and data files after the original MAS

HDF datasets were uncompressed and converted to the ERMapper format. The datasets with all

50 bands were opened in ERMapper. A true color image of each scene was produced for visual

reference and interpretation. Using the Edit Algorithm and Edit Formula functions, the

appropriate ratios and bands were input. The ratio image was then stretched for visual

interpretation. Copies of the ratio product were saved as an ERMapper algorithm and as an

IEEE 4ByteReal raster file. This was done for each scene using all of the appropriate analysis

ratios.

ArcMap

The GIS spatial analysis of the ratio raster files was performed with ArcMap. The ideal

absorption ratio values, distribution histograms, and scene content information were used to

determine a cut-off value to evaluate if the pixel represents a high or low gas concentration. The

raster image was reclassified as high/low pixels. As part of the reclassification, each “high”

pixel was assigned a value used to designate the gas or ratio it represents. Table 6 summarizes the designation values, describes what high gas concentration could be indicated by the code, the name used to identify the ratio, and the ratio algorithm. Ratio rasters were reclassified for each 35

gas and scene. All of the reclassified raster images within each scene were then added together

in Raster Calculator to create a new raster image.

GIS CLASSIFICATIONS CODE GAS RATIO NAME RATIO 0 no high gas concentration 2 Methane meth2 26/27 3 Methane meth3 (26+28)/(27x2) 4 Methane meth4 (26+29)/(27+28) 10 Methane sprt (41+43)/(42*2) 20 Methane 2827 28/27 100 CO CO (33+38)/(34+35+36+37) 1000 CO2 CO2 32/33 2000 HO2 CO2 HO2nCO2 (42+44)/(43*2) 10000 OH (low) lowOH 45/46

Table 6. GIS classification codes for RS ratio algorithm products.

The resultant raster pixel values contain information for each gas. Table 7 is a partial

table of the possible combination values and what the number represents. The complete table is

included in Appendix B. A pixel with a grid code value equal to 0 (zero) would indicate none of

the gasses have “high” concentrations for that specific pixel location. A value of 2 would mean

only the Meth2 ratio had a higher value for that pixel. A pixel where more than one gas ratio had

a high value would have a pixel value equal to the sum of the ratio raster classifications. For

example: if Meth2 (GIS code 2) and Meth3 (GIS code 3) and CO (GIS code 100) all had high ratio values for the same pixel, the resultant GIS code would be 105 (2+3+100). Hence, the resultant raster code gives information for the presence and absence of multiple potential high gas concentrations at one location. 36

Next, the resultant raster was converted to a feature shapefile. This was done to merge contiguous discrete pixels with the same value into coherent polygons with quantitative areas.

The shapefiles were imported into Personal Geodatabases so the file could be edited in order to reduce the number of polygon features. All zero value features were removed, as were features with smaller than one square original pixel. After the GIS polygon database was cleaned up, the areas for each feature type were summed. The area sums could be used to interpret the data.

For scenes which spatially overlap other scenes, further work was conducted. Using the geo-reference data, the two scenes were spatially correlated. The ratio images and data were used to determine if the features within one scene were confirmed in the other scene.

37

Summary of GIS Raster Calculator Results

ratios indicate these gases present at "high" concentrations * * exception is OH

0 none 100 CO 2 meth2 102 CO meth2 3 meth3 103 CO meth3 4 meth4 104 CO meth4 5 meth2&3 105 CO meth2&3 6 meth2&4 106 CO meth2&4 7 meth3&4 107 CO meth3&4 9 meth2&3&4 109 CO meth2&3&4 10 sprt 110 CO sprt 12 sprt meth2 112 CO sprt meth2 13 sprt meth3 113 CO sprt meth3 14 sprt meth4 114 CO sprt meth4 15 sprt meth2&3 115 CO sprt meth2&3 16 sprt meth2&4 116 CO sprt meth2&4 17 sprt meth3&4 117 CO sprt meth3&4 19 sprt meth2&3&4 119 CO sprt meth2&3&4 20 2827 120 CO 2827 22 2827 meth2 122 CO 2827 meth2 23 2827 meth3 123 CO 2827 meth3 24 2827 meth4 124 CO 2827 meth4 25 2827 meth2&3 125 CO 2827 meth2&3 26 2827 meth2&4 126 CO 2827 meth2&4 27 2827 meth3&4 127 CO 2827 meth3&4 29 2827 meth2&3&4 129 CO 2827 meth2&3&4 30 2827 sprt 130 CO 2827 sprt 32 2827 sprt meth2 132 CO 2827 sprt meth2 33 2827 sprt meth3 133 CO 2827 sprt meth3 34 2827 sprt meth4 134 CO 2827 sprt meth4 35 2827 sprt meth2&3 135 CO 2827 sprt meth2&3 36 2827 sprt meth2&4 136 CO 2827 sprt meth2&4 37 2827 sprt meth3&4 137 CO 2827 sprt meth3&4 39 2827 sprt meth2&3&4 139 CO 2827 sprt meth2&3&4

Table 7. Partial list of raster calculator GIS classification codes for combining RS ratio products. The code is the sum of the individual ratio classifications, effectively creating a multi-layer overlap product. 38

RESULTS

MAS Band Ratios

Fifteen different ratios of various MAS bands were considered for RS data analysis and

indication of relative gas concentrations. Of these, nine ratios were selected. Table 8 lists the

primary ratios and the corresponding ABS ratio values for select gas spectra. Below the table are

the names used to identify the ratio algorithm, followed by the ratio algorithm in parentheses,

then brief descriptions of how the ratio algorithm may respond to individual gases.

Analysis ABS ratio values

Meth2 Meth3 Meth4 2827 Sprt CO CO2 HO2nCO2 low OH CH4 0.0621 0.192 0.0610 0.323 0.00219 11.2 4.43 611 1.00 N2 1.00 1.00 1.00 1.00 1.00 1.828 0.344 1.00 1.00 O2 1.00 1.00 1.00 1.00 1.00 0.535 1.00 1.00 1.00 H2O 1.036 0.553 0.967 0.0712 54.6 2.609 0.191 8.060 0.813 CO 1.00 1.00 1.00 1.00 1.00 0.00121 1.00 1.00 1.00 CO2 54.2 27.1 54.169 3.98E-75 2.24E+77 6.48 6.08E-07 0.509 2.13 OH 159 79.477 153.871 0.033 46.902 0.513 8.37E+69 53.4 2.59E+74 HO2 1.92E+79 9.62E+78 9.62E+78 1.00 6.3E+79 0.538 1.00 7.94E-81 0.661 H2CO 2.19E-80 62.9 2.40 125.817 1.00 0.504 1.00 1.00 1.00 SO2 1.00 1.00 1.00E+78 1.00 0.00137 0.520 3.48E+80 359 1.00

Table 8. ABS ratio values for the RS ratio algorithms used in the study.

Meth2 (26/27) should be sensitive to the presence of CH4 gas. The ratio is also sensitive to formaldehyde H2CO. There is an inverse sensitivity to HO2.

2827 (28/27) is moderately sensitive to CH4 gas concentration. The ratio is sensitive to H2O. It is also sensitive to CO2.

Meth3 (26+28)/ (27*2) is moderately sensitive to CH4. The ratio is slightly sensitive to water vapor H2O. There is an inverse sensitivity to HO2.

Meth4 (26+29)/ (27+28) is sensitive to CH4. It too has an inverse sensitivity to HO2.

Sprt (41+43)/ (42*2) is sensitive to CH4 and SO2.

CO (33+38)/ (34+35+36+37) should be sensitive CO.

39

CO2 (32/33) is sensitive to CO2. The ratio is inversely sensitive to OH and SO2.

HO2n CO2 (42+44)/ (43*2) is sensitive to HO2. It is slightly sensitive to CO2. It exhibits a moderate inverse sensitivity to CH4.

low OH (45/46) has an inverse sensitivity to OH gas concentration.

RS Ratio Analysis and Images

The following images are the products of applying the ratio algorithms to the

MAS98031_01 dataset using ERMapper. The approximate location for the images are shown in

figure 13. This dataset is just one of more than 10 datasets examined in this study. The

complete resultant images from all pertinent datasets are included in Appendix C. Not all images

were analyzed to the same extent. All of the ratios listed above were applied to the California

datasets, both off-shore and inland. An additional high OH ratio (46/45) was applied to the two inland California datasets. Only the ratios using the fundamental C-H absorption feature in band

27 were applied to the Louisiana and Ohio datasets.

Images produced from the MAS98031_01 are used here to illustrate some of the concepts

and assumptions that can be applied to all of the other dataset analysis images. A true color image is included for each dataset. This provides some basic contextual knowledge about the visual scene such as background material, cloud cover, and surface features. Figure 14 shows a dataset frame covering a marine environment with an island and limited cloud cover. This knowledge is useful for interpreting the ratio analysis images.

Many of the ratio images are produced in a greyscale display with dark pixels representing low ratio values and brighter pixels having higher ratio values. The images in

Figure 16 have been stretched to enhance the relative value differences within the scene. Color images were generated for some dataset analysis that warranted intensive examination. The 40 color representation of the ratio data produces blue pixels for low ratio values and red pixels for high ratio values. The b&w ratio images in figure 14 correlate to the color ratio images in figure

15. The color images can sometimes be easier to visually interpret than the black & white images. It is important to remember that the images show only the relative values of the ratio algorithm values. A bright/red area in the image does not automatically indicate the presence of the gas that corresponds to that ratio algorithm. The matter of ratio image interpretation is addressed using specific cases in the Discussion section.

Figure 13. General location of RS images in figures 14 and 15. 41

California 98031_01 True Color Meth2 2827 RGB (3,2,1) 26/27 28/27

Figure 14. True color and select b/w RS ratio images from one of the California off-shore datasets. See figure 13 for location. 42

California 98031_01 True Color Meth2 2827 RGB (3,2,1) 26/27 28/27

Figure 15. Same images as in figure 15 with Blue-Red color display of the RS ratio images. See figure 13 for location. 43

GIS Spatial Correlation

GIS was utilized to perform two functions: spatial correlation with known ground features and spatial correlation between ratio algorithm results. To identify ground features, the mission scene boundaries were overlain on topographic DRGs downloaded from the Ohio and

California DNR database compilations. The topographic maps were examined and the location of refineries and other significant oil/petroleum facilities noted. Once incorporated into GIS, the points were overlain on the true color and ratio images. A subset of the Ohio MAS 96144_13 dataset illustrates the ground feature relationship. Figure 16 shows the general area of the refineries and dataset. The locations of these refineries are superimposed on the RS image in

Figures 17. Circles are used instead of points for three reasons: 1) to make the land features easier to identify, 2) as a visual reminder that these features often encompass a small geographic area, 3) any associated gas features would also be area rather than discrete point features.

Figure 16. Location of RS image in figure 17 covering part of Toledo, Ohio and Oregon, Ohio. 44

Figure 17. True color image of subset region in MAS 96144_13. The scene covers the Toledo-Oregon area. The two refinery complex locations are identified with circles. See figure 16 for general location.

Because atmospheric methane is not an isolated inert gas, the absence/presence of other

gases can be used to interpret the methane algorithm ratio results. GIS was used to examine the

spatial relationships of the various ratio algorithms. Figure 18 shows a close-up of a visible

cloud within MAS 98031_02. The GIS results for a defined area of the cloud are also displayed.

The discrete features within the cloud area are labeled with the resultant GIS code identifying which gas ratios have high values for that specific feature (see Figure 19). 45

Figure 18. Image of cloud within MAS 98031_02 (left). GIS overlay of Raster Calculator results for a defined area corresponding to the cloud (right). Subset in right image shows approximate location of figures 18 and 19.

Figure 19. Subset within GIS overlay, each discrete feature area labeled with the GIS feature code.

The GIS classification was applied to all of the California datasets. Additional analysis was performed on one offshore dataset, one inland dataset, and the defined cloud area subset presented above. The use of the results is presented in more detail in the Discussion section. 46

DISCUSSION

RS Ratio Algorithms

During the proposal stage of this project, a small experimental trial was conducted to see if the project was feasible. Four datasets from a mission along the Louisiana coast were acquired. Only the RS ratio Meth3 (26+28)/(27*2) was first considered and applied to the datasets. Two of the scenes contained correlating results that indicated potential detection of a methane plume. Initially, these findings were dismissed as false positives. The ratio results corresponded to a visible feature, most likely a smoke plume from an industrial site. At that early stage of the project, it was assumed that because methane gas is transparent in the visible wavelength region, any methane plume would not be visible. (This assumption was later found to be incorrect, as methane oxidation reactions start a series of chemical reactions that can produce significant amounts of water vapor. Hence, it may be possible for invisible methane plumes to generate visible clouds.)

The “false positive” result led to a closer examination of the (26+28)/(27*2) algorithm and the realization that other gases could affect the ratio values. The impact other gases could have on any potential RS ratio was discussed in the Methodology section and summarized for the selected algorithms in the ABS ratio value table. In this specific case, it was found that both CH4 and H2O could produce higher RS ratio algorithm values. The initial presumption was that the algorithm was detecting water vapor released with the smoke plume.

Later during the study, further analysis showed the Meth3 ratio was three times more sensitive to methane than to water vapor, but the ratio is not greatly sensitive to either gas. The methane absorption intensity in band 27 is over 300 times greater than the water vapor absorption. The methane intensity is even stronger compared to the other gases. So, even 47

though the ratio (26+28)/(27*2) algorithm is capable of detecting methane plumes and

preferentially responsive to methane, it does not provide a unique analysis interpretation.

Because the methane absorption feature extends across a range of wavelengths, part of the feature is contained within band 28 (centered near 3.4µm). In order to encompass the full intensity of the absorption feature, another algorithm was developed. Meth4 (26+29)/(27+28) is

three times more sensitive to methane than Meth3 (26+28)/(27*2) is. The Meth4 ratio is

virtually non-sensitive to water vapor. Both ratios have a strong inverse sensitivity to HO2,

which may be a product of methane atmosphere reactions. The presence of HO2 gas associated with the methane gas could dramatically lower the ratio algorithm values, potentially preventing the detection of the methane plume with these ratios.

A third ratio based upon band 27 is Meth2 (26/27). This ratio shares the same characteristics described for Meth4 (26+29)/(27+28). It is also very sensitive to H2CO

(formaldehyde), one of the immediate CH4 oxidation products. The combination of the ability to

respond to both methane and formaldehyde strengthens the argument that Meth2 (26/27) is a

viable algorithm. However, (shortwavelength/longwavelength) two band ratios are more sensitive to temperature variations. Also, the HO2 problem may reduce the effectiveness of this

ratio.

The fourth algorithm based upon band 27 is labeled 2827 (28/27). It is slightly less

sensitive to methane than Meth3 is. This ratio is not sensitive to HO2. It has a strong sensitivity

to water vapor. The ratio (28/27) is a (longwavelength/shortwavelength) ratio that has an inverse response to temperature variations that (26/27) has.

All of these ratio algorithms produce non-unique results that could represent multiple gas arrangements. Another methane detection ratio, Sprt (41+43)/(42*2), is not based on band 27. 48

Sprt was meant to be an independent collaboration of the primary methane absorption feature.

The ratio does have a strong sensitivity to methane. Unfortunately, the CH4 absorption intensity

in band 42 is 100 times weaker than the CH4 absorption intensity in band 27. It also has a strong

inverse sensitivity to CO2 and HO2. There is a moderate inverse sensitivity to water vapor that is

not overly concerning, but because the CH4 absorption intensity is only ten times greater than the

water vapor absorption intensity, it could present problems for interpretation.

California off-shore MAS 98031

Because the bulk of the MAS 98031 flights occurred over off-shore environments south

and south-west of coastal lands and cities, the expected net chemical reaction series is:

CH4 + 3OH +2O2 CO2 + 3 H2O + HO2

The atmosphere in and near significant methane plumes should have had lower concentrations of

OH and higher concentrations of the products CO2, H2O, and HO2. “Fresh” or recently released

methane plumes in this environment should have CH4 concentrations greater than the water

vapor concentration. The water vapor concentration may soon surpass the methane, as three H2O are produced for every oxidized CH4.

For interpretation of the individual RS ratio results, any of the band 27 algorithms have

the potential to detect fresh plumes. Meth4 is the best single ratio for fresh plumes. It provides a result that is only sensitive to the methane gas. Meth2 is also responsive to fresh plumes but the

methane absorption effect may be negated due to the lower temperature of the plume. Both the

Meth3 and the 2827 ratios will provide positive results for either methane or water vapor.

Older or sustained plumes accompanied by higher H2O concentrations may produce

different algorithm responses. Meth2 will be less effective for older plumes, as the water vapor 49

reduces the impact of the methane absorption. Both Meth3 and 2827 will easily identify older

methane plumes with abundant CH4 and H2O. However, the algorithms cannot easily differentiate between water vapor features with CH4 versus water vapor features without CH4.

Meth4 would not be affected by the additional presence of water vapor. However, Meth2,

Meth3, and Meth4 results could all be dampened by the presence of the HO2 product.

All of the ratio results are included in Appendix C. Of the six off-shore datasets, only

one scene contained a feature that warranted further study and discussion. Within the MAS

98031_02 dataset, multiple RS ratio results indicated a positive feature that corresponded to a

visible cloud. Further scrutiny of the true color image revealed what appears to be an oil slick on

the ocean below the cloud area (see right image in Figure 20).

Figure 20. True color image of MAS 98031_02 (left image). The scene contains some low altitude visible clouds and some hazy, possibly higher altitude clouds. The right image is the same scene with color stretching to enhance the oceanic features. See figure 18 for general location.

50

The RS ratio results are reproduced here in figures 21 and 22 for the purpose of

discussion. Three of the ratios based upon band 27 produce positive results, Meth3, Meth4, and

2827. The methane support ratio using the absorption in band 42 also indicates a positive result,

though it is much harder to discern. These positive results are interpreted as a methane plume- cloud. The arguments for the methane feature interpretation are as follows:

1. Proximity to what appears to be an active oil seep, which are known to occur in the mission

area.

2. The algorithm 2827 with ratio (28/27) has high values in response to this feature. The ratio

results for this cloud do not agree with the results for other clouds. The hazy clouds to

the north-east and along the west edge of the scene have low (28/27) ratio values. The

typical (28/27) response to clouds is easier to see in Figure 16 in the Results section. In

this image, the ratio produced high values (bright pixels) for the island, moderate values

for the ocean, and relatively lower values or dark pixels for the cloud cover. The cloud

feature in figure 22 has higher values, as would be expected if absorption occurred in

band 27. If the absorption were due solely to the presence of water vapor, the cloud ratio

result would not differ significantly from the other clouds.

3. Meth3 exhibits a result similar to 2827. This algorithm is more sensitive to methane than it is

to water vapor. Once again, the other hazy clouds in this scene and the more substantial

visible clouds in other scenes are not positive features. The Meth3 positive feature may

be slightly muted by the presence of HO2.

4. The cloud is also a positive feature using the Meth4 (26+29)/(27+28) algorithm. This ratio is

not affected by water vapor, which strengthens the argument that the other band 27 ratios

that respond to both methane and water vapor are not just detecting the water vapor. 51

5. The methane support algorithm utilizing the minor methane absorption feature in band 42

agrees with the three ratios based upon the main absorption feature in band 27.

6. Ratios for the gas most commonly associated with methane, CO, and its product, CO2, also

produce positive results that agree with the algorithms above.

A thorough examination of the interpretation requires not only arguments for the interpretation, but also possible arguments against the interpretation. Below is a list of counter- arguments against the interpretation and some possible refutations in brackets [].

1. Many of the ratio algorithms are sensitive to more than one gas that may be in the scene.

This produces results which can have multiple interpretations for each ratio.

[Fortunately, not all of the ratios have identical responses. By comparing the results from

other ratios, one or more interpretations can be eliminated, thus validating the remaining

interpretation. See argument #4 above.]

2. If the dataset had invalid data in one band, comparing ratios based upon that same band could

produce invalid results that would not immediately be evident. For example, if the data

for a single pixel were random noise rather than a true signal strength measurement, all of

the ratios using that bad data could produce similar false results. [Hence, the use of the

support ratio with the weaker absorption feature in band 42. See argument #5 above.]

3. The ratios for CO and CO2 produce higher values for the visible clouds in this scene and

some of the other marine datasets. Thus, the positive results for the cloud feature in MAS

98031_02 do not necessarily mean the feature has higher concentrations of these gases

due to the presence of methane. [There is no refuting this counter-argument. The

argument #6 above may not be meaningful.] 52

4. Ratio HO2nCO2 indicates negative feature results, suggesting there is no higher

concentration of the HO2 as might be expected. [This ratio did not appear to respond

well in any of the datasets, as can be seen in the appendix figures. Assuming the results

for this algorithm are accurate, the counter-argument can still be refuted. The ratio is a

poor choice for marine environments because it has an inverse sensitivity to methane. If

the methane concentration exceeds the HO2 concentration, the ratio will produce lower

values.]

5. The ratio for lowOH also has negative results for the cloud, indicating the feature has a

higher concentration of OH, not lower as the net chemical reaction predicts. [The net

chemical reaction series does suggest the area would have decreased concentration as the

hydroxyl is consumed in the oxidation reaction. However, the net series does not

consider the extended reaction of the water vapor production. The clouds in the other

scenes also have negative lowOH results. It may be possible that the continuing reaction

of H2O into OH compensates for the OH oxidation loss.]

53

California Off-shore MAS 98031_02

2827 (28/27) Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28) Support (41+43)/(42*2)

Figure 21. Images of individual RS ratio algorithm results with color display. Low ratio values are blue pixel, high ratio values are red color pixels. See figure 18 for general location. 54

California Off-shore MAS 98031_02

CO (33+38)/(34+35+36+37) CO2 (32/33)

HO2nCO2 (42+44)/(43*2) lowOH (45/46)

Figure 22. Images of individual RS ratio algorithm results with color display. Low ratio values are blue pixel, high ratio values are red color pixels. See figure 18 for general location. 55

California inland MAS 97127

Inland environments, especially those with human activity, generally have higher

production of NOx. The net chemical reaction series for these types of environments is:

CH4 + 10O2 CO2 + H2O + 5O3 + 2OH

The atmosphere in and near significant methane plumes should have had higher concentrations

of the products CO2, H2O, and OH. Because of the possible production of OH, the inverse ratio

of lowOH was added to the analysis: highOH (46/45). Only one molecule of H2O is produced

for each methane molecule consumed. It is expected that the formation of visible clouds is less

likely in these environments. The other major difference for RS ratio algorithm interpretation in

these datasets is the lack of HO2 production. This means there is no reason to believe the

possible methane regions contain higher concentrations of the HO2 gas than areas that do not

have higher methane concentrations. So, the HO2 effect on the ratios will not have any net effect

on the analysis results.

As briefly mentioned in the results, the location of oil refineries and related facilities in and near the dataset coverage areas were incorporated into the analysis. Figure 23 shows a

close-up view of the MAS 97127_02 true color image with the locations identified with circles.

The locations were also included with the RS ratio result images. It was hoped that positive

result features would clearly be associated with one or more of the facilities, either as a discrete

feature proximal to the refinery or a more diffuse feature further downwind. No such features

are located in either of the inland datasets. 56

Figure 23. Subset of MAS 97127_02. True color image showing locations of refineries identified with circles. Approximate location shown in grey area or right image. 57

Meth2 (26/27) Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28) 2827 (28/27)

Figure 24. Band 27 RS ratio results for select refineries in MAS 97127_02 dataset. Blue pixels have lower ratio values; red pixels have higher ratio values. Locations of refineries identified with circles. See figure 23 for location. 58

Figure 24 shows the RS ratio results in the area with some refineries and other oil/gas facilities. No methane plumes are evident in the scene. Each ratio result does have areas with higher values (red areas) near at least one location. However, further examination rejected the interpretation of the individual areas as positive results. There are no visible clouds within the scene, so it was assumed the issue of water vapor interference did not affect the ratio analysis.

This greatly simplified the result interpretation. Only HO2 and CO2 could create potential problems, and these would be easy to overcome. If the presence of HO2 dampened the methane absorption, it would affect Meth2, Meth3, and Meth4 but not affect 2827. Conversely, if CO2 created a false positive in 2827, it would not affect the other ratios.

Each of the RS ratio results show a strong correlation to visible ground features in the true color image. For example, both Meth3 and 2827 indicate possible positive features down and to the right of the two locations near the right edge of the scene. The other two ratios do not agree with the positive feature interpretation, suggesting that something other than band 27 absorption is responsible for the ratio response. A comparison with the true color image shows the results occur in the same locations as some of the visible grey tone areas.

The ratio relationships to visible ground features can be better explored by examining

Figure 25: a close-up view of the north-west region of the dataset (upper right hand corner area of complete true color image in the appendix). In the true color image, green areas show vegetation coverage, while the grey and the brown areas show no significant vegetation coverage. 59

Meth2 Meth3

Meth4 2827

Figure 25. Close-up view of fields in MAS 97127_02. True color image and ratio results. See figure 23 for general location. 60

As can be seen in the ratio algorithm images, some of the bare fields have higher ratio

values. One possible interpretation is that these fields are natural seeps. This region was

selected because the valley contains numerous gas/oil fields and documented natural seeps. It

may be possible that methane is emanating from the soil all across the region, but is restricted or

masked by the vegetation coverage. So, fields with crops would have lower ratio values and bare

fields would have higher ratio results. This interpretation seems unlikely. The boundaries of the

ratio result features closely match the boundaries of the land features. If the ratio results were

due to the presence of methane or another atmospheric gas, it is unlikely the field boundaries

would be so evident in the ratio image.

Also, only certain types of bare fields produce high ratio values. For example, the

algorithms Meth3 and 2827 have higher values for the grey colored fields but not the brown

colored fields, even though both contain no vegetation. A more probable interpretation is that

the ratio algorithms are responding to absorption properties of the soil materials or agricultural

materials distributed on the fields.

Toledo-Oregon, Ohio MAS 96144

Analysis of the Toledo-Oregon, Ohio refineries did not identify any associated features

that could be interpreted as methane plumes. Figure 26 presents the true color image, Meth2

(26/27) results, and 2827 (28/27) results. The refinery locations are identified with circles.

Algorithm Meth2 does produce results at both refinery complexes that could be interpreted as

methane features. The Meth2 results are contradicted by the 2827 algorithm. In this specific case, neither algorithm is displaying results based upon atmospheric gasses or background temperature variations at the refineries. Instead, the ratio results are due to the high solar 61

Figure 26. (Top) True color image of Toledo-Oregon area. (Center) Meth2 results. (Bottom) 2827 results. Circles indicate refinery locations. Result images: blue = lower values red = higher values. See figure 16 for general location. 62

reflection at these sites (notice how bright the complexes are in the visible true color image).

The high degree of reflection means the solar reflection signal will have a greater impact on the

sensor signal strength than the background emission (see the cross-over point discussion in the

Methodology section). The BB curve plot shows that the reflectance will produce a greater

signal strength in band 26 (at 3.12 µm), lower in band 27 (3.28 µm), and even lower in band 28

(3.43 µm). This would produce the higher ratio values for Meth2 (26/27) and lower values for

ratio 2827 (28/27). If a methane plume were present, the absorption in band 27 would raise the

(28/27) ratio result value. The effect could be substantial, as the light path for a methane plume

above the high reflection surface would pass through the plume twice; effectively doubling the

energy absorption due to the methane gas absorption feature.

GIS Analysis

Due to the high degree of heterogeneity in the land scene datasets, visual interpretation of

the ratio results can be difficult. GIS spatial analysis provided a means to better examine and

compare ratios results. The ratio algorithm results were compiled into a single geo-database for

each California mission. The true color scene for MAS 97127_02 is shown in Figure 27. The

yellow areas are part of a GIS overlay displaying regions which have a higher density of pixels where both the Meth2 and 2827 algorithms produce higher values. This combination of ratios was used for the land scene because the ratios Meth2 (26/27) and 2828 (28/27) have inverse responses to temperature variations and to reflection/emission relationships, both of which are important considering the diversity of land background materials. 63

Figure 27. True color image of MAS 97127_02 dataset with yellow areas indicating regions of higher density Meth2—2827 ratio result agreement. General location shown in right image.

Most of the high density areas occur in the valley region (top and center portions of image) rather than the hills and mountains (bottom portion of image). This may or may not be significant. The gas/oil fields are located in the valley rather than the hills, but the land use and development are also much different for the two regions. 64

A close-up of the area around the refineries is displayed in Figure 28. In addition to the yellow density layer, the figure includes the GIS raster calculator features in which Meth2 and

2827 agree (shown in red). There is a loose correlation between the land type features and the

Meth2—2827 features, which can be seen by the (yellow) high density distribution. However, the ratio agreement features occur over all types of land features.

Figure 28. Close-up view of MAS 97127_02 true color image with Meth2—2827 feature agreement density in yellow and actual features in red. See figure 27 for general location.

65

In order to better interpret the results, three types of land cover were examined to see

what GIS features were present in these areas; rural vegetation, greyed rural (site A on figure

28), and greyed near urban edge of Bakersfield (site B). Features with the 10025 code were

common in all three areas, indicating high ratio results for Meth2, Meth3, 2827, and lowOH.

There were also many 10029 features where Meth4 agreed with the ratios listed for 10025.

Other common features present in the three areas were 1025, 11025, and 11029; in which the

CO2 ratio also had high results. The only major difference was the presence of some 3025, 3029, and 13025 features in the site B area. These features have pixels in which both the CO2 and the

HO2 ratios had high values.

Because the site A feature presence is so similar to the agricultural site features, there

seems to be no way to determine which features may be methane plumes and which are false

positives. The differences of some features at site B could indicate these features are due to

differences in atmospheric content. However, there is not enough evidence to interpret them as methane plumes considering HO2 production is not expected in this environment.

Quantitative Estimation

⎛ ⎞ (−α w) ⎜ 0.00037418 ⎟ The equation e λ ε dλ can be used to quantify the effect of the gas λ ⎜ ⎛ 14388 ⎞ ⎟ ∫ ⎜ ⎜ ⎟ ⎟ ⎝ e⎝ λTb ⎠ −1 ⎠ absorption on the sensor signal strength. When the equation is used with an appropriate RS ratio, the total methane column abundance can be determined. The column abundance can then be interpolated to methane plume concentrations (Vincent, 1996). In this study, the quantitative analysis required some adjustments. The equation was simplified by assuming the background

emissivity (ελ) equal to one. Also, the absorption index (αλ) is provided a constant value for each 66 band, determined from the band absorption strength (calculated in this study) and the absorption index plot (found in Vincent, 1997). The modified equation for each individual band is:

⎛ ⎞ ⎜ 0.00037418 ⎟ e −αw dλ ⎜ ⎛ 14388 ⎞ ⎟ ∫⎜ ⎜ ⎟ ⎟ ⎝ e⎝ λTb ⎠ −1 ⎠

The purpose was to calculate the gas abundance (w) within the suspected methane cloud in MAS 98031_02 dataset using the RS ratio (28/27) result values. The ratio values were used to determine the column abundance coefficient, assuming the low RS ratio values were due solely to the average methane atmosphere concentration of 1.7 ppm. The coefficient could then be incorporated with the higher ratio values present in the suspected methane plume. This was done for two different viewing conditions; a model with only 300K background emission and a model with an additional 10% solar reflection due to the presence of the visible cloud.

With the background emission only model, the overall column had an average methane concentration of 16.7 ppm. If the plume was 1000 m thick, the plume concentration would be

334.9 ppm. A combined emission/reflection(@10%) model suggests a higher overall average of

25.7 ppm. A more concentrated plume 1000m thick would have methane at 513 ppm or 0.05% atmospheric volume.

These estimates are only coarse approximations. The estimate may be considerably affected by such factors as: the impact other gases may have on the RS ratio value, the effects of the intervening cloud and atmosphere temperatures, and the simplification of assigning a

constant absorption index (αλ) for each band. 67

CONCLUSIONS

Non-unique MAS sensor band response to such variables as: multiple gas sensitivities,

solar reflection vs background emission, and temperature variation makes result interpretation

problematic. The analysis requires additional information about the environment and viewing conditions. Even with supplemental information about the dataset scene, no single ratio

algorithm produces sufficient results. Using and comparing multiple algorithm results eliminates

some interpretation ambiguity.

The analysis seemed to work better for the off-shore datasets. This is probably because

the background water surface is much more uniform than land is. Water typically has a more

uniform surface temperature, consistently low solar reflection (excluding small glint features),

and more subtle material variations. The major problem with analyzing oceanic scenes arises

because of the water vapor production associated with atmospheric methane reactions. The

water vapor spectra absorption has an important effect on the methane detection ratios. This

study attempted to produce an algorithm to detect only the water vapor. The intent was to try

and couple the methane ratio results with the water vapor ratio results in order to determine if the

methane result was due solely to the presence of methane, just water vapor, or a combination of

methane and water vapor. Unfortunately, MAS bands were configured to avoid the stronger

water absorption features. It may be possible to use other RS data that measures the water vapor

column abundance in conjunction with the MAS ratios to determine if methane is present and

possibly evaluate the methane abundance.

No methane plume features were confirmed in either of the two California inland MAS

datasets nor in the Toledo area MAS dataset. As mentioned above, the high variability of the

surface material spectral properties, background reflection vs emission relationships, and surface 68 temperature variations have a significant affect on the ratios. It is unclear if the failure to identify a positive plume feature is due to the lack of a significant plume or if any plumes did exist and were just lost in the myriad of false positive features.

It may still be possible to utilize the ratios and techniques used in this study to detect methane plumes over land. Using multi-temporal datasets of the same area coverage could provide a basis to detect variable plumes. If the datasets were taken over a short period of days, the land and vegetation features should be consistent in all of the datasets. It is unlikely the plume would have the same geometry or density, especially if the wind conditions were different. The plume could then be detected by close examination of the differences between the datasets. This could be accomplished by a simple visual inspection, GIS overlay, or interferometry method.

While not yet conclusive, the techniques and analysis in this study indicate it is possible to use passive RS methods to detect methane plumes. The MAS sensor is not the ideal sensor for actual application of these techniques. MAS has a spectral bandwidth of approximately 0.15µm.

A truly hyperspectral sensor with a fine spectral resolution with 0.01µm bandwidth (similar to

Hyperion) could work better. A ratio based on two bands (3.358 to 3.368)/(3.306-3.316) would be primarily sensitive to the methane absorption feature. The spectral regions for these two bands contain no significant absorption spectral lines for OH, HO2, or CO2. These bands do contain weak spectral lines for H2O, as water vapor absorption lines are ubiquitous in this spectral region. However, this ratio is designed with an inverse response for water vapor. The presence of methane should produce a high RS ratio result, the presence of water vapor would not. These bands are also designed to effectively eliminate the water vapor inverse sensitivity impact. Because the H2O absorption is very weak in these bands, the atmosphere column water 69

vapor abundance would have to be 6,000 times the total methane abundance in order to mask the

methane response.

In addition to the primary methane ratio, a ratio designed to detect CO would be used to support the methane detection. The CO algorithm would also be a two band ratio (4.42 to

4.43)/(4.60 to 4.70). This ratio could work well because it has no sensitivity to CH4, OH, or

HO2. The ratio has a limited inverse sensitivity to H2O and CO2, but the absorption for either of

these two gases is very weak at these wavelengths. It would also be preferable if the sensor had

a band centered on one of the water vapor main absorption features and if the sensor platform

were capable of multi-temporal coverage.

70

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Monge, Ana, 1998. Underground hydrocarbon spills detection using portable sensors working in the PPM range. Chalmers Tekniska Hogskola, Geologiska Intitutionen, B 1104-9847, vol 460. Nasser, R., and Bernath, P., 2003. Hot methane spectra for astrophysical applications. Journal of Quantitative Spectroscopy & Radiative Transfer, 82, 279-292. Olah, G.A. and Molnar, A., 2003. Hydrocarbon Chemistry, 2nd Ed. John Wiley and Sons, Inc. Hoboken. Rice, Gary K., 1986. Near-surface hydrocarbon gas measurement of vertical migration. In Unconventional Methods in Exploration for Petroleum and Natural Gas IV. Ed. By Martin J. Davidson, Southern Methodist University Press, Dallas, p. 183-200. Rinkenberger, R.K., Rinkenberger, G.R., and Pickford, K., 1997. Remote sensing technology; a means for targeting sites to monitor CO2 and other naturally occurring gases. Proceedings of the Thematic Conference on Geological Remote Sensing, 12, 2, 21-28. Silveira, J.P., Anguita, J., Briones, F., Grasdepot, F., and Bazin, A., 1998. Micromachined methane sensor based on low resolution spectral modulation of IR absorption radiation. Sensors and Actuators B, 48, 305-307. Takeuchi, W., Tamura, M., and Yasuoka, Y., 2003. Estimation of methane emission for West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV. Remote Sensing of Environment, 85, 21-29. The TPF Science Working Group, 1999. The Terrestrial Planet Finder (TPF): A NASA Origins Program to Search for Habitable Planets. Edited by C.A. Beichman, N.J. Woolf, and C.A. Lindensmith. Retrieved May, 2004 from http://tpf.jpl.nasa.gov/ Vincent, Robert K., 1997. Fundamentals of Geological and Environmental Remote Sensing. Prentice Hall. Upper Saddle River. Warneck, Peter, 2000. Chemistry of the Natural Atmosphere 2nd Edition. Academic Press. San Diego. Wei, H., 2000. The seasonal variation of column abundance of atmospheric CH4 and precipitable water derived from ground-based IR solar spectra. Infrared Physiscs & Technology, 41, 313-319. Wuebbles, D.J. and Hayhoe, K., 2001. Atmospheric methane and global change. Earth-Science Reviews, 57, 177-210. 73

Vincent, R.K., 1999. Expanding horizons for geological applications of multispectral and hyperspectral remote sensing data. Proceedings of the Thematic Conference on Geological Remote Sensing, 13, 1, 33-40. Zirnig, W., Hausamann D., and Schreier G., 2002. High-Resolution Remote Sensing Used to Monitor Natural Gas Pipelines. Earth Observation Magazine, Volume 11, Number 9

74

APPENDIX A

Gas spectral plots with MAS band relationships

Excel macros written to assign spectral lines to MAS bands and sum intensities

Absorption Band Strength (ABS) tables

ABS ratio value tables

RS Band ratio evaluation table

Mathematics for BB emission curves, reflectance values, and ideal band signals

Expected ideal Band ratio values 75

CH4 Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm)

CO Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm)

76

CO2 Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm)

H2O Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm) 77

*scaled* N2 Spectra & MAS 98-031 Bands

1.00E-27 1 11 27 43

9.00E-28 2 10 26 42 50

8.00E-28

7.00E-28

6.00E-28

5.00E-28

4.00E-28

3.00E-28

2.00E-28

1.00E-28

0.00E+00 0123456789101112131415 wavelength (μm)

OH Spectra & MAS 98-031 Bands

1.00E-18 1 11 27 43

9.00E-19 2 10 26 42 50

8.00E-19

7.00E-19

6.00E-19

5.00E-19

4.00E-19

3.00E-19

2.00E-19

1.00E-19

0.00E+00 0123456789101112131415 wavelength (μm)

78

Excel macro to create select case text designating the band limits for the next macro

Sub WriteSelectCase() Dim i As Integer Dim j As Integer Dim low As String Dim high As String Dim band As String

i = 1 j = 1 For i = 1 To 50 band = Worksheets("bands 02-602").Cells(i, 1).Value high = Worksheets("bands 02-602").Cells(i, 3).Value low = Worksheets("bands 02-602").Cells(i, 2).Value Worksheets("bands 02-602").Cells(j, 5).Value = "Case " + low + " To " + high Worksheets("bands 02-602").Cells(j + 1, 5).Value = " band = " + band j = j + 2 Next i

End Sub 79

Excel macro used to correlate HITRAN spectral lines with MAS Band configuration

Sub bandStrengths()

Dim band As Integer Dim prevBand As Integer Dim inBand As Integer Dim lines As Double Dim microLine As Double Dim lineStrength As Double Dim bandStrength As Double Dim i As Long i = 1 lines = 0.1 microLine = 0 band = 0 inBand = 0 lineStrength = 0 bandStrength = 0

Do Until lines = 0 lines = Worksheets("GasLines").Cells(i, 1) lineStrength = Worksheets("GasLines").Cells(i, 2) If lines > 0 Then microLine = 10000 / lines Else: If lines <= 0 Then microLine = 0 End If prevBand = band

Worksheets("GasLines").Cells(i, 4) = microLine Select Case microLine Case 0.4514 To 0.4911 band = 1 Case 0.5331 To 0.5749 band = 2 Case 0.6323 To 0.6833 band = 3 Case 0.684 To 0.7267 band = 4 Case 0.725 To 0.768 band = 5 Case 0.8063 To 0.8507 band = 6 Case 0.8493 To 0.892 band = 7 80

Case 0.8907 To 0.9326 band = 8 Case 0.9323 To 0.9737 band = 9 Case 1.593 To 1.6462 band = 10 Case 1.648 To 1.7013 band = 11 Case 1.7011 To 1.7538 band = 12 Case 1.754 To 1.8067 band = 13 Case 1.8073 To 1.8593 band = 14 Case 1.8567 To 1.9109 band = 15 Case 1.9066 To 1.9606 band = 16 Case 1.9569 To 2.0111 band = 17 Case 2.0064 To 2.0621 band = 18 Case 2.0568 To 2.1106 band = 19 Case 2.1058 To 2.1599 band = 20 Case 2.1549 To 2.2079 band = 21 Case 2.2037 To 2.2581 band = 22 Case 2.2541 To 2.3075 band = 23 Case 2.3049 To 2.3572 band = 24 Case 2.3537 To 2.4062 band = 25 Case 3.0442 To 3.2022 band = 26 Case 3.2053 To 3.3551 band = 27 Case 3.3539 To 3.513 band = 28 Case 3.5153 To 3.6726 band = 29 Case 3.674 To 3.8171 band = 30 81

Case 3.831 To 3.9872 band = 31 Case 3.9916 To 4.1466 band = 32 Case 4.1481 To 4.301 band = 33 Case 4.2981 To 4.451 band = 34 Case 4.4655 To 4.6119 band = 35 Case 4.6158 To 4.772 band = 36 Case 4.7766 To 4.925 band = 37 Case 4.9284 To 5.0719 band = 38 Case 5.0808 To 5.2224 band = 39 Case 5.2303 To 5.3666 band = 40 Case 5.346 To 5.4384 band = 41 Case 8.2664 To 8.6973 band = 42 Case 9.439 To 9.9486 band = 43 Case 10.2378 To 10.7006 band = 44 Case 10.74 To 11.2041 band = 45 Case 11.7475 To 12.1724 band = 46 Case 12.6752 To 13.0872 band = 47 Case 13.0409 To 13.4901 band = 48 Case 13.5405 To 14.0977 band = 49 Case 14.0417 To 14.4706 band = 50

Case Else band = 0 End Select

82

Worksheets("GasLines").Cells(i, 6) = band

inBand = band - prevBand If band > 0 Then Select Case inBand Case Is = 0 bandStrength = bandStrength + lineStrength Case Is = band bandStrength = bandStrength + lineStrength Case Else bandStrength = 0 bandStrength = bandStrength + lineStrength End Select Else: If band = 0 Then bandStrength = 0 End If Worksheets("GasLines").Cells(i, 8) = bandStrength

If inBand < 0 Then Worksheets("GasLines").Cells(prevBand, 10) = prevBand Worksheets("GasLines").Cells(prevBand, 11) = Worksheets("GasLines").Cells(i - 1, 8) End If

i = i + 1

Loop

End Sub 83

ABS Values

absorption intensity within each MAS 98-031 band for each individual gas

Gas 1 2 3 4 5 6 7 8 9 10 CH4 2.43E-20 N2 O2 1.23E-26 1.49E-23 2.24E-22 7.89E-27 7.15E-30 H2O 2.32E-24 3.08E-23 1.16E-22 1.13E-21 4.46E-22 1.42E-21 2.5E-23 4.56E-21 2.06E-20 6.02E-24 CO 1.7E-05 CO2 5.41E-22 OH 1.27E-24 7.11E-25 9.41E-22 2.66E-28 2.11E-28 5.82E-21 7.31E-26 HO2 H2CO SO2

Gas 11 12 13 14 15 16 17 18 19 20 CH4 5.55E-20 2.54E-20 1.37E-20 1.54E-21 2.14E-21 N2 O2 H2O 5.01E-23 5.46E-22 1.37E-20 3.5E-19 3.57E-19 1.17E-19 2.87E-21 2.34E-22 7.17E-23 4.9E-23 CO CO2 2.6E-23 5.98E-26 1.2E-24 5.1E-23 6.58E-21 2.59E-20 2.12E-20 4.33E-21 4.63E-24 OH 1.36E-27 1.35E-29 HO2 H2CO SO2

Gas 21 22 23 24 25 26 27 28 29 30 CH4 3.09E-20 8.1E-20 1.99E-19 2.45E-19 2.28E-19 4.67E-19 7.82E-18 2.53E-18 1.67E-19 4.09E-20 N2 O2 H2O 1.11E-22 1.9E-23 4.32E-24 2.8E-22 1.23E-21 2.55E-20 2.46E-20 1.75E-21 8.77E-24 2.19E-25 CO 0.000138 0.0509 0.027219 CO2 2.19E-24 9.94E-26 8.93E-27 1.36E-23 2.51E-25 OH 1.42E-29 1.18E-27 4.2E-26 6.05E-25 1.45E-20 9.1E-23 2.99E-24 7.27E-26 1.06E-26 HO2 1.92E-20 H2CO 4.57E-20 5.75E-18 1.39E-17 1.53E-19 SO2 2.01E-21 1.9E-21

84

ABS Values absorption intensity within each MAS 98-031 band for each individual gas

Gas 31 32 33 34 35 36 37 38 39 40 CH4 2.19E-20 7.59E-21 1.71E-21 1.52E-22 7.29E-24 8.23E-23 N2 1.56E-29 1.17E-27 3.41E-27 1.36E-27 4.86E-28 2.29E-29 O2 H2O 9.45E-25 1.53E-24 8.04E-24 1.12E-22 2.6E-22 1.5E-21 7.66E-21 2.49E-20 9.42E-20 1.27E-19 CO 0.000689 3.811778 4.940904 0.956414 0.005122 1.55E-06 3.72E-10 CO2 5.22E-23 8.59E-17 1.32E-17 1.01E-22 4.28E-22 5.74E-21 1.72E-23 4.01E-22 2.37E-22 OH 1.03E-28 8.37E-30 HO2 H2CO SO2 1.98E-19 3.48E-19

Gas 41 42 43 44 45 46 47 48 49 50 CH4 7.15E-23 1.81E-20 1.48E-23 N2 O2 H2O 1.28E-19 1.17E-21 7.63E-23 5.54E-23 7.1E-23 8.73E-23 3.54E-22 6.96E-22 4.84E-22 2.27E-21 CO CO2 6.15E-24 4.42E-22 4.5E-22 5.7E-23 2.68E-23 6.22E-22 1.46E-20 1.37E-19 4.98E-19 OH 2.68E-29 2.51E-27 2.68E-25 2.59E-25 1.58E-22 2.97E-30 HO2 1.26E-19 1.08E-24 1.63E-24 6.13E-24 3.99E-25 7.88E-24 6.33E-25 H2CO SO2 1.81E-18 2.52E-21

85

ABS ratio values

Meth2 Meth3 Meth4 2827 Sprt CO CO2 HO2nCO2 low OH CH4 0.060 0.192 0.061 0.323 0.002 11.241 4.426 611.246 1 N2 1 1 1 1 1 1.828 0.344 1 1 O2 1 1 1 1 1 0.5 1 1 1 H2O 1.036 0.553 0.967 0.071 54.550 2.609 0.191 8.060 0.813 CO 1 1 1 1 1 0.001 1 1 1 CO2 54.169 27.085 54.169 3.98E-75 2.24E+77 6.483 6.08E-07 0.509 2.127 OH 158.921 79.477 153.871 0.033 46.902 0.5 8.37E+69 53.408 2.59E+74 HO2 1.92E+79 9.62E+78 9.62E+78 1 6.3E+79 0.5 1 7.94E-81 0.661 H2CO 2.19E-80 62.908 2.402 125.817 1 0.5 1 1 1 SO2 1 1 1E+78 1 0.001 0.5 3.48E+80 359.11 1

86

Table for Evaluating RS band ratio based on ABS ratios and Gas concentrations

Target Gas Interference Gas Impact of Interference on Evaluation ABS ratio Concentration ABS ratio Concentration RS band ratio near 0 low near 0 low significant Poor near 0 low near 0 high high Fail near 0 high near 0 low low Good near 0 high near 0 high significant Poor near 0 low between 0-1 low low Good near 0 low between 0-1 high significant Poor near 0 high between 0-1 low very low Excellent near 0 high between 0-1 high low Good near 0 low near 1 low very low Excellent near 0 low near 1 high very low Excellent near 0 high near 1 low very low Excellent near 0 high near 1 high very low Excellent near 0 low greater than 1 low low Good near 0 low greater than 1 high significant Poor near 0 high greater than 1 low very low Excellent near 0 high greater than 1 high low Good near 0 low much greater low high Fail near 0 low much greater high very high Fail near 0 high much greater low significant Poor near 0 high much greater high very high Fail between 0-1 low near 0 low high Fail between 0-1 low near 0 high very high Fail between 0-1 high near 0 low high Fail between 0-1 high near 0 high high Fail between 0-1 low between 0-1 low significant Poor between 0-1 low between 0-1 high high Fail between 0-1 high between 0-1 low significant Poor between 0-1 high between 0-1 high high Fail between 0-1 low near 1 low low Good between 0-1 low near 1 high low Good between 0-1 high near 1 low low Good between 0-1 high near 1 high low Good between 0-1 low greater than 1 low significant Poor between 0-1 low greater than 1 high high Fail between 0-1 high greater than 1 low significant Poor between 0-1 high greater than 1 high high Fail

All other combinations are Failures

87

Equations and mathematics

The BB curve values and plots were generated using:

2hc 2

⎛ hc ⎞ ⎡ ⎜ ⎟ ⎤ λ5 ⎢e⎝ λkT ⎠ −1⎥ ⎣⎢ ⎦⎥ where: h = Planck's constant c = speed of light λ = wavelength k = Boltzmann’s constant T= BB temperature

The same BB curve formula was used to integrate the BB emissions for individual MAS bands.

λi 2 u 2hc ε()()λ τ λ dλ ∫λi ⎛ hc ⎞ l ⎜ ⎟ 5 ⎡ λkT ⎤ λ e⎝ ⎠ −1 ⎣⎢ ⎦⎥

Assume the BB emissivity ε(λ) = 1 The transmission property of the atmosphere τ(λ) is related to what we are trying to evaluate (actually absorption), so τ(λ) is also set to 1. This makes the equation:

i λu dλ 2hc 2ε hc ∫λi ⎛ ⎞ l ⎡ ⎜ ⎟ ⎤ λ5 ⎢e⎝ λkT ⎠ −1⎥ ⎣⎢ ⎦⎥

The above integration results can then incorporated into the ratio algorithms to determine the expected or ideal band ratio values. For example, a two band ratio would look like:

i λu dλ 2hc 2ε hc ∫λi ⎛ ⎞ l ⎡ ⎜ ⎟ ⎤ λ5 ⎢e⎝ λkT ⎠ −1⎥ ⎣⎢ ⎦⎥ i λu dλ 2hc 2ε hc ∫λi ⎛ ⎞ l ⎡ ⎜ ⎟ ⎤ λ5 ⎢e⎝ λkT ⎠ −1⎥ ⎣⎢ ⎦⎥

88

The formula can be simplified by using:

⎛ hc ⎞ ⎛14388 ⎞ ⎜ ⎟ = ⎜ ⎟ ⎝ λkT ⎠ ⎝ λT ⎠

So, for integration purposes, the important part of the equation is:

i λu dλ

14388 ∫λi ⎛ ⎞ l ⎡ ⎜ ⎟ ⎤ λ5 ⎢e⎝ λT ⎠ −1⎥ ⎣⎢ ⎦⎥

Integration for each band at various BB temperatures was calculated using MAPLE9 and the expression:

>evalf(Int(1/((x^5)*(exp(14388/(x*T))-1)), x=λl..λu)); where: T = BB temperature λl = lower band limit λu = upper band limit

The results were incorporated into an excel worksheet in order to determine the band and ratio values with various emission/reflectance relationships.

The 6000K BB curve and band integrations were for solar emission. So, the amount of solar emission energy that reaches the Earth’s surface had to be calculated. The total amount of solar emission can be expressed as:

b 2 φλ (4πRS )

The proportion of solar energy that reaches the Earth is the ratio of the Earths cross-sectional area divided by the spherical distribution area of the total solar energy at the Earth-Sun distance: πR 2 E 4πd 2

89

The amount of solar energy that reaches the Earth is then spread across the half of the Earth surface: 2 ⎛ 4πRE ⎞ ⎜ ⎟ ⎝ 2 ⎠

The complete formula to calculate the potential solar reflectance is :

φ b (4πR 2 )(πR 2 )(0.20) λ S E 2 2 ⎛ 4πRE ⎞ ()4πd ⎜ ⎟ ⎝ 2 ⎠ where: ø = BB emission/m2 RS = radius of the sun RE = Earth radius d = Earth-Sun distance

The (0.20) value is the typical Earth albedo, meaning approximately 20% of the illumination is reflected from the earth, while the other 80% is absorbed and re-emitted at longer wavelengths. The (0.20) value was the variable for the different reflectance calculations.

The equation can be simplified to:

φ b R 2 (0.20) λ S 2d 2

90

Expected ideal band ratio values

MAS 98-031 Emittance Band Ratios 300K 290K 280K 6000K Ratio Є = 1 Є = 1 Є = 1 Є = 1

(26+28)/(27x2) 1.135 1.145 1.156 0.826

(26+29)/(27+28) 1.154 1.172 1.192 0.877

26/27 0.648 0.632 0.614 0.858

28/27 1.622 1.659 1.697 0.795

(42+44)/(43*2) 0.852 0.981

(27+30)/(28+29) 1.004 1.016 1.030 1.059

(25+27)/(26*2) 0.777 0.796 0.818 1.118

1.5(34+38)/(35+36+37) 0.988 1.029

(33+38)/(34+35+36+37) 0.501 0.528

(41+43)/(42*2) 0.658 1.152

32/33 0.793 1.168

45/46 1.165 1.197

300K Emittance for MAS 98-031Band Ratios

no solar with 1% solar Ratio reflection reflection (26+28)/(27x2) 1.14 1.12 (26+29)/(27+28) 1.15 1.14 26/27 0.65 0.76 28/27 1.62 1.49 (42+44)/(43*2) 0.85 0.85 (27+30)/(28+29) 1.00 1.00 (25+27)/(26*2) 0.78 0.79 1.5(34+38)/(35+36+37) 0.99 0.99 (33+38)/(34+35+36+37) 0.50 0.50 (41+43)/(42*2) 0.66 0.66 32/33 0.79 0.80 45/46 1.16 1.16

91

300K Emittance for MAS 97-127Band Ratios

with 20% sun 300K only reflection Ratio Є = 1 Є = 1

(26+28)/(27x2) 1.131 1.080

(26+29)/(27+28) 1.143 1.066

26/27 0.635 1.130

28/27 1.626 1.030

(42+44)/(43*2) 0.682 0.682

(27+30)/(28+29) 1.002

(25+27)/(26*2)

1.5(34+38)/(35+36+37)

(33+38)/(34+35+36+37) 0.489 0.491

(41+43)/(42*2) 0.964 0.965

32/33 0.750 0.821

45/46 1.213 1.213

46/45 0.824476 0.824 92

APPENDIX B

Complete GIS code table

Summary of GIS Raster Calculator Results ratios indicate these gases present at "high" concentrations * * exception is OH

0 none 100 CO 1000 CO2 2 meth2 102 CO meth2 1002 CO2 meth2 3 meth3 103 CO meth3 1003 CO2 meth3 4 meth4 104 CO meth4 1004 CO2 meth4 5 meth2&3 105 CO meth2&3 1005 CO2 meth2&3 6 meth2&4 106 CO meth2&4 1006 CO2 meth2&4 7 meth3&4 107 CO meth3&4 1007 CO2 meth3&4 9 meth2&3&4 109 CO meth2&3&4 1009 CO2 meth2&3&4 10 sprt 110 CO sprt 1010 CO2 sprt 12 sprt meth2 112 CO sprt meth2 1012 CO2 sprt meth2 13 sprt meth3 113 CO sprt meth3 1013 CO2 sprt meth3 14 sprt meth4 114 CO sprt meth4 1014 CO2 sprt meth4 15 sprt meth2&3 115 CO sprt meth2&3 1015 CO2 sprt meth2&3 16 sprt meth2&4 116 CO sprt meth2&4 1016 CO2 sprt meth2&4 17 sprt meth3&4 117 CO sprt meth3&4 1017 CO2 sprt meth3&4 19 sprt meth2&3&4 119 CO sprt meth2&3&4 1019 CO2 sprt meth2&3&4 20 2827 120 CO 2827 1020 CO2 2827 22 2827 meth2 122 CO 2827 meth2 1022 CO2 2827 meth2 23 2827 meth3 123 CO 2827 meth3 1023 CO2 2827 meth3 24 2827 meth4 124 CO 2827 meth4 1024 CO2 2827 meth4 25 2827 meth2&3 125 CO 2827 meth2&3 1025 CO2 2827 meth2&3 26 2827 meth2&4 126 CO 2827 meth2&4 1026 CO2 2827 meth2&4 27 2827 meth3&4 127 CO 2827 meth3&4 1027 CO2 2827 meth3&4 29 2827 meth2&3&4 129 CO 2827 meth2&3&4 1029 CO2 2827 meth2&3&4 30 2827 sprt 130 CO 2827 sprt 1030 CO2 2827 sprt 32 2827 sprt meth2 132 CO 2827 sprt meth2 1032 CO2 2827 sprt meth2 33 2827 sprt meth3 133 CO 2827 sprt meth3 1033 CO2 2827 sprt meth3 34 2827 sprt meth4 134 CO 2827 sprt meth4 1034 CO2 2827 sprt meth4 35 2827 sprt meth2&3 135 CO 2827 sprt meth2&3 1035 CO2 2827 sprt meth2&3 36 2827 sprt meth2&4 136 CO 2827 sprt meth2&4 1036 CO2 2827 sprt meth2&4 37 2827 sprt meth3&4 137 CO 2827 sprt meth3&4 1037 CO2 2827 sprt meth3&4 39 2827 sprt meth2&3&4 139 CO 2827 sprt meth2&3&4 1039 CO2 2827 sprt meth2&3&4 93

1100 CO2 CO 2000 HO2 2100 HO2 CO 1102 CO2 CO meth2 2002 HO2 meth2 2102 HO2 CO meth2 1103 CO2 CO meth3 2003 HO2 meth3 2103 HO2 CO meth3 1104 CO2 CO meth4 2004 HO2 meth4 2104 HO2 CO meth4 1105 CO2 CO meth2&3 2005 HO2 meth2&3 2105 HO2 CO meth2&3 1106 CO2 CO meth2&4 2006 HO2 meth2&4 2106 HO2 CO meth2&4 1107 CO2 CO meth3&4 2007 HO2 meth3&4 2107 HO2 CO meth3&4 1109 CO2 CO meth2&3&4 2009 HO2 meth2&3&4 2109 HO2 CO meth2&3&4 1110 CO2 CO sprt 2010 HO2 sprt 2110 HO2 CO sprt 1112 CO2 CO sprt meth2 2012 HO2 sprt meth2 2112 HO2 CO sprt meth2 1113 CO2 CO sprt meth3 2013 HO2 sprt meth3 2113 HO2 CO sprt meth3 1114 CO2 CO sprt meth4 2014 HO2 sprt meth4 2114 HO2 CO sprt meth4 1115 CO2 CO sprt meth2&3 2015 HO2 sprt meth2&3 2115 HO2 CO sprt meth2&3 1116 CO2 CO sprt meth2&4 2016 HO2 sprt meth2&4 2116 HO2 CO sprt meth2&4 1117 CO2 CO sprt meth3&4 2017 HO2 sprt meth3&4 2117 HO2 CO sprt meth3&4 1119 CO2 CO sprt meth2&3&4 2019 HO2 sprt meth2&3&4 2119 HO2 CO sprt meth2&3&4 1120 CO2 CO 2827 2020 HO2 2827 2120 HO2 CO 2827 1122 CO2 CO 2827 meth2 2022 HO2 2827 meth2 2122 HO2 CO 2827 meth2 1123 CO2 CO 2827 meth3 2023 HO2 2827 meth3 2123 HO2 CO 2827 meth3 1124 CO2 CO 2827 meth4 2024 HO2 2827 meth4 2124 HO2 CO 2827 meth4 1125 CO2 CO 2827 meth2&3 2025 HO2 2827 meth2&3 2125 HO2 CO 2827 meth2&3 1126 CO2 CO 2827 meth2&4 2026 HO2 2827 meth2&4 2126 HO2 CO 2827 meth2&4 1127 CO2 CO 2827 meth3&4 2027 HO2 2827 meth3&4 2127 HO2 CO 2827 meth3&4 1129 CO2 CO 2827 meth2&3&4 2029 HO2 2827 meth2&3&4 2129 HO2 CO 2827 meth2&3&4 1130 CO2 CO 2827 sprt 2030 HO2 2827 sprt 2130 HO2 CO 2827 sprt 1132 CO2 CO 2827 sprt meth2 2032 HO2 2827 sprt meth2 2132 HO2 CO 2827 sprt meth2 1133 CO2 CO 2827 sprt meth3 2033 HO2 2827 sprt meth3 2133 HO2 CO 2827 sprt meth3 1134 CO2 CO 2827 sprt meth4 2034 HO2 2827 sprt meth4 2134 HO2 CO 2827 sprt meth4 1135 CO2 CO 2827 sprt meth2&3 2035 HO2 2827 sprt meth2&3 2135 HO2 CO 2827 sprt meth2&3 1136 CO2 CO 2827 sprt meth2&4 2036 HO2 2827 sprt meth2&4 2136 HO2 CO 2827 sprt meth2&4 1137 CO2 CO 2827 sprt meth3&4 2037 HO2 2827 sprt meth3&4 2137 HO2 CO 2827 sprt meth3&4 1139 CO2 CO 2827 sprt meth2&3&4 2039 HO2 2827 sprt meth2&3&4 2139 HO2 CO 2827 sprt meth2&3&4

94

3000 HO2 CO2 3100 HO2 CO2 CO 10000 OH 3002 HO2 CO2 meth2 3102 HO2 CO2 CO meth2 10002 OH meth2 3003 HO2 CO2 meth3 3103 HO2 CO2 CO meth3 10003 OH meth3 3004 HO2 CO2 meth4 3104 HO2 CO2 CO meth4 10004 OH meth4 3005 HO2 CO2 meth2&3 3105 HO2 CO2 CO meth2&3 10005 OH meth2&3 3006 HO2 CO2 meth2&4 3106 HO2 CO2 CO meth2&4 10006 OH meth2&4 3007 HO2 CO2 meth3&4 3107 HO2 CO2 CO meth3&4 10007 OH meth3&4 3009 HO2 CO2 meth2&3&4 3109 HO2 CO2 CO meth2&3&4 10009 OH meth2&3&4 3010 HO2 CO2 sprt 3110 HO2 CO2 CO sprt 10010 OH sprt 3012 HO2 CO2 sprt meth2 3112 HO2 CO2 CO sprt meth2 10012 OH sprt meth2 3013 HO2 CO2 sprt meth3 3113 HO2 CO2 CO sprt meth3 10013 OH sprt meth3 3014 HO2 CO2 sprt meth4 3114 HO2 CO2 CO sprt meth4 10014 OH sprt meth4 3015 HO2 CO2 sprt meth2&3 3115 HO2 CO2 CO sprt meth2&3 10015 OH sprt meth2&3 3016 HO2 CO2 sprt meth2&4 3116 HO2 CO2 CO sprt meth2&4 10016 OH sprt meth2&4 3017 HO2 CO2 sprt meth3&4 3117 HO2 CO2 CO sprt meth3&4 10017 OH sprt meth3&4 3019 HO2 CO2 sprt meth2&3&4 3119 HO2 CO2 CO sprt meth2&3&4 10019 OH sprt meth2&3&4 3020 HO2 CO2 2827 3120 HO2 CO2 CO 2827 10020 OH 2827 3022 HO2 CO2 2827 meth2 3122 HO2 CO2 CO 2827 meth2 10022 OH 2827 meth2 3023 HO2 CO2 2827 meth3 3123 HO2 CO2 CO 2827 meth3 10023 OH 2827 meth3 3024 HO2 CO2 2827 meth4 3124 HO2 CO2 CO 2827 meth4 10024 OH 2827 meth4 3025 HO2 CO2 2827 meth2&3 3125 HO2 CO2 CO 2827 meth2&3 10025 OH 2827 meth2&3 3026 HO2 CO2 2827 meth2&4 3126 HO2 CO2 CO 2827 meth2&4 10026 OH 2827 meth2&4 3027 HO2 CO2 2827 meth3&4 3127 HO2 CO2 CO 2827 meth3&4 10027 OH 2827 meth3&4 3029 HO2 CO2 2827 meth2&3&4 3129 HO2 CO2 CO 2827 meth2&3&4 10029 OH 2827 meth2&3&4 3030 HO2 CO2 2827 sprt 3130 HO2 CO2 CO 2827 sprt 10030 OH 2827 sprt 3032 HO2 CO2 2827 sprt meth2 3132 HO2 CO2 CO 2827 sprt meth2 10032 OH 2827 sprt meth2 3033 HO2 CO2 2827 sprt meth3 3133 HO2 CO2 CO 2827 sprt meth3 10033 OH 2827 sprt meth3 3034 HO2 CO2 2827 sprt meth4 3134 HO2 CO2 CO 2827 sprt meth4 10034 OH 2827 sprt meth4 3035 HO2 CO2 2827 sprt meth2&3 3135 HO2 CO2 CO 2827 sprt meth2&3 10035 OH 2827 sprt meth2&3 3036 HO2 CO2 2827 sprt meth2&4 3136 HO2 CO2 CO 2827 sprt meth2&4 10036 OH 2827 sprt meth2&4 3037 HO2 CO2 2827 sprt meth3&4 3137 HO2 CO2 CO 2827 sprt meth3&4 10037 OH 2827 sprt meth3&4 3039 HO2 CO2 2827 sprt meth2&3&4 3139 HO2 CO2 CO 2827 sprt meth2&3&4 10039 OH 2827 sprt meth2&3&4

95

10100 OH CO 11000 OH CO2 11100 OH CO2 CO 10102 OH CO meth2 11002 OH CO2 meth2 11102 OH CO2 CO meth2 10103 OH CO meth3 11003 OH CO2 meth3 11103 OH CO2 CO meth3 10104 OH CO meth4 11004 OH CO2 meth4 11104 OH CO2 CO meth4 10105 OH CO meth2&3 11005 OH CO2 meth2&3 11105 OH CO2 CO meth2&3 10106 OH CO meth2&4 11006 OH CO2 meth2&4 11106 OH CO2 CO meth2&4 10107 OH CO meth3&4 11007 OH CO2 meth3&4 11107 OH CO2 CO meth3&4 10109 OH CO meth2&3&4 11009 OH CO2 meth2&3&4 11109 OH CO2 CO meth2&3&4 10110 OH CO sprt 11010 OH CO2 sprt 11110 OH CO2 CO sprt 10112 OH CO sprt meth2 11012 OH CO2 sprt meth2 11112 OH CO2 CO sprt meth2 10113 OH CO sprt meth3 11013 OH CO2 sprt meth3 11113 OH CO2 CO sprt meth3 10114 OH CO sprt meth4 11014 OH CO2 sprt meth4 11114 OH CO2 CO sprt meth4 10115 OH CO sprt meth2&3 11015 OH CO2 sprt meth2&3 11115 OH CO2 CO sprt meth2&3 10116 OH CO sprt meth2&4 11016 OH CO2 sprt meth2&4 11116 OH CO2 CO sprt meth2&4 10117 OH CO sprt meth3&4 11017 OH CO2 sprt meth3&4 11117 OH CO2 CO sprt meth3&4 10119 OH CO sprt meth2&3&4 11019 OH CO2 sprt meth2&3&4 11119 OH CO2 CO sprt meth2&3&4 10120 OH CO 2827 11020 OH CO2 2827 11120 OH CO2 CO 2827 10122 OH CO 2827 meth2 11022 OH CO2 2827 meth2 11122 OH CO2 CO 2827 meth2 10123 OH CO 2827 meth3 11023 OH CO2 2827 meth3 11123 OH CO2 CO 2827 meth3 10124 OH CO 2827 meth4 11024 OH CO2 2827 meth4 11124 OH CO2 CO 2827 meth4 10125 OH CO 2827 meth2&3 11025 OH CO2 2827 meth2&3 11125 OH CO2 CO 2827 meth2&3 10126 OH CO 2827 meth2&4 11026 OH CO2 2827 meth2&4 11126 OH CO2 CO 2827 meth2&4 10127 OH CO 2827 meth3&4 11027 OH CO2 2827 meth3&4 11127 OH CO2 CO 2827 meth3&4 10129 OH CO 2827 meth2&3&4 11029 OH CO2 2827 meth2&3&4 11129 OH CO2 CO 2827 meth2&3&4 10130 OH CO 2827 sprt 11030 OH CO2 2827 sprt 11130 OH CO2 CO 2827 sprt 10132 OH CO 2827 sprt meth2 11032 OH CO2 2827 sprt meth2 11132 OH CO2 CO 2827 sprt meth2 10133 OH CO 2827 sprt meth3 11033 OH CO2 2827 sprt meth3 11133 OH CO2 CO 2827 sprt meth3 10134 OH CO 2827 sprt meth4 11034 OH CO2 2827 sprt meth4 11134 OH CO2 CO 2827 sprt meth4 10135 OH CO 2827 sprt meth2&3 11035 OH CO2 2827 sprt meth2&3 11135 OH CO2 CO 2827 sprt meth2&3 10136 OH CO 2827 sprt meth2&4 11036 OH CO2 2827 sprt meth2&4 11136 OH CO2 CO 2827 sprt meth2&4 10137 OH CO 2827 sprt meth3&4 11037 OH CO2 2827 sprt meth3&4 11137 OH CO2 CO 2827 sprt meth3&4 10139 OH CO 2827 sprt meth2&3&4 11039 OH CO2 2827 sprt meth2&3&4 11139 OH CO2 CO 2827 sprt meth2&3&4

96

12000 OH HO2 12100 OH HO2 CO 13000 OH HO2 CO2 12002 OH HO2 meth2 12102 OH HO2 CO meth2 13002 OH HO2 CO2 meth2 12003 OH HO2 meth3 12103 OH HO2 CO meth3 13003 OH HO2 CO2 meth3 12004 OH HO2 meth4 12104 OH HO2 CO meth4 13004 OH HO2 CO2 meth4 12005 OH HO2 meth2&3 12105 OH HO2 CO meth2&3 13005 OH HO2 CO2 meth2&3 12006 OH HO2 meth2&4 12106 OH HO2 CO meth2&4 13006 OH HO2 CO2 meth2&4 12007 OH HO2 meth3&4 12107 OH HO2 CO meth3&4 13007 OH HO2 CO2 meth3&4 12009 OH HO2 meth2&3&4 12109 OH HO2 CO meth2&3&4 13009 OH HO2 CO2 meth2&3&4 12010 OH HO2 sprt 12110 OH HO2 CO sprt 13010 OH HO2 CO2 sprt 12012 OH HO2 sprt meth2 12112 OH HO2 CO sprt meth2 13012 OH HO2 CO2 sprt meth2 12013 OH HO2 sprt meth3 12113 OH HO2 CO sprt meth3 13013 OH HO2 CO2 sprt meth3 12014 OH HO2 sprt meth4 12114 OH HO2 CO sprt meth4 13014 OH HO2 CO2 sprt meth4 12015 OH HO2 sprt meth2&3 12115 OH HO2 CO sprt meth2&3 13015 OH HO2 CO2 sprt meth2&3 12016 OH HO2 sprt meth2&4 12116 OH HO2 CO sprt meth2&4 13016 OH HO2 CO2 sprt meth2&4 12017 OH HO2 sprt meth3&4 12117 OH HO2 CO sprt meth3&4 13017 OH HO2 CO2 sprt meth3&4 12019 OH HO2 sprt meth2&3&4 12119 OH HO2 CO sprt meth2&3&4 13019 OH HO2 CO2 sprt meth2&3&4 12020 OH HO2 2827 12120 OH HO2 CO 2827 13020 OH HO2 CO2 2827 12022 OH HO2 2827 meth2 12122 OH HO2 CO 2827 meth2 13022 OH HO2 CO2 2827 meth2 12023 OH HO2 2827 meth3 12123 OH HO2 CO 2827 meth3 13023 OH HO2 CO2 2827 meth3 12024 OH HO2 2827 meth4 12124 OH HO2 CO 2827 meth4 13024 OH HO2 CO2 2827 meth4 12025 OH HO2 2827 meth2&3 12125 OH HO2 CO 2827 meth2&3 13025 OH HO2 CO2 2827 meth2&3 12026 OH HO2 2827 meth2&4 12126 OH HO2 CO 2827 meth2&4 13026 OH HO2 CO2 2827 meth2&4 12027 OH HO2 2827 meth3&4 12127 OH HO2 CO 2827 meth3&4 13027 OH HO2 CO2 2827 meth3&4 12029 OH HO2 2827 meth2&3&4 12129 OH HO2 CO 2827 meth2&3&4 13029 OH HO2 CO2 2827 meth2&3&4 12030 OH HO2 2827 sprt 12130 OH HO2 CO 2827 sprt 13030 OH HO2 CO2 2827 sprt 12032 OH HO2 2827 sprt meth2 12132 OH HO2 CO 2827 sprt meth2 13032 OH HO2 CO2 2827 sprt meth2 12033 OH HO2 2827 sprt meth3 12133 OH HO2 CO 2827 sprt meth3 13033 OH HO2 CO2 2827 sprt meth3 12034 OH HO2 2827 sprt meth4 12134 OH HO2 CO 2827 sprt meth4 13034 OH HO2 CO2 2827 sprt meth4 12035 OH HO2 2827 sprt meth2&3 12135 OH HO2 CO 2827 sprt meth2&3 13035 OH HO2 CO2 2827 sprt meth2&3 12036 OH HO2 2827 sprt meth2&4 12136 OH HO2 CO 2827 sprt meth2&4 13036 OH HO2 CO2 2827 sprt meth2&4 12037 OH HO2 2827 sprt meth3&4 12137 OH HO2 CO 2827 sprt meth3&4 13037 OH HO2 CO2 2827 sprt meth3&4 12039 OH HO2 2827 sprt meth2&3&4 12139 OH HO2 CO 2827 sprt meth2&3&4 13039 OH HO2 CO2 2827 sprt meth2&3&4

97

13100 OH HO2 CO2 CO 13102 OH HO2 CO2 CO meth2 13103 OH HO2 CO2 CO meth3 13104 OH HO2 CO2 CO meth4 13105 OH HO2 CO2 CO meth2&3 13106 OH HO2 CO2 CO meth2&4 13107 OH HO2 CO2 CO meth3&4 13109 OH HO2 CO2 CO meth2&3&4 13110 OH HO2 CO2 CO sprt 13112 OH HO2 CO2 CO sprt meth2 13113 OH HO2 CO2 CO sprt meth3 13114 OH HO2 CO2 CO sprt meth4 13115 OH HO2 CO2 CO sprt meth2&3 13116 OH HO2 CO2 CO sprt meth2&4 13117 OH HO2 CO2 CO sprt meth3&4 13119 OH HO2 CO2 CO sprt meth2&3&4 13120 OH HO2 CO2 CO 2827 13122 OH HO2 CO2 CO 2827 meth2 13123 OH HO2 CO2 CO 2827 meth3 13124 OH HO2 CO2 CO 2827 meth4 13125 OH HO2 CO2 CO 2827 meth2&3 13126 OH HO2 CO2 CO 2827 meth2&4 13127 OH HO2 CO2 CO 2827 meth3&4 13129 OH HO2 CO2 CO 2827 meth2&3&4 13130 OH HO2 CO2 CO 2827 sprt 13132 OH HO2 CO2 CO 2827 sprt meth2 13133 OH HO2 CO2 CO 2827 sprt meth3 13134 OH HO2 CO2 CO 2827 sprt meth4 13135 OH HO2 CO2 CO 2827 sprt meth2&3 13136 OH HO2 CO2 CO 2827 sprt meth2&4 13137 OH HO2 CO2 CO 2827 sprt meth3&4 13139 OH HO2 CO2 CO 2827 sprt meth2&3&4 98

APPENDIX C

California off-shore mission images and results

MAS 98031_01

MAS 98031_02

MAS 98031_03

MAS 98031_04

MAS 98031_05

MAS 98031_06

California inland mission images and results

MAS 97127_02

MAS 97127_08

Toledo, Ohio area mission images and results

MAS 96144_13

Louisiana coast mission images and results

MAS 03915_5

MAS 03915_7

MAS 03915_9

99

California 98031_01

True Color Meth2 RGB (3,2,1) 26/27 28/27

100

California 98031_01

True Color Meth2 RGB (3,2,1) 26/27 28/27 101

California 98031_01

Meth2 Meth3 Meth4 26/27 (26+28)/(27*2) (26+29)/(27+28) 102

California 98031_01

Support CO CO2 (41+43)/(42*2) (33+38)/(34+35+36+37) 32/33 103

California 98031_01

HO2nCO2 lowOH (42+44)/(43*2) 45/46

104

California 98031_02

True Color RGB (3,2,1) Color Stretched

26/27 28/27 105

California 98031_02

True Color RGB (3,2,1) Color Stretched

26/27 28/27 106

California 98031_02

Meth2 (26/27) Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28) Support (41+43)/(42*2) 107

California 98031_02

CO (33+38)/(34+35+36+37) CO2 (32/33)

HO2nCO2 (42+44)/(43*2) lowOH 45/46

108

California 98031_03

True Color RGB (3,2,1) Color Stretched

26/27 28/27 109

California 98031_03

Meth2 (26/27) Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28) Support (41+43)/(42*2) 110

California 98031_03

CO (33+38)/(34+35+36+37) CO2 (32/33)

HO2nCO2 (42+44)/(43*2) lowOH 45/46 111

California 98031_04

True Color Meth2 2827 RGB (3,2,1) (26/27) (28/27)

112

California 98031_04

Meth2 Meth3 Meth4 (26/27) (26+28)/(27*2) (26+29)/(27+28)

113

California 98031_04

Support CO CO2 (41+43)/(42*2) (33+38)/(34+35+36+37) (32/33)

114

California 98031_04

HO2nCO2 lowOH (42+44)/(43*2) (45/46)

115

California 98031_05

True Color RGB(3,2,1) Stretched Color

Meth2 (26/27) 2827 (28/27) 116

California 98031_05

Meth2 (26/27) Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28) Support (41+43)/(42*2) 117

California 98031_05

CO (33+38)/(34+35+36+37) CO2 (32/33)

HO2nCO2 (42+44)/(43*2) lowOH (45/46) 118

California 98031_06

True Color RGB (3,2,1) Stretched Color

119

California 98031_06

Meth2 (26/27) 2827 (28/27)

120

California 98031_06

Meth3 (26+28)/(27*2) Meth4 (26+29)/(27+28)

121

California 98031_06

Support (41+43)/(42*2) CO (33+38)/(34+35+36+37)

122

California 98031_06

CO2 (32/33) HO2nCO2 (42+44)/(43*2)

123

California 98031_06

lowOH (45/46)

124

California 97127_02

True Color Meth2 Meth3 Meth4 Support 2827 RGB (3,2,1) (26/27) (26+28)/(27*2) (26+29)/(27+28) (41+43)/(42*2) (28/27)

125

California 97127_02

CO CO2 HO2nCO2 lowOH highOH (33+38)/(34+35+36+37) (32/33) (42+44)/(43*2) (45/46) (46/45)

126

California 97127_08

True Color Meth2 Meth3 Meth4 Support 2827 RGB (3,2,1) (26/27) (26+28)/(27*2) (26+29)/(27+28) (41+43)/(42*2) (28/27)

127

California 97127_08

CO CO2 HO2nCO2 lowOH highOH (33+38)/(34+35+36+37) (32/33) (42+44)/(43*2) (45/46) (46/45)

128

Ohio – Toledo area 96144_13

True Color RGB (3,2,1)

Meth2 (26/27) 129

Ohio – Toledo area 96144_13

Meth3 (26+28)/(27*2)

Meth4 (26+29)/(27+28)

130

Louisiana Coast 03915_3

True Color Meth2 Meth3 Meth4 RGB (3,2,1) 26/27 (26+28)/(27*2) (26+29)/(27+28)

131

Louisiana Coast 03915_5

True Color Meth2 Meth3 Meth4 RGB (3,2,1) (26/27) (26+28)/(27*2) (26+29)/(27+28)

132

Louisiana Coast 03915_7

True Color Meth2 Meth3 Meth4 RGB (3,2,1) (26/27) (26+28)/(27*2) (26+29)/(27+28)