|Software, Tools, Libraries, Utilities etc.||Detail|
|SAR data processing|
|Polarimetric and polarimetric interferometric SAR (PolSAR / PolInSAR)||
|Interferometric synthetic aperture radar (InSAR)|
|Multitemporal/time series InSAR analysis|
|Performing Tropospheric Noise Corrections|
|Geospatial and Post-processing Analysis of SAR data|
|Projects on Github related to SAR||
|Image Processing Libraries||
|Training, Tutorials, Classes & Other Online Educational Material|
Link to Part 1
Brunt Ice Shelf
Source: Ice Shelves : Brunt
Two large cracks, Chasm 1 and the ‘Halloween Crack’, are growing on the Brunt Ice Shelf in Antarctica and when they meet, a large iceberg around 3 times the size of Leeds (1,594 km2) will break off.
Ice Speed on the Brunt Ice Shelf
Ice Shelf Calving
Source: Ice Shelves : Brunt
For inquiries of a scientific nature, please contact Dr. Anna Hogg, for inquiries related to web site operations please contact: Alan Muir at email@example.com.
McDonald Ice Rumples on Brunt Ice Shelf by Ade’s glacier gallery – 2019
Where the Brunt Ice Shelf, growing inexorably from the edge of the Antarctic Ice Sheet, meets a obstacle protruding from the sea floor, the McDonald Ice Rumples are formed – a region of ice deformation, rift generation and shelf fracture. The animation combines images every week or so from the ESA Copernicus Sentinel-1 satellites.
In recent years fracturing has increased and Brunt Ice Shelf will soon lose an area the size of Greater London to a large calving event. Although significant in the known history of this ice shelf, this is believed to be a natural part of the cycle of growth and decay (de Rydt et al., 2019).
Please feel free to download and use this animated GIF.
And here’s a compact version:
Canada’s RADARSAT-1 was the first operational radar-based Earth Observation Satellite. RADARSAT-1 acquired numerous data collections from 1995 to 2013. The historical value of this data is clear as it allows making comparisons using images of the same region acquired over the years: for example, to study climate change effects.
The Canadian Space Agency has recently released over 37 000 RADARSAT-1 images for public use, free of charge; you can download them here. In light of this initiative, we are evaluating the feasibility of opening up more RADARSAT-1 data over Canada and internationally. Please answer the survey before 2019-06-28, by (Clicking here) in order to help us better understand your needs and preferences with respect to RADARSAT-1 data.
If you have already filled out this survey, please ignore this email. If you know other people who would like to fill out the survey, please provide them with this link: https://forms.gle/QRpso98XpTj2fnHF6. For any questions please contact firstname.lastname@example.org
P.S.: Note that if you are experiencing difficulties in completing the document due to software issues, please let us know. As a first step, please try to first copy the following address into your internet browser: http://www4.asc-csa.gc.ca/metadata/default.aspx?FIId=8A1B6AA243585146E0530B0011ACACF9&CId=8A1B6AA247B85146E0530B0011ACACF9&lng=en-CA.
- Open data: over 36,000 historical RADARSAT-1 satellite images of the Earth now available to the public
- Earth Observation Data Management System
I was able to develop and test a code in R software using a simple pan sharpening formula (described here ) to create Pansharpened image of WorldView-2 (WV2) Multi-Spectral (MS) bands with high resolution Panchromatic (Pan) band. I have created a gif as shown in the figure above with Pan and MS ( a vegetation composite NIR2 in red, Yellow in green and Coastal in blue) images (data credit: esa).
Pansharpening is a process that merges/fuses high-resolution Pan data with medium-resolution MS data to create a high-resolution MS image (USGS).
WV2 is an imaging satellite of DigitalGlobe Inc., USA (a follow-on to WorldView-1 – WV1). WV2 sensor offers high resolution images in Pan 0.46 cm and unique MS 1.8 m at nadir. The MS bands are listed in the table below (credit: DigitalGlobe):
|Coastal Blue||400 – 450 nm||New band|
Absorbed by chlorophyll in healthy plants and aids in conducting vegetative analysis
Least absorbed by water, and will be very useful in bathymetric studies
Substantially influenced by atmospheric scattering and has the potential to improve atmospheric correction techniques
|Blue||450 – 510 nm||Identical to QuickBird|
Readily absorbed by chlorophyll in plants
Provides good penetration of water
Less affected by atmospheric scattering and absorption compared to the Coastal Blue band
|Green||510 – 580 nm||Narrower than the green band on QuickBird|
Able to focus more precisely on the peak reflectance of healthy vegetation
Ideal for calculating plant vigor
Very helpful in discriminating between types of plant material when used in conjunction with the Yellow band
|Yellow||585 – 625 nm||New band|
Very important for feature classification
Detects the “yellowness” of particular vegetation, both on land and in the water
|Red||630 – 690 nm||Narrower than the red band on QuickBird and shifted to longer wavelengths|
Better focused on the absorption of red light by chlorophyll in healthy plant materials
One of the most important bands for vegetation discrimination
Very useful in classifying bare soils, roads, and geological features
|Red-Edge||705 – 745 nm||New band|
Centered strategically at the onset of the high reflectivity portion of vegetation response
Very valuable in measuring plant health and aiding in the classification of vegetation
|NIR1||770 – 895 nm||Narrower than the NIR1 band on QuickBird to provide more separation between it and the Red-Edge sensor|
Very effective for the estimation of moisture content and plant biomass
Effectively separates water bodies from vegetation, identifies types of vegetation and also discriminates between soil types
|NIR2||860 – 1040 nm||New band|
Overlaps the NIR1 band but is less affected by atmospheric influence
Enables broader vegetation analysis and biomass studies
I have also created a gif as shown in the figure above with Pan and MS ( a shadow composite NIR2 in red, Red Edge in green and Yellow in blue) images to compare results.
Copernicus Sentinel 5P
The Copernicus Sentinel-5P (S5P) data is available (here) for download since July 2018 to monitor air quality and changes in ozone over Antarctica. The TROPOspheric Monitoring Instrument (TROPOMI) is the single sensor on board of the S5P satellite. The S5P is the first of the atmospheric composition Sentinels (operational satellite missions supporting the Copernicus programme), launched in 2017, for a nominal lifetime of 7 years. S5P, is a gap-filler and a preparatory programme covering products and applications for Sentinel-5. The S5P mission will fill the gap between the end of the Ozone Monitoring Instrument (OMI) and SCIAMACHY exploitation and the Sentinel-5 mission (credit: ESA).
This high spatial resolution data is useful for air pollution to locate origin of key pollutants (trace gases such as sulfur dioxide in the atmosphere) and finding pollution hotspots. Measurements of atmospheric ozone from the Copernicus S5P satellite are now being used in daily forecasts of air quality.
The S5P data in “pre ops” phase can be downloaded from the scihub https://scihub.copernicus.eu/ . I downloaded a level 2 NO2 file in netCDF format (.nc files).
search results are shown
The downloaded netcdf file first imported into “Panoply netCDF Visualization Software”
Copyright/Credit contains modified Copernicus Sentinel data (2018), processed by DLR/BIRA
One added value of Copernicus Atmosphere Monitoring Service (CAMS) ozone products compared to satellite total column retrievals is that CAMS provides 3D global fields. This allows structures like the Antrctic ozone hole to be viewed in a different way. This animation shows a cross section of the ozone layer (in partial pressure) over the South Pole from 1 July to 25 November 2018 and illustrates the development and recovery of the ozone hole.
Copyright/Credit Processed by CAMS/ECMWF
The reduction of ozone concentrations in the stratosphere and the formation of the ozone hole each year are caused by complex meteorological and chemical processes. Changes in the ozone between 7 July and 22 November 2018 are displayed here as a 3D rendered animation.
Copyright/Credit processed by CAMS/ECMWF
More Information available:
- Tropomi.eu (KNMI R&D Satellite Observations here )
- TROPOMI (the Netherlands here)
- European Space Agency (ESA) Sentinel-5 Precursor / TROPOMI here
- ESA Sentinel-5 Precursor launch campaign blog here
- Sentinel-5 Precursor Level-2 Product User manual here
- Research articles/presentations: link1, link2, link3, link4,
It’s #WorldEnvironmentDay and we’re looking at air pollution and how satellites like @CopernicusEU #Sentinel5P can be used to measure it! Explore this global map of Nitrogen Dioxide emissions and see how your country is doing here: https://t.co/0TwnGKWmhN #BeatAirPollution pic.twitter.com/rlB83gbRRc
— ESA EarthObservation (@ESA_EO) June 5, 2019
Well matched correlation seen between Fire counts(from #NASA #VIIRS(Red) #MODIS 05/13) Vs #Sentinel5 Nitrogen Dioxide on 13/05/19.— Ashim K. Mitra 🛰 (@ashimmitra) May 14, 2019
Biomass burning is a global phenomenon and can be an important contributor to poor air quality worldwide.@sentinel_hub @CopernicusEU @NASA #india pic.twitter.com/8oLLoOCzVx
⬅️El satélite #Sentinel5 registró altos niveles de CO en el día de ayer en #México (13/05/2019) debido a diversos incendios. 🛰️🔥— Iban Ameztoy (@i_ameztoy) May 14, 2019
➡️VIIRS #SNPP 🛰️@CONAFOR en su reporte diario anunciaba 144 -> https://t.co/UjBnnUWLOA pic.twitter.com/tyS9tYSLtB
We’re in Milan for #LPS19 and the latest science from Europe’s Sentinel satellites… like the new #Sentinel5P, which returns daily views of pollution. It shows nitrogen dioxide, mostly from fossil-fuel burning. This is data averaged for March 2019. pic.twitter.com/H2xXJZaNFX
— Jonathan Amos (@BBCAmos) May 13, 2019
The online portal will also help developing countries like Pakistan to build capacity and competence in their technical and administrative infrastructures. The maps in following figures shows:
- Chlorophyll-a CHL an essential pigment included in phytoplankton cells and therefore a measure of phytoplankton. The displayed CHL is calculated from total scattering and total organic absorption of water constituents. Unit is [µg/l].
- Harmful Algae Blooms (HAB) indicator shows possible areas affected by harmful algae blooms formed by cyanobacteria containing phycocyanin.
- Total Absorption (ABS) is the absorption of organic and anorganic of water components is provided as absorption unit in [1/m].
- Turbidity measures the degree to which light is being backscattered by particles in the water.Turbidity caused by scattering of particles is provided in Formazine Turbidity Unit [FTU].
Figure: Karachi a Metropolitan city located on the coastline of Sindh province in southern Pakistan, along a natural harbour on the Arabian Sea. (1) Turbidity and (2) Chlorophyll-a.
Figure: The Mangla Dam is a multipurpose dam located on the Jhelum River in the Mirpur District of Azad Kashmir. (1) Chlorophyll-a , (2) HAB indicator (probability), (3) Total Absorption, and (4) Turbidity.
Figure: Gomal Zam Dam is a multi-purpose gravity dam in South Waziristan Agency of Federally Administered Tribal Areas (FATA), Pakistan. (1) Chlorophyll-a , (2) HAB indicator (probability), (3) Total Absorption, and (4) Turbidity.
Figure: Ghazi-Barotha Hydropower Project is a 1,450 MW run-of-the-river hydropower connected to the Indus River about 10 km west of Attock in Punjab, Pakistan. (1) Chlorophyll-a , (2) HAB indicator (probability), (3) Total Absorption, and (4) Turbidity.
Note: Please read the information booklet for further information on the water quality products and to learn more about the validity range of the products. Products are generated independent on any form of ground truth data, and inter-comparable over the various resolutions provided. The Chlorophyll and HAB indicator may have site-specific limitations e.g. for extremely humid, calcareous, or ferruginous waters, and can be improved with local adaptations. General restrictions are caused by clouds, optical shallow waters, or undetected artefacts from e.g. cloud shadows.