Monitoring air pollutants distribution in urban areas are critical for public health and safety. A country like Pakistan with no network of advanced weather stations to extract high quality data to derive information products is very important. Trend maps of pollutants and other information parameters derived from satellite remote sensing data is a replicable technique to integrate into management decisions. This allows city management to effectively monitor visibility and air quality concerns informing public for to take effective measures.
Following are examples of the available satellite remote sensing products for air quality measurements.
Aerosol Optical Depth/Thickness product provides information on aerosol in the atmosphere.
Fires and Thermal Anomalies product shows active fire detection (including crop burning) and thermal anomalies.
Normalized Difference Vegetation Index (NDVI) is a measure of greeness and health of vegetation.
1. Aerosol Optical Depth
MODIS (Terra and Aqua) Combined Value-Added Aerosol Optical Depth (Temporal Coverage: 31 January 2013 – present). The MODIS (Terra and Aqua) Combined Value-Added Aerosol Optical Depth layer is a value-added layer based on MODIS Level 2 aerosol products. The layer can give a quick, synoptic view of the level of aerosol in the atmosphere.. MODIS Aerosol Optical Depth (or Aerosol Optical Thickness) layer indicates the level at which particles in the air (aerosols) prevent light from traveling through the atmosphere. Aerosols absorb and scatter incoming sunlight, which reduces visibility and increases the optical depth. An optical depth of less than 0.1 indicates a clear sky with maximum visibility, and a value of 1 indicates the presence of aerosols so dense that people would have difficulty seeing the Sun. Aerosols have an effect on human health, weather and the climate. Sources of aerosols include pollution from factories, smoke from fires, dust from dust storms, sea salts, and volcanic ash and smog. Aerosols compromise human health when inhaled by people with asthma or other respiratory illnesses. Aerosols also have an affect on the weather and climate by cooling or warming the earth, helping or preventing clouds from forming.
This level 3 gridded product is designed for quantitative applications including aerosol data assimilation and model validation. This layer is useful for aerosol forecasting communities such as the United States Navy Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Oceanic and Atmospheric Administration (NOAA), European Centre for Medium-Range Weather Forecasts (ECMWF), National Aeronautics and Space Administration (NASA) Global Modeling Assimilation Office (GMAO), University research groups and support for field/aircraft campaigns.
The MODIS Combined Value-Added Aerosol Optical Depth layer is a near real-time layer and available as a combined Terra satellite and Aqua satellite layer (MCDAODHD). The sensor resolution is 0.5 degrees, imagery resolution is 2 km, and the temporal resolution is daily.
References: NASA Earthdata – NRT Value-Added MODIS AOD Product; GCMD Entry: MCDAODHD
2. Fire and Thermal Anomalies
MODIS (Terra) Fire and Thermal Anomalies Temporal Coverage: 8 May 2012 – present. The MODIS Fire and Thermal Anomalies layer shows active fire detections and thermal anomalies, such as volcanoes, and gas flares. Fires can be set naturally, such as by lightning, or by humans, whether intentionally or accidentally. Fire is often thought of as a menace and detriment to life, but in some ecosystems it is necessary to maintain the equilibrium, for example, some plants only release seeds under high temperatures that can only be achieved by fire, fires can also clear undergrowth and brush to help restore forests to good health, humans use fire in slash and burn agriculture, to clear away last year’s crop stubble and provide nutrients for the soil and to clear areas for pasture. The fire layer is useful for studying the spatial and temporal distribution of fire, to locate persistent hot spots such as volcanoes and gas flares, to locate the source of air pollution from smoke that may have adverse human health impacts.
The MODIS Fire and Thermal Anomalies product is available from the Terra (MOD14) and Aqua (MYD14) satellites as well as a combined Terra and Aqua (MCD14) satellite product. The sensor resolution is 1 km, and the temporal resolution is daily. The thermal anomalies are represented as red points (approximate center of a 1 km pixel) in the Global Imagery Browse Services (GIBS)/Worldview.
Normalized Difference Vegetation Index (NDVI) (rolling 8-day) MODIS rolling 8-day Normalized Difference Vegetation Index (NDVI). The MODIS Normalized Difference Vegetation Index (NDVI) layer is a measure of the greenness and health of vegetation. The index is calculated based on how much red and near-infrared light is reflected by plant leaves. The index values range from -0.2 to 1 where higher values (0.3 to 1) indicate areas covered by green, leafy vegetation and lower values (0 to 0.3) indicate areas where there is little or no vegetation. Areas with a lot of green leaf growth, indicates the presence of chlorophyll which reflects more infrared light and less visible light, are depicted in dark green colors, areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are depicted in tan colors.
The MODIS rolling 8-day NDVI layer is available as a near real-time, rolling 8-day product (MOD13Q4N) from from the Terra satellite. It is created from a rolling 8-day land surface reflectance product, MOD09Q1N. The sensor resolution is 250 m, imagery resolution is 250 m, and the temporal resolution is an 8-day product which is updated daily.
References: NASA Earth Observatory – Measuring Vegetation; NASA Earthdata – New Vegetation Indices and Surface Reflectance Products Available from LANCE; NASA NEO – Vegetation Index
Karachi, the largest city of Pakistan received heavy monsoon rain August 30, 2017. The flood in Karachi due to heavy rains is the continuation of the similar monsoon related flooding crisis in the South East Asia region (India, Bangladesh etc.).The Flood map below is derived (subset of Karachi city ) from European Space Agency (ESA)’s Copernicus Program SENTINEL-1 Synthetic Aperture RADAR (SAR) image acquired on September 01, 2017. The green color in the map shows the flooded region.
The total rainfall derived from satellite data (GPM IMERG) for Karachi from August 29-31, 2017 is shown in Figure below:
Used for fire extent detection measurement, coastal and vegetation monitoring, land cover and land use mapping. WFI-2 (Amazonia-1) is the same instrument as WFI-2 (CBERS), however due differences in orbital altitude, they have different spatial resolution
Khyber Pakhtunkhaw (KPK) provincial government in Pakistan, govern by the Pakistan Tehreek-e-Insaf (PTI) party launched a reforestation program named “Billion tree Tsunami”, in 2015 (@btap2015). Imran Khan (@ImranKhanPTI), a cricket super star turned politician is the head of PTI party, Prime Minister of Pakistan and main driver behind this massive plantation campaign to turn degraded into forested land. The important aspect of this project is to monitor and identify the growth of these plantation regions. The remote sensing and Geographic Information Systems (GIS) tools provides this near-real-time (NRT) information at low cost compared to field campaigns.
The well known method to identify and monitor land surface changes using satellite remote sensing data utilizes a combination of band thresholding and optical indices (such as Normalized Difference Vegetation Index – NDVI) to separate land surface features. Applying this approach to two separate images by a given period of time allows changes in the extent of the area of interest to be identified. The atmospheric correction to the two images separated over time, extent of land can be compared. allowing for changes to be identified. this approach will provide an excellent alternative to field level change detection methods in challenging environments across Pakistan. We tested this approach for Bannu forest region (as shown in the Figure 2). The Figure 1 shows the land cover map of Bannu region for the year 2015.
Figure 1: Land Cover map of Bannu forest region (credit to ESA CCI)
Figure 2: Map of Bannu forest region (credit to Billion Tree Tsunami website)
Two Landsat 8 images are used for this study area acquired in June 01, 2013 and June 12, 2017. The Landsat 8 images are freely available from the United States Geological Survey (USGS) “EarthExplorer” (https://earthexplorer.usgs.gov/). The images were converted into surface reflectance before NDVI calculations using a standardised approach ( for detail check http://landsat.usgs.gov/CDR_LSR.php).
Figure 3: NDVI map of Bannu forest region derived from Landsat 8 image acquired on June 01, 2013.
Figure 4: NDVI map of Bannu forest region derived from Landsat 8 image acquired on June 12, 2017.
Figure 5: NDVI map in KMZ format of Bannu forest region derived from Landsat 8 image acquired on June 01, 2013 shown in google earth.
Figure 6: NDVI map in KMZ format of Bannu forest region derived from Landsat 8 image acquired on June 12, 2017 shown in google earth.
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Cyclone Mora has formed in the Bay of Bengal, will head towards highly populated Bangladesh by Tuesday. The data for this animation is used from NOAA-GFS model for period from 2017-05-28 to 2017-06-01.
ESA’s Climate Change Initiative in Glaciers_CCI Project, a team of researchers are using Copernicus Sentinel-1 SAR data with other optical data to monitor glaciers from space. The Negribreen glacier surge has been captured and shown in the animated gif (credit: ESA)
This post will provide an overview of the basics of Synthetic Aperture RADAR (SAR) and applications. The main topics discussed in the listed documents include: SAR basics, backscatter, geometry, interferometry, polarimetry, SAR data, data acquisition, available data sets/access to data, data analysis tools, future missions and SAR applications. Please do check Part 2 for more details.
What is RADAR? – RAdio Detection And Ranging
What is SAR? – Synthetic Aperture Radar – Synthetic Aperture Radar (SAR) is an active remote sensing technology that uses microwave energy to illuminate the surface. The system records the elapsed timeand energy of the return pulse received by the antenna (PDF).
The Canada Centre for Mapping and Earth Observation (CCMEO) is considered an international leader in the development and use of synthetic aperture radar or SAR sensors. From space, SAR can image the Earth’s surface through clouds and in total darkness. This makes it a tremendously useful sensor for monitoring Canada’s changing landmass and coastal zones. CCMEO scientists have worked with the Canadian Space Agency in the development of both RADARSAT 1 and RADARSAT 2 satellite missions. Their research has led to improved data quality through enhanced sensor design and post-launch calibration and validation activities.
This training manual introduces and explains Interferometric Synthetic Aperture Radar (InSAR), including applications for data from the Envisat ASAR sensor and how to combine Envisat and ERS images to produce interferograms and differential interferograms.
Active Microwave Remote Sensing provides cloud penetration and day-night imaging capability. These unique characteristics of C-band (5.35GHz) Synthetic Aperture Radar enable applications in agriculture, particularly paddy monitoring in kharif season and management of natural disasters like flood and cyclone.
Terra SAR-X / TanDEM-X
Launched in 2007/10, X-band quad polirzation, DLR/Astrium, Germany
Terra SAR-X (TSX) mission overview, spacecraft, references (Link) (Link to documents)
JAXA conducted research and development activities for ALOS-2 to improve wide and high-resolution observation technologies developed for ALOS in order to further fulfill social needs. These social needs include: 1) Disaster monitoring of damage areas, both in considerable detail, and when these areas may be large 2) Continuous updating of data archives related to national land and infrastructure information 3) Effective monitoring of cultivated areas 4) Global monitoring of tropical rain forests to identify carbon sinks.
Launched in 2007/10, 4 Satellites X-band dual polirzation, ASI/Italy
COSMO SkyMed offers high resolution X‐Band SAR (synthetic aperture radar) images. Despite its enormous potential, research investigating the possible uses in archaeology is still very scarce, especially of one which works solely with single date analysis starting with a single SAR scene (PDF).
Launched in 2013, S-band (HH or VV) polarization CRESDA/CAST/NRSCC, China
HJ-1A/B/C corresponding to environment and disaster monitoring and forecasting small satellite constellation A/B/C include two optical satellites – HJ-1A/B and one radar satellite HJ-1C, which can carry out large-scale, all-weather and 24h dynamic monitoring for ecological environment and disaster (Link).
Launched in 2014, X-band quad polarization, Ministry of Defense, Spain
PAZ is a Spanish radar technology satellite designed to address not only security and defense requirements, but also others of civilian nature. It is capable of daily taking more than 100 images of up to 25 cm resolution, both day and night, and independently of weather conditions (Link).
Launched in 2013, X-band dual polarization, KARI, Korea
The Argentina National Space Activities Commission (CONAE) launched a new Earth observationsatellite that will support disaster management efforts. SAOCOM 1A is the first of a constellation of two radar satellites. The remote sensing mission aims to provide timely information for disaster management as well as monitoring services for agriculture, mining and ocean applications.
The launch of the first dual-frequency synthetic aperture radar (SAR). The data collected by the L-band (produced by NASA) and S-band (produced by ISRO) SAR systems aboard the NISAR satellite and processed into cloud-free, ultra-sharp imagery will facilitate cutting-edge research into some of the planet’s most complex processes, including ecosystem disturbances, ice-sheet dynamics, earthquakes, tsunamis, volcanoes, and landslides.
RADARSAT Constellation Mission (RCM)
Will launch in 2019 three satellites, C-band quad compact polirzation, Canadian Space Agency (CSA) (Link)
SM2RAIN is a simple algorithm for estimating rainfall from soil moisture data.
The SM2RAIN code and the soil moisture derived rainfall data sets are freely available and can be downloaded here. The description of SM2RAIN and of its performance can be found in Brocca et al. (2013) and Brocca et al., 2014. (also here at IRPI-CNR website)
Here you can find two recent TALKS (IPWG 2016 – SMAP 2016) showing the latest results with the applications of SM2RAIN to multiple satellite soil moisture products (ASCAT, QUIKSCAT, RAPIDSCAT, AMSR-E, AMSR2, SMOS, and SMAP). In the paper by Massari et al. (2014) it is shown that SM2RAIN-derived rainfall from in-situ soil moisture observations even improves flood modelling (see also here, Italian media). In this paper by Brocca et al. (2015) the application of SM2RAIN to synthetic and in situ observations at several sites in Europe further underlines the robustness of the method. In Ciabatta et al. (2015) the integration of SM2RAIN with state-of-the-art products has provided significant improvements for rainfall estimation over the whole Italian territory. The latest application of SM2RAIN is for irrigation assessment, see here the project and a first overview paper.
Other scientist are using SM2RAIN in Mexico, in the Tibet Pleateau, and on a global scale. Click on the image below for the current list (PDF) of SM2RAIN papers with link to full text.