Sentinel To Be Launched

Sentinel-2 Image of Plymouth from 2016. Data courtesy of Copernicus/ESA.

Sentinel-2B was launched at 01:49 GMT on the 7th March from Europe’s Spaceport in French Guiana. It’s the second of a constellation of optical satellites which are part of the European Commission’s Copernicus Programme.

Its partner Sentinel-2A was launched on the 23rd June 2015, and has been providing some stunning imagery over the last eighteen months like the picture of Plymouth above. We’ve also used the data within our own work. Sentinel-2B carries an identical Multispectral Imager (MSI) instrument to its twin with 13 spectral bands:

  • 4 visible and near infrared spectral bands with a spatial resolution of 10 m
  • 6 short wave infrared spectral bands with a spatial resolution of 20 m
  • 3 atmospheric correction bands with a spatial resolution of 60 m

With a swath width of 290 km the constellation will acquire data in a band of latitude extending from 56° South around Isla Hornos, Cape Horn, South America to 83° North above Greenland, together with observations over specific calibration sites, such as Dome-C in Antarctica. Its focus will be on continental land surfaces, all European islands, islands bigger than 100 square kilometres, land locked seas and coastal waters.

The satellites will orbit 180 degrees apart at an altitude of 786 km, which means that together they will revisit the same point on Earth every five days at the equator, and it may be faster for parts of southern Europe. In comparison, Landsat takes sixteen days to revisit the same point.

With all Copernicus data being made freely available to anyone, the short revisit time offers opportunities small and micro Earth Observation businesses to establish monitoring products and services without the need for significant investment in satellite data paving the way for innovative new solutions to the way in which certain aspects of the environment are managed. Clearly, five day revisits are not ‘real-time’ and the spatial resolution of Sentinel data won’t be suitable for every problem.There is joint work between the US and Europe, to have complementarity with Landsat-8, which has thermal bands, and allows a further opportunity for cloud-free data acquisitions. Also, commercial operators provide higher spatial resolution data.

At Pixalytics we’re supporters of open source in both software and imagery. Our first point of call with any client is to ask whether the solution can be delivered through free to access imagery, as this can make a significant cost saving and allow large archives to be accessed. Of course, for a variety of reasons, it becomes necessary to purchase imagery to ensure the client gets the best solution for their needs. Of course, applications often include a combination of free to access and paid for data.

Next’s week launch offers new opportunities for downstream developers and we’ll be interested to see how we can exploit this new resource to develop our products and services.

Reprocessing Data Challenges of Producing A Time Series

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

August 2009 Monthly Chlorophyll-a Composite; data courtesy of the ESA Ocean Colour Climate Change Initiative project

Being able to look back at how our planet has evolved over time, is one of the greatest assets of satellite remote sensing. With Landsat, you have a forty year archive to examine changes in land use and land cover. For in situ (ground based) monitoring, this is something that’s only available for a few locations, and you’ll only have data for the location you’re measuring. Landsat’s continuous archive is an amazing resource, and it is hoped that the European Union’s Copernicus programme will develop another comprehensive archive. So with all of this data, producing a time series analysis is easy isn’t it?

Well, it’s not quite that simple. There are the basic issues of different missions having different sensors, and so you need to know whether you’re comparing like with like. Although data continuity has been a strong element of Landsat, the sensors on Landsat 8 are very different to those on Landsat 1. Couple this with various positional, projection and datum corrections, and you have lots of things to think about to produce an accurate time series. However, once you’ve sorted all of these out and you’ve got your data downloaded, then everything is great isn’t it?

Well, not necessarily; you’ve still got to consider data archive reprocessing. The Space Agencies, who maintain this data, regularly reprocess satellite datasets. This means that the data you downloaded two years ago, isn’t necessarily the same data that could be downloaded today.

We faced this issue recently as NASA completed the reprocessing of the MODIS Aqua data, which began in 2014. The data from the MODIS Aqua satellite has been reprocessed seven times, whilst its twin, Terra, has been reprocessed three times.

Reprocessing the data can include changes to some, or all, of the following:

  • Update of the instrument calibration, to take account of current knowledge about sensor degradation and radiometric performance.
  • Appyling new knowledge, in terms of atmospheric correction and/or derived product algorithms.
  • Changes to parallel datasets that are used as inputs to the processing; for example, the meteorological conditions are used to aid the atmospheric correction.

Occasionally, they also change the output file format the data is provided in; and this is what has caught us out. The MODIS output file format has changed from HDF4 to NetCDF4 with the reason being that NetCDF is a more efficient, sustainable, extendable and interoperable data file format. A change we’ve known about for a long time, as it resulted from community input, but until you get the new files you can’t check and update your software.

We tend to use a lot of Open Source software, enabling our clients to carry on working with remote sensing products without having to invest in expensive software. The challenge is that it takes software provider time to catch up with the format changes. Hence, the software is unable to load the new files or the data is incorrectly read e.g., comes in upside down. Sometimes large changes, mean you may have to alter your approach and/or software.

Reprocessing is important, as it improves the overall quality of the data, but you do need to keep on top what is happening with the data to ensure that you are comparing like with like when you analyse a time series.