Browsing by Author "Shi, Yingxi"
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Item Continuing the MODIS Dark Target Aerosol Time Series with VIIRS(MDPI, 2020-01-17) Sawyer, Virginia; Levy, Robert C.; Mattoo, Shana; Cureton, Geoff; Shi, Yingxi; Remer, Lorraine A.For reflected sunlight observed from space at visible and near-infrared wavelengths, particles suspended in Earth’s atmosphere provide contrast with vegetation or dark water at the surface. This is the physical motivation for the Dark Target (DT) aerosol retrieval algorithm developed for the Moderate Resolution Imaging Spectrometer (MODIS). To extend the data record of aerosol optical depth (AOD) beyond the expected 20-year lifespan of the MODIS sensors, DT must be adapted for other sensors. A version of the DT AOD retrieval for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership (SNPP) is now mature enough to be released as a standard data product, and includes some upgraded features from the MODIS version. Di erences between MODIS Aqua and VIIRS SNPP lead to some inevitable disagreement between their respective AOD measurements, but the o set between the VIIRS SNPP and MODIS Aqua records is smaller than the o set between those of MODIS Aqua and MODIS Terra. The VIIRS SNPP retrieval shows good agreement with ground-based measurements. For most purposes, DT for VIIRS SNPP is consistent enough and in close enough agreement with MODIS to continue the record of satellite AOD. The reasons for the o set from MODIS Aqua, and its spatial and temporal variability, are investigated in this study.Item The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future(MDPI, 2020-09-01) Remer, Lorraine; Levy, Robert C.; Mattoo, Shana; Tanré, Didier; Gupta, Pawan; Shi, Yingxi; Sawyer, Virginia; Munchak, Leigh A.; Zhou, Yaping; Kim, Mijin; Ichoku, Charles; Patadia, Falguni; Li, Rong-Rong; Gassó, Santiago; Kleidman, Richard G.; Holben, Brent N.The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.Item Exploring systematic offsets between aerosol products from the two MODIS sensors(Copernicus Publications, 2018-07-13) Levy, Robert C.; Mattoo, Shana; Sawyer, Virginia; Shi, Yingxi; Colarco, Peter R.; Lyapustin, Alexei I.; Wang, Yujie; Remer, Lorraine A.Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm is applied to Moderate Resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55µm) over both land and ocean, and an Ångström exponent (AE derived from 0.55 and 0.86µm) over ocean. Here, we analyze the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by ∼13% (∼0.02 over land and ∼0.015 over ocean), and this offset (MOD04 – MYD04) has seasonal as well as long-term variability. Focusing on 2008 and deriving yearly gridded mean AOD and AE, we find that, over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensor's polarization sensitivity and the response versus the scan angle and to cross-calibration from MODIS-T to MODIS-A. A second convolves the detrending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A collection (C6.1) of all MODIS Atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that (a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. (b) Because the MODIS-A product appears to have an overall smaller bias compared to ground-truth data, it may be more suitable for some applications. However (c), since the AOD offset is only ∼0.02 and within the noise level for single retrievals, both MODIS products may be adequate for most applications.Item Image Segmentation for Dust Detection Using Semi-supervised Machine Learning(IEEE, 2021-03-19) Yu, Manzhu; Bessac, Julie; Xu, Ling; Gangopadhyay, Aryya; Shi, Yingxi; Wang, JianwuDust plumes originating from the Earth’s major arid and semi-arid areas can significantly affect the climate system and human health. Many existing methods have been developed to identify dust from non-dust pixels from a remote sensing point of view. However, these methods use empirical rules and therefore have difficulty detecting dust above or below the detectable thresholds. Supervised machine learning methods have also been applied to detect dust from satellite imagery, but these methods are limited especially when applying to areas outside the training data due to the inadequate amount of ground truth data. In this work, we proposed an automatic dust segmentation framework using semi-supervised machine learning, based on a collocated dataset using Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The proposed method utilizes unsupervised machine learning for segmentation of VIIRS imagery, and leverages the guidance from the dust labels using the dust profile product of CALIPSO to determine the dust clusters as the final product. The dust clusters are determined based on the similarity of spectral signature from dust pixels along the CALIPSO tracks. Experiment results show that the accuracy of the proposed framework outperforms the traditional physical infrared method along CALIPSO tracks. In addition, the proposed method performs consistently over three different study areas, the North Atlantic Ocean, East Asia, and Northern Africa.Item Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences(UMBC HPCF, 2020) Bessac, Julie; Xu, Ling; Yu, Manzhu; Gangopadhyay, Aryya; Shi, Yingxi; Guo, PeiDust and sandstorms originating from Earth’s major arid and semi-arid desert areas can significantly affect the climate system and health. Many existing methods use heuristic rules to classify on a pixel-level regarding dust or dust-free. However, these heuristic rules are limited in applicability when the study area or the study period has changed. Based on a multisensor collocation dataset, we sought to utilize unsupervised machine learning techniques to detect and segment dust in multispectral satellite imagery. In this report, we describe the datasets used, discuss our methodology, and provide preliminary validation results.Item Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX-AQ(AGU, 2024-01-18) Shi, Yingxi; Levy, R. C.; Remer, Lorraine; Mattoo, S.; Arnold, G. T.Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10-min refresh, imagers in Geostationary (GEO) orbit have advantages monitoring smoke over once-per-day sensors in low-earth orbit (LEO). However, both can be inadequate in capturing the characteristics of smoke plumes close to their sources due to too-coarse spatial resolution (both detector and product resolution), too-sparse temporal resolution (from LEO sensors), and too-conservative masking. In addition to satellite observations, the Fire Influence on Regional to Global Environments and Air Quality experiment offered sub-orbital enhanced-MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution—including multiple eMAS flight tracks over individual fires in short time periods. It provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied modified aerosol algorithm to different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products.Item Observation and modeling of the historic “Godzilla” African dust intrusion into the Caribbean Basin and the southern US in June 2020(EGI, 2021-08-18) Yu, Hongbin; Tan, Qian; Zhou, Lillian; Zhou, Yaping; Bian, Huisheng; Chin, Mian; Ryder, Claire L.; Pradhan, Yaswant; Shi, Yingxi; Song, Qianqian; Zhang, Zhibo; Colarco, Peter R.; Kim, Dongchul; Remer, Lorraine; Yuan, Tianle\; Mayol-Bracero, Olga; Brent N. Holben, Brent N.This study characterizes a massive African dust intrusion into the Caribbean Basin and southern US in June 2020, which is nicknamed the “Godzilla” dust plume, using a comprehensive set of satellite and ground-based observations (including MODIS, CALIOP, SEVIRI, AERONET, and EPA Air Quality network) and the NASA GEOS global aerosol transport model. The MODIS data record registered this massive dust intrusion event as the most intense episode over the past 2 decades. During this event, the aerosol optical depth (AOD) observed by AERONET and MODIS peaked at 3.5 off the coast of West Africa and 1.8 in the Caribbean Basin. CALIOP observations show that the top of the dust plume reached altitudes of 6–8 km in West Africa and descended to about 4 km altitude over the Caribbean Basin and 2 km over the US Gulf of Mexico coast. The dust intrusion event degraded the air quality in Puerto Rico to a hazardous level, with maximum daily PM10 concentration of 453 µg m−3 recorded on 23 June. The dust intrusion into the US raised the PM2.5 concentration on 27 June to a level exceeding the EPA air quality standard in about 40 % of the stations in the southern US. Satellite observations reveal that dust emissions from convection-generated haboobs and other sources in West Africa were large albeit not extreme on a daily basis. However, the anomalous strength and northern shift of the North Atlantic Subtropical High (NASH) together with the Azores low formed a closed circulation pattern that allowed for accumulation of the dust near the African coast for about 4 d. When the NASH was weakened and wandered back to the south, the dust outflow region was dominated by a strong African easterly jet that rapidly transported the accumulated dust from the coastal region toward the Caribbean Basin, resulting in the record-breaking African dust intrusion. In comparison to satellite observations, the GEOS model reproduced the MODIS observed tracks of the meandering dust plume well as it was carried by the wind systems. However, the model substantially underestimated dust emissions from haboobs and did not lift up enough dust to the middle troposphere for ensuing long-range transport. Consequently, the model largely missed the satellite-observed elevated dust plume along the cross-ocean track and underestimated the dust intrusion into the Caribbean Basin by a factor of more than 4. Modeling improvements need to focus on developing more realistic representations of moist convection, haboobs, and the vertical transport of dust.Item Retrieval of Aerosol Properties from MODIS Terra, MODIS Aqua, and VIIRS SNPP: Calibration Focus(2016-06-06) Levy, Robert C; Mattoo, Shana; Sawyer, Virginia; Kleidman, Richard; Patadia, Falguni; Zhou, Yaping; Gupta, Pawan; Shi, Yingxi; Remer, Lorraine; Holz, RobertMODIS-DT Collection 6 - Aqua/Terra level 2, 3; entire record processed - "Trending" issues reduced - Still a 15% or 0.02 Terra vs Aqua offset. - Terra/Aqua convergence improved with C6+, but bias remains. - Other calibration efforts yield mixed results. VIIRS-‐DT in development - VIIRS is similar, yet different then MODIS - With 50% wider swath, VIIRS has daily coverage - Ensures algorithm consistency with MODIS. - Currently: 20% NPP vs Aqua offset over ocean. - Only small bias (%) over land (2012-‐2016) - Can VIIRS/MODIS create aerosol CDR? Calibration for MODIS - VIIRS continues to fundamentally important. It's not just Terra, or just Aqua, or just NPP-‐VIIRS, I really want to push synergistic calibration.Item Stitching a MODIS-VIIRS Time Series of Aerosol Properties Using the Dark Target Algorithm: Status in 2016(2016-06) Levy, Robert C; Mattoo, Shana; Sawyer, Virginia; Kleidman, Richard; Patadia, Falguni; Zhou, Yaping; Gupta, Pawan; Shi, Yingxi; Remer, Lorraine