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NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-Spectral-Resolution Near-Infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 0.5. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

Fluorescence↗
NASA NTRS2014

How Accurate is Land/Ocean Moisture Transport Variability in Reanalyses?

Quantifying the global hydrological cycle and its variability across various time scales remains a challenge to the climate community. Direct measurements of evaporation (E), evapotranspiration (ET), and precipitation (P) are not feasible on a global scale, nor is the transport of water vapor over the global oceans and sparsely populated land areas. Expanding satellite data streams have enabled development of various water (and energy) flux products, complementing reanalyses and facilitating observationally constrained modeling. But the evolution of the global observing system has produced additional complications--improvements in satellite sensor resolution and accuracy have resulted in "epochs" of observational quasi-uniformity that can adversely affect reanalysis trends. In this work we focus on vertically integrated moisture flux convergence (VMFC) variations within the period 1979 - present integrated over global land. We show that VMFC in recent reanalyses (e.g. ERA-I, NASA MERRA, NOAA CFSR and JRA55) suffers from observing system changes, though differently in each product. Land Surface Models (LSMs) forced with observations-based precipitation, radiation and near-surface meteorology share closely the interannual P-ET variations of the reanalyses associated with ENSO events. (VMFC over land and P-ET estimates are equivalent quantities since atmospheric storage changes are small on these scales.) But the long-term LSM trend over the period since 1979 is approximately one-fourth that of the reanalyses. Additional reduced observation reanalyses assimilating only surface pressure and /or specifying seasurface temperature also have a much smaller trend in P-ET like the LSMs. We explore the regional manifestation of the reanalysis P-ET / VMFC problems, particularly over land. Both principal component analysis and a simple time series changepoint analysis highlight problems associated with data poor regions such as Equatorial Africa and, for one reanalysis, the Equatorial Andes region. Onset of the availability of passive microwave Special Sensor Microwave Imager (SSMI) moisture data in July 1987 and the transition from the Microwave Sounder Unit (MSU) to an advanced version (AMSU) have significant impacts on VMFC variability. Simple accounting for these errors of leading importance results in modified reanalysis VMFC estimates that agree much better with the LSM results. Regional details of the modified reanalysis VMFC and LSM P-ET are related to changes in Pacific Decadal Variability as manifest in SST changes after the late 1990s.

Robertson, F. R.↗
NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-spectral-resolution Near-infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 deg × 0.5 deg. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

remote sensing↗
NASA NTRS2014

Nondestructive Evaluation (NDE) for Inspection of Composite Sandwich Structures

Composite honeycomb structures are widely used in aerospace applications due to their low weight and high strength advantages. Developing nondestructive evaluation (NDE) inspection methods are essential for their safe performance. Flash thermography is a commonly used technique for composite honeycomb structure inspections due to its large area and rapid inspection capability. Flash thermography is shown to be sensitive for detection of face sheet impact damage and face sheet to core disbond. Data processing techniques, using principal component analysis to improve the defect contrast, are discussed. Limitations to the thermal detection of the core are investigated. In addition to flash thermography, X-ray computed tomography is used. The aluminum honeycomb core provides excellent X-ray contrast compared to the composite face sheet. The X-ray CT technique was used to detect impact damage, core crushing, and skin to core disbonds. Additionally, the X-ray CT technique is used to validate the thermography results.

Zalameda, Joseph N.↗
NASA NTRS2014

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Meng, Huan↗
NASA NTRS2015

NPP ATMS Snowfall Rate Product

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH‐Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

Forecasting↗
NASA NTRS2015

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗
NASA NTRS2011

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

Adaboost↗
NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗
NASA NTRS2010

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗
NASA NTRS2014

Aura OMI Observations of Global SO2 and NO2 Pollution from 2005 to 2013

The Ozone Monitoring Instrument (OMI), a NASA partnership with the Netherlands and Finland, flies on the NASA Aura satellite and uses reflected sunlight to measure the two critical atmospheric trace gases: nitrogen dioxide (NO2) and sulfur dioxide (SO2) characterizing daily air quality. Both gases and the secondary pollutants they produce (particulate matter, PM2.5, and tropospheric ozone) are USEPA designated criteria pollutants, posing serious threats to human health and the environment (e.g., acid rain, plant damage and reduced visibility). Our group at NASA GSFC has developed and maintained OMI standard SO2 and NO2 data products. We have recently released an updated version of the standard NO2 L2 and L3 products (SP v2.1) and continue improving the algorithm. We are currently in the process of releasing next generation pollution SO2 product, based on an innovative Principal Component Analysis (PCA) algorithm, which greatly reduces the noise and biases. These new standard products provide valuable datasets for studying anthropogenic pollution on local to global scales. Here we highlight some of the OMI observed changes in air quality over several regions. Over the US average NO2 and SO2 pollution levels had decreased dramatically as a result of both technological improvements (e.g., catalytic converters on cars) and stricter regulations of emissions. We see continued decline in pollution over Europe. Over China OMI observed an increase of about 60 percent in NO2 pollution between 2005 and 2013, despite a temporal reversal of the growing trend due to both 2008 Olympic Games and the economic recession in 2009. Chinese SO2 pollution seems to have stabilized since peaking in 2007, probably due to government efforts to curb SO2 emissions from the power sector. We have also observed large increases in both SO2 and NO2 pollution particularly in Eastern India where a number of large new coal power plants had been built in recent years. We expect that further improvements in the OMI NO2 and SO2 products will allow more robust quantification of long-term trends in global air quality.

SO2↗
NASA NTRS2014

Profiling the SO2 Plume from Volcan Turrialba: Ticosonde Balloon Measurements Compared with OMI and OMPS Retrievals

The summit of Volcan Turrialba (elev. 3340 m) lies less than 50 km upstream in the prevailing easterlies from the Ticosonde balloon launch site at San Jose, Costa Rica, where ECC ozone sondes have been launched regularly since 2005. In 2006 we began to see telltale notches in the ozone profiles in the altitude range between 2 and 6 km. Given the proximity of Turrialba, it seemed likely that SO2 in the volcano's plume was interfering in the chemical reaction in the ECC ozone sonde used to detect ozone. In early 2010, fumarolic activity in the Turrialba crater increased strongly, and the profile notches in our soundings increased in frequency as well, consistent with this hypothesis. In February 2012 we tested a dual ECC sonde system, where an additional sonde is flown on the same payload using a selective SO2 filter. The difference of the measurements in the dual sonde is a direct measure of the amount of SO2 encountered. This first dual sonde passed through the plume, and the data indicated a tropospheric SO2 column of 1.4 DU, comparing favorably with a total column of 1.7 DU in the OMI 3-km linear fit (LF) product at the sonde profile location and at nearly the same time. We are now launching dual sondes on a regular basis with 18 launches in the first 12 months through July 2014; 11 of these have detectable SO2 signals. These soundings have great potential for validation of the Aura OMI and the Suomi-NPP OMPS retrievals of SO2. Here we present the sonde measurements and compare them with two satellite datasets: the Aura OMI Linear Fit (LF) product and the Suomi-NPP OMPS Principal Components Analysis (PCA) boundary layer product. The PCA algorithm reduces retrieval noise and artifacts by more accurately accounting for various interferences in SO2 retrievals such as O3 absorption and rotational Raman scattering. The comparisons with the in situ observations indicate a significant improvement of the PCA algorithm in capturing relatively weak volcanic SO2 signals.

sondes↗
NASA NTRS2015

The Legacy of Episodic Climatic Events in Shaping Temperate, Broadleaf Forests

In humid, broadleaf-dominated forests where gap dynamics and partial canopy mortality appears to dominate the disturbance regime at local scales, paleoecological evidence shows alteration at regional-scales associated with climatic change. Yet, little evidence of these broad-scale events exists in extant forests. To evaluate the potential for the occurrence of large-scale disturbance, we used 76 tree-ring collections spanning approx. 840 000 sq km and 5327 tree recruitment dates spanning approx. 1.4 million sq km across the humid eastern United States. Rotated principal component analysis indicated a common growth pattern of a simultaneous reduction in competition in 22 populations across 61 000 km2. Growth-release analysis of these populations reveals an intense and coherent canopy disturbance from 1775 to 1780, peaking in 1776. The resulting time series of canopy disturbance is so poorly described by a Gaussian distribution that it can be described as ''heavy tailed,'' with most of the years from 1775 to 1780 comprising the heavy-tail portion of the distribution. Historical documents provide no evidence that hurricanes or ice storms triggered the 1775-1780 event. Instead, we identify a significant relationship between prior drought and years with elevated rates of disturbance with an intense drought occurring from 1772 to 1775. We further find that years with high rates of canopy disturbance have a propensity to create larger canopy gaps indicating repeated opportunities for rapid change in species composition beyond the landscape scale. Evidence of elevated, regional-scale disturbance reveals how rare events can potentially alter system trajectory: a substantial portion of old-growth forests examined here originated or were substantially altered more than two centuries ago following events lasting just a few years. Our recruitment data, comprised of at least 21 species and several shade-intolerant species, document a pulse of tree recruitment at the subcontinental scale during the late-1600s suggesting that this event was severe enough to open large canopy gaps. These disturbances and their climatic drivers support the hypothesis that punctuated, episodic, climatic events impart a legacy in broadleaf-dominated forests centuries after their occurrence. Given projections of future drought, these results also reveal the potential for abrupt, meso- to large-scale forest change in broadleaf-dominated forests over future decades.

Climatology↗
NASA NTRS2015

Non-destructive Evaluation of Bonds Between Fiberglass Composite and Metal

To assess the integrity and reliability of an adhesive joint in an automotive composite component, several non-destructive evaluation (NDE) methodologies are correlated to lap shear bond strengths. A glass-fabric-reinforced composite structure was bonded to a metallic structure with a two-part epoxy adhesive. Samples were subsequently cut and tested in shear, and flaws were found in some areas. This study aims to develop a reliable and portable NDE system for service-level adhesive inspection in the automotive industry. The results of the experimental investigation using several NDE methods are presented and discussed. Fiberglass-to-metal bonding is the ideal configuration for NDE via thermography using excitation with induction heating, due to the conductive metal and non-conductive glass-fiber-reinforced composites. Excitation can be either by a research-grade induction heater of highly defined frequency and intensity, or by a service-level heater, such as would be used for sealing windshields in a body shop. The thermographs thus produced can be captured via a high-resolution infrared camera, with principal component analysis and 2D spatial Laplacian processing. Alternatively, the thermographs can be captured by low resolution thermochromic microencapsulated liquid crystal film imaging, which needs no post-processing and can be very inexpensive. These samples were also examined with phased-array ultrasound. The NDE methods are compared to the lap shear values and to each other for approximate cost, accuracy, and time and level of expertise needed.

Zhao, Selina↗
NASA NTRS2016

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

model parameterisation↗
NASA NTRS2016

A Global Catalogue of Large SO2 Sources and Emissions Derived from the Ozone Monitoring Instrument

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr(exp -1) to more than 4000 kt yr(exp -1) of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005- 2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30% of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80% over the 2005-2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr(exp -1) and not detected by OMI.

Emissions↗
NASA NTRS2017

New-Generation NASA Aura Ozone Monitoring Instrument (OMI) Volcanic SO2 Dataset: Algorithm Description, Initial Results, and Continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50% reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (approximately 1700 kt total SO2/ and Sierra Negra in 2005 (greater than 1100DU maximum SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

OMI↗
NASA NTRS2016

Physical State and Distribution of Materials at the Surface of Pluto from New Horizons LEISA Imaging Spectrometer

From Earth based observations Pluto is known to be the host of N2, CH4 and CO ices and also a dark red material. Very limited spatial distribution information is available from rotational visible and near-infrared spectral curves obtained from hemispheric measurements. In July 2015 the New Horizons spacecraft reached Pluto and its satellite system and recorded a large set of data. The LEISA spectro-imager of the RALPH instruments are dedicated to the study of the composition and physical state of the materials composing the surface. In this paper we report a study of the distribution and physical state of the ices and non-ice materials on Pluto's illuminated surface and their mode and degree of mixing. Principal Component analysis as well as various specific spectral indicators and correlation plots are used on the first set of 2 high resolution spectro-images from the LEISA instrument covering the whole illuminated face of Pluto at the time of the New Horizons encounter. Qualitative distribution maps have been obtained for the 4 main condensed molecules, N2, CH4, CO, H2O as well as for the visible-dark red material. Based on specific spectral indicators, using either the strength or the position of absorption bands, these 4 molecules are found to indicate the presence of 3 different types of ices: N2-rich:CH4:CO ices, CH4-rich(:CO:N2?) ices and H2O ice. The mixing lines between these ices and with the dark red material are studied using scatter plots between the various spectral indicators. CH4 is mixed at the molecular level with N2, most probably also with CO, thus forming a ternary molecular mixture that follows its phase diagram with low solubility limits. The occurrence of a N2-rich - CH4-rich ices mixing line associated with a progressive decrease of the CO/CH4 ratio tells us that a fractionation sublimation sequence transforms one type of ice to the other forming either a N2-rich - CH4-rich binary mixture at the surface or an upper CH4-rich ice crust that may hide the N2-rich ice below. The strong CH4-rich - H2O mixing line witnesses the subsequent sublimation of the CH4-rich ice lag left behind by the N2:CO sublimation (N spring-summer), or a direct condensation of CH4 ice on the cold H2O ice (S autumn). The weak mixing line between CH4-containing ices and the dark red material and the very sharp spatial transitions between these ices and this non-volatile material are probably due to thermal incompatibility. Finally the occurrence of a H2O ice - red material mixing line advocates for a spatial mixing of the red material covering H2O ice, with possibly a small amount intimately mixed in water ice. From this analysis of the different materials distribution and their relative mixing lines, H2O ice appears to be the substratum on which other ices condense or non-volatile organic material is deposited from the atmosphere. N2-rich ices seem to evolve to CH4-dominated ices, possibly still containing traces of CO and N2, as N2 and CO sublimate away. The spatial distribution of these materials is very complex. The high spatial definition of all these composition maps, as well as those at even higher resolution that will be soon available, will allow us to compare them with Pluto's geologic features observed by LORRI panchromatic and MVIC multispectral imagers to better understand the geophysical processes in action at the surface of this astonishingly active frozen world.

Schmitt, B.↗