Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The MODIS-like VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the dark-target algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012-31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04-0.06 and the correlation of 0.83-0.86, with the largest MB (0.10-0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13-0.14 and a higher correlation (0.93-0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02-0.04 for fine mode, 0.06-0.12 for coarse mode and 0.03-0.05 for bi-mode at 440nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g. surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models

Evaluation of aerosol optical depth and aerosol models from VIIRS retrieval algorithms over North China Plain

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The “MODIS-like” VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the “dark-target” algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012 – 31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04-0.06 and the correlation of 0.83-0.86, with the largest MB (0.10-0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13-0.14 and a higher correlation (0.93-0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02-0.04 for fine mode, 0.06-0.12 for coarse mode and 0.03-0.05 for bi-mode at 440nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g. surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models

Downscaling Aerosol Optical Thickness from Satellite Observations: Physics and Machine Learning Approaches

In recent years, the satellite observation of aerosol properties has been greatly improved. As a result, the derivation of Aerosol Optical Thickness (AOT), one of the most popular atmospheric parameters used in air pollution monitoring, over ocean and continents from satellite observations shows comparable quality to ground-based measurements. Satellite AOT products is often applied for monitoring at global scale because of its coarse spatial resolution. However, monitoring at local scale such as over cities requires more detailed AOT information. The increase spatial resolution to suitable level has potential for applications of air pollution monitoring at global-to-local scale, detecting emission sources, deciding pollution management strategies, localizing aerosol estimation, etc. In this thesis, we investigated, proposed, implemented and validated algorithms to derive AOT maps with spatial resolution increased up to 1×1 km2 from MODerate resolution Imaging Spectrometer (MODIS) observations provided by National Aeronautics and Space Administration (NASA), while MODIS standard aerosol products provide maps at 10×10 km2 of spatial resolution. The solutions are considered on two perspectives: dynamical downscaling by improving the algorithm for remote sensing of tropospheric aerosol from MODIS and statistical downscaling using Support Vector Regression

Development and verification of the NASA Multi-Angle Imager for Aerosols operational cloud mask

The National Aeronautics and Space Administration (NASA) Multi-Angle Imager for Aerosols (MAIA) instrument is set to launch in 2022 with the mission of quantifying the epidemiological relationships between aerosols and human health. The MAIA instrument's primary product is a level 2 aerosol particulate matter concentration measurement collected over cloud-free pixels. The quality of this product heavily depends on the validity of the cloud mask. In this project, we present a cloud masking algorithm for MAIA constrained to its hardware. It consists of 7 observables that are tested against predetermined static thresholds. Both observables and thresholds are a function of scene type, which is a unique combination of sun-view geometry, day of year and surface type, including a novel surface classification scheme derived from the Multi-Angle Implementation of Atmospheric Correction Bi-Directional Reflectance Distribution Function (MAIAC BRDF) data set. The cloud mask algorithm works by checking if an observation exceeds or falls short of a threshold for any of the 7 observables, resulting in a cloudy or clear classification. The thresholds are derived to match the performance of the Terra Moderate Resolution Imaging Spectro-Radiometer (MODIS) high-confidence-cloud cloud mask to achieve cloud conservative behavior. The algorithm allows tuning of the conservativeness by introducing the quantities of Distance-to-Threshold, Activation Value and number of tests to activate. These user-specified parameters determine how much confidence is needed for a cloudy or clear classification. The results are presented for the Los Angeles primary target area. The overall agreement between the MODIS cloud mask and the MAIA cloud mask (MCM) is 92.9%. Of the 7.1% disagreement, 60% of it was due to false positives by the MCM, considering MODIS as the truth. The MCM is more than 90% in agreement with MODIS for deep non-sun-glint water and the first 11 of the 16 snow-free land surface types. It differs from the MODIS cloud mask the most over bright desert, mountains and coastlines due to false cloudy flags. It agrees well with the MODIS cloud mask for cumulus, stratus and high cirrus, with greater disagreements over cloud edges, smoke plumes from wildfires, and very thin cirrus. The MCM agrees well with the MODIS cloud mask (>85%) for most solar zenith angles between 25 and 53 degrees, viewing zenith angles less than 60 degrees, and relative azimuth angles between 105 and 135 degrees. Several recommendations for improving the MCM are discussed, and its advantages over the MODIS cloud mask

Impact of land use and land cover change on land surface temperature in Iskandar Malaysia using remote sensing technique

Iskandar Malaysia is one of the impressive development projects ever undertaken in Malaysia that has been experiencing rapid rate of land use change since 2006. Land use change is due to the urban expansion and reduction in natural green areas resulted from enhanced economic growth. The three objectives of this study are (i) to estimate the land use and land cover changes (LULC) in Iskandar Malaysia from 1989 to 2014, (ii) to investigate the effect of LULC changes on land surface temperature (LST) change in the study area and (iii) to predict the LST by 2025. Remote sensing data namely Landsat (Landsat 5, 7 and 8) and Moderate Resolution Imaging Spectroradiometer (MODIS) of Terra product (MOD11A1) were used to classify various LULC and to calculate the LST in Iskandar Malaysia. There are two digital classification techniques used to classify and test the different LULC in this study area. Maximum Likelihood Classification (MLC) technique provided higher accuracies compared to the Support Vector Machine (SVM) technique. Consequently, the classified satellite images using the MLC technique were used to monitor changes in LULC in Iskandar Malaysia. LST was extracted using mono window. The mean LST using Geographic Information System (GIS) analysis according to LULC shows that water areas recorded the highest night time LST value, while forest recorded the lowest day time LST value. Urban areas are the warmest land use during the day and the second warmest land use during the night time. Moreover, the weighted average used to predict the mean LST of entire Iskandar Malaysia, it was found that if green space increases LST value would decrease by 0.5○C. To predict the effect of LULC changes on mean LST of each LULC types linear curve fitting model was used. According to the results, the mean night LST from 2000 to 2025 will increase in Iskandar Malaysia as urban (20.89°C to 22.39°C±0.45), mangrove (20.88°C to 22.59°C±0.50), forest (20.39°C to 21.04°C±0.18), oil palm (20.39°C to 21.25±0.25), rubber (20.34°C to 22.36°C ± 0.57), and water (21.61 °C to 23.31°C ± 0.51). The results show increment in day time at urban (29.26°C to 32.78°C±1.07), mangrove (26.23°C to 28.82 °C±0.89), forest (25.76°C to 27.54°C±0.49), oil palm (27.02°C to 29.54±0.70), rubber (26.49°C to 27.24°C ±0.29), and water (26.10 °C to 28.77 °C ± 0.8) respectively. Moreover, the relationship between LST and several impervious and vegetation indexes show that there is a strong relationship between impervious indexes and LST, and an inverse relationship between vegetation indexes and LST. Finally, this study concluded that replacing green natural area with improvise surface can increase the land surface temperature and have negative effect on urban thermal comfort

SIMBIOS Project 1998 Annual Report

The purpose of this series of technical reports is to provide current documentation of the Sensor Intercomparison and Merger for Biological and Interdisciplinary Ocean Studies (SIMBIOS) Project activities, NASA Research Announcement (NRA) research status, satellite data processing, data product validation and field calibration. This documentation is necessary to ensure that critical information is related to the scientific community and NASA management. This critical information includes the technical difficulties and challenges of combining ocean color data from an array of independent satellite systems to form consistent and accurate global bio-optical time series products. This technical report is not meant to substitute for scientific literature. Instead, it will provide a ready and responsive vehicle for the multitude of technical reports issues by an operational project

Characterising dust emission events from long-term surface observations in northern Africa

Dust plays multiple important roles in the Earth system with emissions from northern Africa contributing on the order of 60% to the global total. Current model estimates of annual dust production from this crucial region vary by a factor of up to 5. This low agreement between models is to a great extent due to differences in the representation of near-surface winds. One barrier to better understanding of wind processes is the sparse observation network in northern Africa combined with regionally varying, but not necessarily documented, reporting procedures that lead to uncertainties and biases. Previous studies have utilised long-term station observations of visibility over this region to investigate dust climatology, but this work is the first to focus specifically on emission, based on quality-controlled reports from station observers and measurements of 10 m wind-speed. The interannual, seasonal and diurnal cycles of dust emission frequency (FDE), as well as trends, are investigated using existing and new analysis methods, such as the estimation of emission thresholds. Spatially, it is shown that threshold wind-speeds for dust emission are highest in northern Algeria and lowest in Sudan and around the latitude band 16◦N - 21◦N. FDE peaks in spring at most stations, while in the Sahel seasonal cycles vary between stations depending on their proximity to the Saharan Heat Low, and as a result of seasonal exposure to both the summer monsoon and winter Harmattan. Seasonally, FDE is largely controlled by changes in strong winds, rather than changes in emission thresholds. The relative contribution of different wind-speeds to dust uplift are investigated using the observed winds and calculated thresholds. Case studies and field campaign data are analysed to determine the plausibility of SYNOP high-wind reports. In northern regions, 50% of uplift is associated with high winds which occur only 0.3% - 0.5% of the time. This contrasts with an occurrence range of 0.7% - 2.5% for southern regions. Winds of 12 – 15 ms−1 contribute the most to northern total DUP, while in the south the range is lower at 7-11 ms−1. A percentage occurrence of 0.3% equates to only 5.5 events per year. Previous studies have documented changes in the dust output from northern Africa on interannual to decadal time scales, though the reasons for this variability are still debated. This study shows that the likely contributors to an observed decreasing trend in FDE are changes in circulation patterns, changes to the Bowen ratio and, most significantly, the effect of a change in roughness on wind-speed as a result of a greening of the Sahel. This work forms a base for further investigations into mechanisms for dust emission in northern Africa and their relative importance, as well as providing reference material for model and reanalysis evaluation

Space-based Monitoring of Volcanic Emissions Using the GOME-2 Instrument

Die satellitengestützte Beobachtung der Erdatmosphäre ermöglicht es heutzutage, Vulkanemissionen, insbesondere auch während großer Eruptionen, global und aus sicherer Entfernung zu quantifizieren. Im Rahmen dieser Doktorarbeit wurden Daten des Satelliteninstruments GOME-2 mit Hilfe der differentiellen optischen Absorpionsspektroskopie (DOAS) systematisch auf das Vorkommen vulkanischer Emissionen von Schwefeldioxid (SO2) und Brommonoxid (BrO) untersucht. Neben einer Verbesserung des SO2 Auswertealgorithmus, der nun die deutlich genauere Ermittlung von Säulendichten im Fall sehr hoher SO2 Konzentrationen ermöglicht, wurde ein neues Verfahren entwickelt, das die automatische Detektion von Vulkanfahnen aus globalen Satellitendaten erlaubt. Die Anwendung auf den GOME-2 Datensatz der Jahre 2007-2011 ermöglichte eine systematische Suche nach BrO in nahezu 800 extrahierten Vulkanfahnen, welches in 64 Fällen nachgewiesen werden konnte. In einer Vielzahl dieser Fälle wurden Unterschiede in den räumliche Verteilungen von SO2 und BrO festgestellt. Die mittleren BrO/SO2 Verhältnisse entsprachen dabei weitgehend den Beobachtungen durch Bodenmessungen der letzten Jahren. Auf Grund der erzielten Ergebnisse konnte die Gesamtzahl der weltweit bekannten Vulkane mit signifikanten Bromemissionen von 12 auf 19 gesteigert werden. Darüber hinaus wurde erstmals das BrO-Vorkommen an einem passiv ausgasenden Vulkan durch jährlich gemittelte Satellitenmessungen nachgewiesen. Der neu gewonnene, umfangreiche Datensatz vulkanischer BrO Emissionen erlaubt eine detaillierte Untersuchung der Halogenchemie in großflächigen Vulkanfahnen sowie eine Abschätzung der globalen Bromemissionen in die Atmosphäre

Modélisation 3D du transfert raidatif pour simuler les images et données de spectroradiomètres et Lidars satellites et aéroportés de couverts végétaux et urbains

Les mesures de télédétection (MT) dépendent de l'interaction du rayonnement avec les paysages terrestres et l'atmosphère ainsi que des configurations instrumentales (bande spectrale, résolution spatiale, champ de vue: FOV,...) et expérimentales (structure et propriétés optiques du paysage et atmosphère,...). L'évolution rapide des techniques de télédétection requiert des outils appropriés pour valider leurs principes et améliorer l'emploi des MT. Les modèles de transfert radiatif (RTM) simulent des quantités (fonctions de distribution de la réflectance (BRDF) et température (BTDF), forme d'onde LiDAR, etc.) plus ou moins proches des MT. Ils constituent l'outil de référence pour simuler les MT, pour diverses applications : préparation et validation des systèmes d'observation, inversion de MT,... DART (Discrete Anisotropic Radiative Transfer) est reconnu comme le RTM le plus complet et efficace. J'ai encore nettement amélioré son réalisme via les travaux de modélisation indiqués ci-dessous. 1. Discrétisation de l'espace des directions de propagation des rayons. DART simule la propagation des rayons dans les paysages terrestres et l'atmosphère selon des directions discrètes. Les méthodes classiques définissent mal le centroïde et forme des angles solides de ces directions, si bien que le principe de conservation de l'énergie n'est pas vérifié et que l'obtention de résultats précis exige un grand nombre de directions. Pour résoudre ce problème, j'ai conçu une méthode originale qui crée des directions discrètes de formes définies. 2. Simulation d'images de spectroradiomètre avec FOV fini (caméra, pushbroom,...). Les RTMs sont de type "pixel" ou "image". Un modèle "pixel" calcule une quantité unique (BRDF, BTDF) de toute la scène simulée via sa description globale (indice foliaire, fraction d'ombre,...). Un modèle "image" donne une distribution spatiale de quantités (BRDF,...) par projection orthographique des rayons sur un plan image. Tous les RTMs supposent une acquisition monodirectionnelle (FOV nul), ce qui peut être très imprécis. Pour pouvoir simuler des capteurs à FOV fini (caméra, pushbroom,...), j'ai conçu un modèle original de suivi de rayons convergents avec projection perspective. 3. Simulation de données LiDAR. Beaucoup de RTMs simulent le signal LiDAR de manière rapide mais imprécise (paysage très simplifié, pas de diffusions multiples,...) ou de manière précis mais avec de très grands temps de calcul (e.g., modèles Monte-Carlo: MC). DART emploie une méthode "quasi-MC" originale, à la fois précise et rapide, adaptée à toute configuration instrumentale (altitude de la plateforme, attitude du LiDAR, taille de l'empreinte,...). Les acquisitions multi-impulsions LiDAR (satellite, avion, terrestre) sont simulées pour toute configuration (position du LiDAR, trajectoire de la plateforme,...). Elles sont converties dans un format industriel pour être traitées par des logiciels dédiés. Un post-traitement convertit les formes d'onde LiDAR simulées en données LiDAR de comptage de photons. 4. Bruit solaire et fusion de données LiDAR et d'images de spectroradiomètre. DART peut combiner des simulations de LiDAR multi-impulsions et d'image de spectro-radiomètre (capteur hyperspectral,...). C'est une configuration à 2 sources (soleil, laser LiDAR) et 1 capteur (télescope du LiDAR). Les régions mesurées par le LiDAR, dans le plan image du sol, sont segmentées dans l'image du spectro-radiomètre, elle aussi projetée sur le plan image du sol. Deux applications sont présentées : bruit solaire dans le signal LiDAR, et fusion de données LiDAR et d'images de spectro-radiomètre. Des configurations d'acquisition (trajectoire de plateforme, angle de vue par pixel du spectro-radiomètre et par impulsion LiDAR) peuvent être importées pour encore améliorer le réalisme des MT simulées, De plus, j'ai introduit la parallélisation multi-thread, ce qui accélère beaucoup les calculsRemote Sensing (RS) data depend on radiation interaction in Earth landscapes and atmosphere, and also on instrumental (spectral band, spatial resolution, field of view (FOV),...) and experimental (landscape/atmosphere architecture and optical properties,...) conditions. Fast developments in RS techniques require appropriate tools for validating their working principles and improving RS operational use. Radiative Transfer Models (RTM) simulate quantities (bidirectional reflectance; BRDF, directional brightness temperature: BTDF, LiDAR waveform...) that aim to approximate actual RS data. Hence, they are celebrated tools to simulate RS data for many applications: preparation and validation of RS systems, inversion of RS data... Discrete Anisotropic Radiative Transfer (DART) model is recognized as the most complete and efficient RTM. During my PhD work, I further improved its modeling in terms of accuracy and functionalities through the modeling work mentioned below. 1. Discretizing the space of radiation propagation directions.DART simulates radiation propagation along a finite number of directions in Earth/atmosphere scenes. Classical methods do not define accurately the solid angle centroids and geometric shapes of these directions, which results in non-conservative energy or imprecise modeling if few directions are used. I solved this problem by developing a novel method that creates discrete directions with well-defined shapes. 2. Simulating images of spectroradiometers with finite FOV.Existing RTMs are pixel- or image-level models. Pixel-level models use abstract landscape (scene) description (leaf area index, overall fraction of shadows,...) to calculate quantities (BRDF, BTDF,...) for the whole scene. Image-level models generate scene radiance, BRDF or BTDF images, with orthographic projection of rays that exit the scene onto an image plane. All models neglect the multi-directional acquisition in the sensor finite FOV, which is unrealistic. Hence, I implemented a sensor-level model, called converging tracking and perspective projection (CTPP), to simulate camera and cross-track sensor images, by coupling DART with classical perspective and parallel-perspective projection. 3. Simulating LiDAR data.Many RTMs simulate LiDAR waveform, but results are inaccurate (abstract scene description, account of first-order scattering only...) or require tremendous computation time for obtaining accurate results (e.g., Monte-Carlo (MC) models). With a novel quasi-MC method, DART can provide accurate results with fast processing speed, for any instrumental configuration (platform altitude, LiDAR orientation, footprint size...). It simulates satellite, airborne and terrestrial multi-pulse laser data for realistic configurations (LiDAR position, platform trajectory, scan angle range...). These data can be converted into industrial LiDAR format for being processed by LiDAR processing software. A post-processing method converts LiDAR waveform into photon counting LiDAR data, through modeling single photon detector acquisition. 4. In-flight Fusion of LiDAR and imaging spectroscopy.DART can combine multi-pulse LiDAR and cross-track imaging spectroscopy (hyperspectral sensor...). It is a 2 sources (sun, LiDAR laser) and 1 sensor (LiDAR telescope) system. First, a LiDAR multi-pulse acquisition and a sun-induced spectro-radiometer radiance image are simulated. Then, the LiDAR FOV regions projected onto the ground image plane are segmented in the spectro-radiometer image, which is also projected on the ground image plane. I applied it to simulate solar noise in LiDAR signal, and to the fusion of LiDAR data and spectro-radiometer images. To further improve accuracy when simulating actual LiDAR and spectro-radiometer, DART can also import actual acquisition configuration (platform trajectory, view angle per spectro-radiometer pixel / LiDAR pulse). Moreover, I introduced multi-thread parallelization, which greatly accelerates DART simulation