scholarly journals Monitoring Dust Storms in Iraq Using Satellite Data

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3687
Author(s):  
Albarakat ◽  
Lakshmi
Keyword(s):  
Dust Storm ◽  
Land Surface ◽  
Large Degree ◽  
Dust Storms ◽  
Medical Problems ◽  
Ice Cloud ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. The time between June 29 to July 8, 2009 is considered one of the worst dust storm periods of all times and many Iraqis suffered medical problems as a result. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Surface Reflectance Daily L2G Global 1km and 500m data were utilized to calculate the Normalized Difference Dust Index (NDDI). The MYD09GA V006 product was used to monitor, map, and assess the development and spread of dust storms over the arid and semi-arid territories of Iraq. We set thresholds for NDDI to distinguish between water and/or ice cloud and ground features and dust storms. In addition; brightness temperature data (TB) from the Aqua /MODIS thermal band 31 were analyzed to distinguish sand on the land surface from atmospheric dust. We used the MODIS level 2 MYD04 deep blue 550nm Aerosol Option Depth (AOD) data that maintains accuracy even over bright desert surfaces. We found NDDI values lower than 0.05 represent clouds and water bodies, while NDDI greater than 0.18 correspond to dust storm regions. The threshold of TB of 310.5 K was used to distinguish aerosols from the sand on the ground. Approximately 75% of the territory was covered by a dust storm in July 5th 2009 due to strong and dry northwesterly winds.

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%.

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 A Slight Temperature Warming Trend Occurred over Lake Ontario from 2001 to 2018

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1315
Author(s):  
Xiaoying Ouyang ◽  
Dongmei Chen ◽  
Shugui Zhou ◽  
Rui Zhang ◽  
Jinxin Yang ◽  
...  
Keyword(s):  
Land Surface ◽  
Lake Ontario ◽  
Kendall Test ◽  
Warming Trend ◽  
Linear Regression Method ◽  
Ground Measurement ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Difference

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

scholarly journals Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

2014 ◽  
Vol 7 (2) ◽  
pp. 1671-1707
Author(s):  
J. Kala ◽  
J. P. Evans ◽  
A. J. Pitman ◽  
C. B. Schaaf ◽  
M. Decker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Surface Model ◽  
Parameter Uncertainties ◽  
Australian Continent ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.

Spatiotemporal variability of land surface temperature in north-western Ethiopia

2021 ◽  
Author(s):  
Getachew Bayable ◽  
Getnet Alemu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatiotemporal Variability ◽  
Environmental Challenges ◽  
Annual Variations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
North Western

Abstract The aggravating deforestation, industrialization and urbanization are increasingly becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal discrepancies in Land Surface Temperature (LST) and the link with elevation in Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001–2020) was used. The pixel-based linear regression model was employed to explore the spatiotemporal discrepancies of LST changes pixel-wise. Furthermore, Sen's slope and Mann-Kendall were used for determining the extent of temporal shifts of the areal average LST and evaluating trends in areal average LST values, respectively. Coefficient of Variation (CV) was calculated to examine spatial and temporal discrepancies in seasonal and annual LST for each pixel. The distribution of average seasonal LST spatially ranged from 43.45–16.62℃, 39.89–14.59℃, 50.39-21.102℃ and 43.164–20.39℃ for autumn (September-November), summer (June-August), spring (March-May) and winter (December-February) seasons, respectively. The seasonal LST CV varied from1.096-10.72%, 0.7–11.06%, 1.29–14.76% and 2.19–10.35% for average autumn, summer, spring and winter seasons, respectively. The seasonal spatial LST trend varied from − 0.7 − 0.16, -0.4-0.224, 0.6 − 0.19 and − 0.6 − 0.32 for average autumn, summer, spring and winter seasons, respectively. Besides, the annual spatial LST slope varied from − 0.58 − 0.17. An insignificantly declining trend in LST shown at 0.036℃ yr− 1, 0.041℃ yr− 1, 0.074℃ yr− 1, 0.005℃ yr− 1 in autumn, summer, spring and winter seasons (P < 0.05), respectively. Moreover, the annual variations of mean LST decreased insignificantly at 0.046℃ yr− 1. Generally, the LST is tremendously variable in space and time and negatively correlated with an elevation.

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