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Using Petabytes of Pixels to Create 3 New Images

by Rohit Kumar

In this blog, we’ll share how global composite satellite imageries are constructed by using Landsat 8, Sentinel-2A and Sentinel-1 satellites and how they appear.

Composites: A Perfect Summer’s Day

When we see satellite images in Google maps then we notice a strange thing that is the satellite data is cloud free and the greenery looks like summer vegetation. This view is made possible by combining multiple imageries of the same location into one image. This image is kind of synthetic, summary image and de facto a composite image.  The earth never looks so perfect cloud free and lush green but a composite imagery is the representation of such beautiful earth.

How multiple images can be combined into one image? A few different algorithms can be imagined that might work: using the most recent mage, an image with the healthiest vegetation or the image with least clouds. Besides, mathematical functions like the minimum, mean or median value across time on individual pixels.

These all are useful techniques which are mixed and matched depending on the application. One best suited composite for classifying land cover is different from best suited composite for monitoring crop yields or for detecting objects with computer vision algorithms.

Three Satellites, Three Composites

Above described method has been applied to create three new global composite images.

Landsat 8

A global RGB composite is made by using satellite imageries collected by Landsat 8 which is related to NASA’s Earth observation program. Effects of haze have been reduced by converting to “surface reflectance” and images are pan-sharpened to 15 meter pixel resolution.

Sentinel-2A

Sentinel-2A is the counterpart to Landsat 8 which was launched by European Space Agency (ESA) in June 2015. Global composite image of the “Red Edge” bands is produced by using all Sentinel-2A data. These bands lie between red and infrared and are invisible to the human eye. These bands are particularly useful for vegetation health. As far as, this Sentinel-2A composite is the first global Red Edge image.

      ©Descartes Labs, A view of Christchurch, New Zealand from Landsat 8

 

                            ©Descartes Labs, Agriculture in the Black Sea region of Russia from Sentinel-2A

Sentinel-1

Most of the satellites passively observe reflected sunlight, while Sentinel-1A and Sentinel-1B actively illuminate the earth with radar and detect the reflected signal. This active remote sensing technology is known as Synthetic Aperture Radar (SAR). SAR imageries are particularly sensitive to manmade features like building, road and ships. SAR has another very unique feature as its wavelength is long so it can penetrate and see through even clouds. Descartes Labs possibly created the 1st global composite and SAR image from Sentinel-1.

                                

                                        ©Descartes Labs, The San Francisco Bay Area from Sentinel-1

Summary

These global composite satellite imageries are constructed by using Landsat 8, Sentinel-2A and Sentinel-1 satellite imageries. Resultant satellite data is cloud free and the greenery appears like summer vegetation. In Landsat-8, effects of haze have been reduced by converting to “surface reflectance” and images are pan-sharpened to 15 meter pixel resolution. Sentinel-2A bands are particularly useful for vegetation health. Sentinel-1A actively illuminates the earth surface with radar and detects the reflected signal. It is sensitive to manmade features like building, road and ships etc.


SATPALDA is a privately owned company and a leading provider of satellite imagery and GeoSpatial services to the user community. Established in 2002, SATPALDA has successfully completed wide range of photogrammetric and Remote Sensing Projects.

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