Reproducibility assessment and uncertainty quantification in subjective dust source mapping

2021 ◽  
Author(s):  
Samantha Sinclair ◽  
Sandra LeGrand
Keyword(s):  
Source Location ◽  
Point Sources ◽  
Dust Storms ◽  
Location Error ◽  
Dust Source ◽  
Airborne Dust ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Source Markers

Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.

scholarly journals Reproducibility assessment and uncertainty quantification in subjective dust source mapping

2021 ◽  
Author(s):  
Samantha Sinclair ◽  
Sandra LeGrand
Keyword(s):  
Source Location ◽  
Point Sources ◽  
Dust Storms ◽  
Location Error ◽  
Dust Source ◽  
Airborne Dust ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Source Markers

Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.

scholarly journals Dynamics of Muddy Rain of 15 June 2018 in Nepal

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 529
Author(s):  
Ashok Kumar Pokharel ◽  
Tianli Xu ◽  
Xiaobo Liu ◽  
Binod Dawadi
Keyword(s):  
Dust Emission ◽  
Dust Storms ◽  
Strong Wind ◽  
Hysplit Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Upper Level ◽  
Terra Satellite ◽  
Negative Buoyancy

It has been revealed from the Modern-Era Retrospective analysis for Research and Applications MERRA analyses, Moderate Resolution Imaging Spectroradiometer MODIS/Terra satellite imageries, Naval Aerosol Analysis and Prediction System NAAPS model outputs, Cloud –Aerosol Lidar and Infrared Pathfinder Satellite Observations CALIPSO imageries, Hybrid Single Particle Lagrangian Integrated Trajectory HYSPLIT model trajectories, atmospheric soundings, and observational records of dust emission that there were multiple dust storms in the far western parts of India from 12 to 15 June 2018 due to thunderstorms. This led to the lifting of the dust from the surface. The entry of dust into the upper air was caused by the generation of a significant amount of turbulent kinetic energy as a function of strong wind shear generated by the negative buoyancy of the cooled air aloft and the convective buoyancy in the lower planetary boundary layer. Elevated dust reached a significant vertical height and was advected towards the northern/northwestern/northeastern parts of India. In the meantime, this dust was carried by northwesterly winds associated with the jets in the upper level, which advected dust towards the skies over Nepal where rainfall was occurring at that time. Consequently, this led to the muddy rain in Nepal.

scholarly journals Radiometric Cross-Calibration of Tiangong-2 MWI Visible/NIR Channels over Aquatic Environments using MODIS

2018 ◽  
Vol 10 (11) ◽  
pp. 1803 ◽  
Author(s):  
Qu Zhou ◽  
Liqiao Tian ◽  
Jian Li ◽  
Qingjun Song ◽  
Wenkai Li
Keyword(s):  
Ocean Color ◽  
Aquatic Environments ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Analysis System ◽  
Cross Calibration ◽  
Data Analysis System ◽  
Mean Square Errors ◽  
Calibration Coefficients

The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality ocean color products for MWI observations. We proposed a simple and effective cross-calibration scheme for MWI data using well calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over aquatic environments. The path radiance of the MWI was estimated using the quasi-synchronized MODIS images as well as the MODIS Rayleigh and aerosol look up tables (LUTs) from SeaWiFS Data Analysis System 7.4 (SeaDAS 7.4). The results showed that the coefficients of determination (R2) of the calibration coefficients were larger than 0.97, with sufficient matched areas to perform cross-calibration for MWI. Compared with the simulated Top of Atmosphere (TOA) radiance using synchronized MODIS images, all errors calculated with the calibration coefficients retrieved in this paper were less than 5.2%, and lower than the lab calibrated coefficients. The Rayleigh-corrected reflectance (ρrc), remote sensing reflectance (Rrs) and total suspended matter (TSM) products of MWI, MODIS and the Geostationary Ocean Color Imager (GOCI) images for Taihu Lake in China were compared. The distribution of ρrc of MWI, MODIS and GOCI agreed well, except for band 667 nm of MODIS, which might have been saturated in relatively turbid waters. Besides, the Rrs used to retrieve TSM among MWI, MODIS and GOCI was also consistent. The root mean square errors (RMSE), mean biases (MB) and mean ratios (MR) between MWI Rrs and MODIS Rrs (or GOCI Rrs) were less than 0.20 sr−1, 5.52% and within 1 ± 0.023, respectively. In addition, the derived TSM from MWI and GOCI also agreed with a R2 of 0.90, MB of 13.75%, MR of 0.97 and RMSE of 9.43 mg/L. Cross-calibration coefficients retrieved in this paper will contribute to quantitative applications of MWI. This method can be extended easily to other similar ocean color satellite missions.

Annual mapping of large forest disturbances across Canada’s forests using 250 m MODIS imagery from 2000 to 2011

2014 ◽  
Vol 44 (12) ◽  
pp. 1545-1554 ◽  
Author(s):  
L. Guindon ◽  
P.Y. Bernier ◽  
A. Beaudoin ◽  
D. Pouliot ◽  
P. Villemaire ◽  
...  
Keyword(s):  
Validation Dataset ◽  
Commission Error ◽  
Forest Disturbances ◽  
Forest Change ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Supplementary Material ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Data Layers

Disturbances such as fire and harvesting shape forest dynamics and must be accounted for when modelling forest properties. However, acquiring timely disturbance information for all of Canada’s large forest area has always been challenging. Therefore, we developed an approach to detect annual forest change resulting from fire, harvesting, or flooding using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at 250 m spatial resolution across Canada and to estimate the within-pixel fractional change (FC). When this approach was applied to the period from 2000 to 2011, the accuracy of detection of burnt, harvested, or flooded areas against our validation dataset was 82%, 80%, and 85%, respectively. With FC, 77% of the area burnt and 82% of the area harvested within the validation dataset were correctly identified. The methodology was optimized to reduce the commission error but tended to omit smaller disturbances as a result. For example, the omitted area for harvest blocks greater than 80 ha was less than 14% but increased to between 38% and 50% for harvest blocks of 20 to 30 ha. Detection of burnt and harvested areas in some regions was hindered by persistent haze or cloud cover or by insect outbreaks. All resulting data layers are available as supplementary material.

scholarly journals Long-term aerosol optical depth datasets over China retrieved from satellite data

2011 ◽  
Vol 4 (6) ◽  
pp. 6643-6678 ◽  
Author(s):  
Y. Xue ◽  
H. Xu ◽  
Y. Li ◽  
L. Yang ◽  
L. Mei ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Dust Storms ◽  
The Tibetan Plateau ◽  
Hysplit Model ◽  
The North ◽  
Moderate Resolution ◽  
Dust Weather ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Nine years of daily aerosol optical depth (AOD) measurements have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over China for the period from August 2002 to August 2011, comprising AODs at 470, 550, and 660 nm. Then, the variation over China over the nine years was determined from the derived AOD data. Preliminary daily results show the agreement between the Aerosol Robotic Network (AERONET) AOD data and the derived AOD data. From 1219 daily collocations, representing mutually cloud-free conditions, we find that more than 54% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 20%. From 222 monthly averaged collocations, representing mutually cloud-free conditions, we find that more than 63% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 15% and more than 70% within an expected error envelop of 20%. In addition, the long-term SRAP AOD dataset has been implemented in analysing case studies involving dust storms, haze and the characteristics of AOD variation over China over the past nine years. It was found that areas in China with high AOD values generally appear in the Inner Mongolia, the North China Plain, Tarim Basin, the Sichuan Basin, the Tibetan Plateau and the middle and lower reaches of the Yangtze River and area with low AOD values generally appear in the Fujian Province, the Yungui Plateau, and northeast plain. The seasonal averaged AOD results indicate that AOD values generally reach their maximum in spring and their minimum in winter. The yearly mean and monthly mean SRAP AOD were also used to study the spatial and temporal aerosol distributions over China. The results indicate that the AOD over China exhibited no obvious change. Monthly averaged AOD in August in Beijing experienced one decreasing processes from 2006 to 2010, especially after 2007. The monthly mean AOD decreased from 0.46 in 2007 to 0.29 in 2010. SRAP AODs were used to study one haze case and dust case. Combining AOD data from the SRAP AOD dataset and HYSPLIT model can forecast the transport of haze. SRAP AOD data are also sensitive enough to reflect the occurrence and intensity of dust weather. Thus, the SRAP AOD dataset can be used to precisely reflect the spatial distribution, concentration distribution and transmission path of dust.

scholarly journals Ice extent in sub-arctic fjords and coastal areas from 2001 to 2019 analyzed from MODIS imagery

2020 ◽  
Vol 61 (82) ◽  
pp. 210-226
Author(s):  
Megan O'Sadnick ◽  
Chris Petrich ◽  
Camilla Brekke ◽  
Jofrid Skarðhamar
Keyword(s):  
Coastal Areas ◽  
Degree Days ◽  
Weather Variables ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Arctic Fjords

AbstractResults examining variations in the ice extent along the Norwegian coastline based on the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 to 2019, February through May, are presented. A total of 386 fjords and coastal areas were outlined and grouped into ten regions to assess seasonal and long-term trends in ice extent. In addition, three fjords were examined to investigate how ice extent may vary over short distances (<100 km). Of the 386 outlined, 47 fjords/coastal areas held >5 km2 of ice at least once between 2001 and 2019. Over this span of time, no statistically significant trend in ice extent is found for all ten regions; however, variations between regions and years are evident. Ice extent is assessed through comparison to three weather variables – freezing degree days (FDD), daily new snowfall and daily freshwater supply from rainfall plus snowmelt. Six out of ten regions are significantly positively correlated (p < 0.05) to FDD. In addition, ice in two regions is significantly positively correlated to daily new snowfall, and in one region negatively correlated to rainfall plus snowmelt. The importance of fjord geometry and bathymetry as well as other weather variables including wind is discussed.

scholarly journals Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms

2008 ◽  
Vol 25 (4) ◽  
pp. 501-518 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Thomas J. Kopp ◽  
John M. Jackson
Keyword(s):  
Satellite System ◽  
Classification Algorithms ◽  
Cloud Classification ◽  
Infrared Imager ◽  
Other Information ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
New Procedures

Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

scholarly journals Northern Hemisphere Contrail Properties Derived from Terra and Aqua MODIS Data for 2006 and 2012

2018 ◽  
Author(s):  
David P. Duda ◽  
Sarah T. Bedka ◽  
Patrick Minnis ◽  
Douglas Spangenberg ◽  
Konstantin Khlopenkov ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Detection Algorithm ◽  
Air Traffic ◽  
Effective Diameter ◽  
Spreading Factor ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery, and are compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase (from 0.136 % to 0.140 %) compared to 2006 data for Terra and increased by almost 7 % (0.134 % to 0.143 %) for Aqua. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be three to four times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcing (TCRF) for all 2012 Terra observations were −6.3, 14.3, and 8.0 mW m−2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, −6.4, 15.5, and 9.0 mW m−2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by interannual changes in contrail coverage over each area.

scholarly journals DUST STORM DETECTION USING MODIS DATA OVER THE MIDDLE EAST

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1147-1151
Author(s):  
A. Zandkarimi ◽  
P. Fatehi
Keyword(s):  
Remote Sensing ◽  
Middle East ◽  
Dust Storm ◽  
Dust Storms ◽  
Time Data ◽  
Sensing Technology ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Data Source ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Dust storms are one of the common phenomena in the arid and semi-arid regions which cause many economic and environmental losses also affect human health. Therefore, it is necessary to be able to detect dust storms. Several methods exist for dust monitoring, such as Ground-based measurements, satellite remote sensing, video surveillance, wireless sensors. Remote sensing technology provides wide coverage, high spectral and temporal resolutions, even near real-time data, which can offer a valuable data source for dust storm monitoring. We used an algorithm based on Moderate Resolution Imaging Spectroradiometer (MODIS) images for detecting dust storm over the Middle East. The proposed algorithm uses the brightness temperature using multi-bands. The performance of the algorithm was evaluated using the ground-based observations of synoptic stations. The results showed that by applying the algorithm, the dust area can be clearly separated, especially in the regions that cloud is mixed with dust and achieved overall accuracy was ~78%.

Sign in / Sign up

Export Citation Format

Share Document