Flip-Sides of Soil Moisture

Soil Moisture changes between 19th and 25th August around Houston, Texas due to rainfall from Hurricane Harvey. Courtesy of NASA Earth Observatory image by Joshua Stevens, using soil moisture data courtesy of JPL and the SMAP science team.

Soil moisture is an interesting measurement as it can be used to monitor two diametrically opposed conditions, namely floods and droughts. This was highlighted last week by maps produced from satellite data for the USA and Italy respectively. These caught our attention because soil moisture gets discussed on a daily basis in the office, due to its involvement in a project we’re working on in Uganda.

Soil moisture can have a variety of meanings depending on the context. For this blog we’re using soil moisture to describe the amount of water held in spaces between the soil in the top few centimetres of the ground. Data is collected by radar satellites which measure microwaves reflected or emitted by the Earth’s surface. The intensity of the signal depends on the amount of water in the soil, enabling a soil moisture content to be calculated.

Floods
You can’t have failed to notice the devastating floods that have occurred recently in South Asia – particularly India, Nepal and Bangladesh – and in the USA. The South Asia floods were caused by monsoon rains, whilst the floods in Texas emanated from Hurricane Harvey.

Soil moisture measurements can be used to show the change in soil saturation. NASA Earth Observatory produced the map at the top of the blogs shows the change in soil moisture between the 19th and 25th August around Houston, Texas. The data is based on measurements acquired by the Soil Moisture Active Passive (SMAP) satellite, which uses a radiometer to measure soil moisture in the top 5 centimetres of the ground with a spatial resolution of around 9 km. On the map itself the size of each of the hexagons shows how much the level of soil moisture changed and the colour represents how saturated the soil is.

These readings have identified that soil moisture levels got as high as 60% in the immediate aftermath of the rainfall, partly due to the ferocity of the rain, which prevented the water from seeping down into the soil and so it instead remained at the surface.

Soil moisture in Italy during early August 2017. The data were compiled by ESA’s Soil Moisture CCI project. Data couresy of ESA. Copyright: C3S/ECMWF/TU Wien/VanderSat/EODC/AWST/Soil Moisture CCI

Droughts
By contrast, Italy has been suffering a summer of drought and hot days. This year parts of the country have not seen rain for months and the temperature has regularly topped one hundred degrees Fahrenheit – Rome, which has seventy percent less rainfall than normal, is planning to reduce water pressure at night for conservation efforts.

This has obviously caused an impact on the ground, and again a soil moisture map has been produced which demonstrates this. This time the data was come from the ESA’s Soil Moisture Climate Change Initiative project using soil moisture data from a variety of satellite instruments. The dataset was developed by the Vienna University of Technology with the Dutch company VanderSat B.V.

The map shows the soil moisture levels in Italy from the early part of last month, with the more red the areas, the lower the soil moisture content.

Summary
Soil moisture is a fascinating measurement that can provide insights into ground conditions whether the rain is falling a little or a lot.

It plays an important role in the development of weather patterns and the production of precipitation, and is crucial to understanding both the water and carbon cycles that impact our weather and climate.

Two New Earth Observation Satellites Launched

Artist's rendition of a satellite - paulfleet/123RF Stock Photo

Artist’s rendition of a satellite – paulfleet/123RF Stock Photo

Two new Earth observation satellites were launched last week from European Space Centre in Kourou in French Guyana, although you may only get to see the data from one. Venµs and OPTSAT-3000 were put into sun synchronous orbits by Arianespace via its Vega launch vehicle on the 1st August. Both satellites were built by Israel’s state-owned Israel Aerospace Industries and carry instruments from Israel’s Elbit Systems.

Venµs, or to give its full title of Vegetation and Environment monitoring on a New MicroSatellite, is a joint scientific collaboration between the Israeli Space Agency (ISA) and France’s CNES space agency.

Venµs is focussed on environmental monitoring including climate, soil and topography. Its aim is to help improve the techniques and accuracy of global models, with a particular emphasis on understanding how environmental and human factors influence plant health. The satellite is equipped with the VENµS Superspectral Camera (VSSC) that uses 12 narrow spectral bands in the Visible Near Infrared (VNIR) spectrum – ranging from 420nm wavelength up to 910 nm wavelength – to capture 12 simultaneous overlapping high resolution images which are then combined into a single image. The camera uses a pushbroom collection technique and has a spatial resolution of 5.3m and a swath size of 27.56 km.

Venµs won’t have full global coverage; instead there are 110 areas of interest around the world that includes forests, croplands and nature reserves. With a two day revisit time, during which time it completes 29 orbits of the planet. This means every thirtieth image will be collected over the same place, at the same time and with the same angle. This will provide high resolution imagery more frequently than is currently available from existing EO satellites. The consistency of the place, time and angle will help researchers better assess fine-scale changes on the land to improve our understanding of the:

  • State of the soil,
  • vegetation growth,
  • detection of spreading disease or contamination,
  • snow cover and glacial movements; and
  • sediment movement in coastal estuaries

A specific software algorithm has been developed for the mission to work with the different wavelengths to remove clouds and aerosols from the satellite’s imagery, giving clear images of the planet irrespective of atmospheric conditions.

The second satellite launched was the OPTSAT-3000 which is an Italian controlled optical surveillance satellite, which will operate in conjunction with the COSMO-SkyMed radar satellites giving Italy’s Ministry of Defence independent autonomous national Earth observation capability across optical and radar imagery.

This is a military satellite and so some of the details are difficult to verify. As mentioned earlier the instrument was made by Elbit systems, and the camera used usually offers a spatial resolution of around 0.5 m. However, it has been reported that the resolution will be much closer to 0.3m because the satellite is in a very low earth orbit of a 450 km.

OPTSAT-3000 will collect high resolution imaging of the Earth, it’s not clear at this stage whether any of the imagery will be made available for commercial/scientific use or purchase, although it is worth noting that COSMOS-SkyMed images are sold.

Two more Earth observation satellites launched shows that our industry keeps on moving forward! We’re really interested, and in OPTSAT’s case hopeful, to see the imagery they produce.

Have you read the top Pixalytics blogs of 2016?

Artist's rendition of a satellite - paulfleet/123RF Stock Photo

Artist’s rendition of a satellite – paulfleet/123RF Stock Photo

As this is the final blog of the year we’d like to take a look back over the past fifty-two weeks and see which blog’s captured people’s attention, and conversely which did not!

It turns out that seven of the ten most widely viewed blogs of the last year weren’t even written in 2016. Four were written in 2015, and three were written in 2014! The other obvious trend is the interest in the number of satellites in space, which can be seen by the titles of six of the ten most widely read blogs:

We’ve also found these blogs quoted by a variety of other web pages, and the occasional report. It’s always interesting to see where we’re quoted!

The other most read blogs of the year were:

Whilst only three of 2016’s blogs made our top ten, this is partly understandable as they have less time to attract the interest of readers and Google. However, looking at most read blogs of 2016 shows an interest in the growth of the Earth Observation market, Brexit, different types of data and Playboy!

We’ve now completed three years of weekly blogs, and the views on our website have grown steadily. This year has seen a significant increase in viewed pages, which is something we’re delighted to see.

We like our blog to be of interest to our colleagues in remote sensing and Earth observation, although we also touch on issues of interest to the wide space, and small business, communities.

At Pixalytics we believe strongly in education and training in both science and remote sensing, together with supporting early career scientists. As such we have a number of students and scientists working with us during the year, and we always like them to write a blog. Something they’re not always keen on at the start! This year we’ve had pieces on:

Writing a blog each week can be hard work, as Wednesday mornings always seem to come around very quickly. However, we think this work adds value to our business and makes a small contribution to explaining the industry in which we work.

Thanks for reading this year, and we hope we can catch your interest again next year.

We’d like to wish everyone a Happy New Year, and a very successful 2017!

Differences Between Optical & Radar Satellite Data

Ankgor Wat, Cambodia. Sentinel-2A image courtesy of ESA.

Ankgor Wat, Cambodia. Sentinel-2A image courtesy of ESA.

The two main types of satellite data are optical and radar used in remote sensing. We’re going to take a closer look at each type using the Ankgor Wat site in Cambodia, which was the location of the competition we ran on last week’s blog as part of World Space Week. We had lots of entries, and thanks to everyone who took part!

Constructed in the 12th Century, Ankgor Wat is a temple complex and the largest religious monument in the world. It lies 5.5 kilometres north of the modern town of Siem Reap and is popular with the remote sensing community due to its distinctive features. The site is surrounded by a 190m-wide moat, forming a 1.5km by 1.3km border around the temples and forested areas.

Optical Image
The picture at the top, which was used for the competition, is an optical image taken by a Multi-Spectral Imager (MSI) carried aboard ESA’s Sentinel-2A satellite. Optical data includes the visible wavebands and therefore can produce images, like this one, which is similar to how the human eye sees the world.

The green square in the centre of the image is the moat surrounding the temple complex; on the east side is Ta Kou Entrance, and the west side is the sandstone causeway which leads to the Angkor Wat gateway. The temples can be clearly seen in the centre of the moat, together with some of the paths through the forest within the complex.

To the south-east are the outskirts of Siem Reap, and the square moat of Angkor Thom can be seen just above the site. To the right are large forested areas and to the left are a variety of fields.
In addition to the three visible bands at 10 m resolution, Sentinel-2A also has:

  • A near-infrared band at 10 m resolution,
  • Six shortwave-infrared bands at 20 m resolution, and
  • Three atmospheric correction bands at 60 m resolution.

Radar Image
As a comparison we’ve produced this image from the twin Sentinel-1 satellites using the C-Band Synthetic Aperture Radar (SAR) instrument they carry aboard. This has a spatial resolution of 20 m, and so we’ve not zoomed as much as with the optical data; in addition, radar data is noisy which can be distracting.

Angkor Wat, Cambodia. SAR image from Sentinel-1 courtesy of ESA.

Angkor Wat, Cambodia. SAR image from Sentinel-1 courtesy of ESA.

The biggest advantage of radar data over optical data is that it is not affected by weather conditions and can see through clouds, and to some degree vegetation. This coloured Sentinel-1 SAR image is produced by showing the two polarisations (VV and VH i.e. vertical polarisation send for the radar signal and vertical or horizontal receive) alongside a ratio of them as red, green and blue.

Angkor Wat is shown just below centre, with its wide moat, and other archaeological structures surrounding it to the west, north and east. The variety of different landscape features around Angkor Wat show up more clearly in this image. The light pink to the south is the Cambodian city of Siem Reap with roads appearing as lines and an airport visible below the West Baray reservoir, which also dates from the Khmer civilization. The flatter ground that includes fields are purple, and the land with significant tree cover is shown as pale green.

Conclusion
The different types of satellite data have different uses, and different drawbacks. Optical imagery is great if you want to see the world as the human eye does, but radar imagery offers better options when the site can be cloudy and where you want an emphasis on the roughness of the surfaces.

Remote Sensing and the DIKW Pyramid

DIKW PyramidSatellite remote sensing industry is evolving and anyone working in it needs to become familiar with the Data, information, Knowledge, Wisdom (DIKW) pyramid as this is one map, albeit simplistic, of the industry’s and our current journey.

Historically, satellite data was either sold as the original image or with a small amount of processing undertaken. If anyone wanted to do anything beyond basic processing, they had to do it themselves. However, things are changing.

According to a recent Euroconsult report, at least 3,600 small satellites will be launched over the next decade. The United Nations Office on Outer Space Affairs only lists 7,370 objects that have ever been launched into space, of which only 4,197 are still in orbit. We’re increasing the number of objects orbiting the Earth by 85% by smallsats alone, larger satellites will add even more.

The volume, variety and speed of this data collected by these satellites will present a step change not only in the type of applications companies will be able to offer, but, crucially, also in customer expectations – more and more they will be looking for added value.

One way of considering this is through the DIKW pyramid, which can be seen at the top of the blog, it’s credited to American organisational theorist Russell Ackoff in 1989, building on the ideas of Milan Zeleny two years earlier.

A simple summary of the pyramid starts with the collection of data which means nothing in its own right, it is simply data. Information is derived from data by asking the who, what, where, when and how questions. Knowledge is information to which expert skills and experience have been added to create more value – which is more profitable in a business context. Finally, wisdom is understanding what actions to take based on the knowledge you’ve gained.

Applying this to satellite remote sensing for agriculture, one example might be: data is the satellite data/image of the field. Information is knowing when the image was taken leading to where in the growing cycle the crop was. Knowledge is applying scientific algorithms to know the soil moisture, how much nutrients are in the soil or how much vegetation is present in various parts of the field. Wisdom is knowing what nutrients and fertilizers to apply, based on the knowledge gained, to improve crop yields.

A lot of Earth observation products are at the data or information level, with a few at the knowledge level, and even fewer at the wisdom level. Customers more and more want wisdom products, and they aren’t that interested in what was required to create them. When you add to this the additional types of geospatial information, e.g., optical and radar used together alongside airborne and in-field ground based measurements, the variety of open datasets and the new science and technological breakthroughs, things are going to look very different, very quickly.

We’d accept that the DIKW isn’t a perfect tool, nor a perfect representation of our industry, but it is simple, indicative and worth thinking about. We wrote about our intention to create products in an earlier blog. We’re a long way from the wisdom sector, but are hoping to be firmly within the knowledge sector and collaborating to create wisdom. It’s not easy and some companies will find it harder to do than others, but is going to be the future. How are you preparing?

Two Fantastic Remote Sensing Innovations

Aberdeenshire (Scotland) January 2016 flooding captured by Sentinel-1; Data courtesy of Copernicus/ESA

Aberdeenshire (Scotland) January 2016 flooding captured by Sentinel-1

Two academic remote sensing research announcements caught our eye this week. To be fair most remote sensing announcements catch our eye, but these two were intriguing as they are repurposing remote sensing techniques.

Remote Sensing the Human Body
Researchers at Kyoto University Centre of Innovation have developed a system based on spread-spectrum radar technology to remotely sense signals from the human body. They have focussed on heartbeats, although they acknowledge that other elements such as breathing and movement are also measured by the system. It uses a unique signal analysis algorithm to extract the beats of the heart from the radar signals, and then calculates the intervals to give the heartbeat.

Anyone who has ever needed to wear a Holter monitor for twenty-four or forty-eight hours will appreciate the advantage of having measurements taken remotely, in real time. In addition, under controlled conditions, the system has worked with a similar accuracy to an electrocardiographs (ECG). This will be music to the ears of regular ECG takers who know how much removing those sticky electrode pads can hurt!

This system is still at an early developmental stage and further testing and validation is necessary, but it offers a potential new use of remote sensing technology.

Remote Sensing & Social Media
Researchers from Pennsylvania State University have led a project developing an innovative way of combining social media and remote sensing. The research was undertaken on a flood in Boulder, Colorado in September 2013 with a particular focus on urban locations.

The team identified over 150,000 flood related tweets and used a cloud-based geo-social networking application called CarbonScanner, from The Carbon Project, to cluster the pictures from Twitter and Flickr to identify flooding hotspots. These were then used to obtain optical data, in this case from the high resolution commercial satellite Worldview 2 and the lower resolution, but freely available, Landsat 8.

A machine learning algorithm was developed to perform a semi-automated classification to identify individual pixels that contained water. As the data was optical it used the near infrared band as, due to its strong absorption, water is easily distinguishable from soil and vegetation. The researchers believe that this methodology has the potential to give emergency teams near real-time data, which could make live-saving differences to their work.

This is a particularly interesting development for us, given our current work on flood-mapping using synthetic aperture radar (SAR) data as part of the Space for Smarter Government Programme.

These two current examples show that remote sensing is an exciting, innovative and developing field, and one that is not solely related to Earth observation.

SMAP ready to map!

Artist's rendering of the Soil Moisture Active Passive satellite.  Image credit: NASA/JPL-Caltech

Artist’s rendering of the Soil Moisture Active Passive satellite.
Image credit: NASA/JPL-Caltech

On the 31st January NASA launched their Soil Moisture Active Passive satellite, generally known by the more pronounceable acronym SMAP, aboard the Delta 2 rocket. It will go into a near polar sun-synchronous orbit at an altitude of 685km.

The SMAP mission will measure the amount of water in the top five centimetres of soil, and whether the ground is frozen or not. These two measurements will be combined to produce global maps of soil moisture to improve understanding of the water, carbon and energy cycles. This data will support applications ranging from weather forecasting, monitoring droughts, flood prediction and crop productivity, as well as providing valuable information to climate science.

The satellite carries two instruments; a passive L-Band radiometer and an active L-Band synthetic aperture radar (SAR). Once in space the satellite will deploy a spinning 6m gold-coated mesh antenna which will measure the backscatter of radar pulses, and the naturally occurring microwave emissions, from off the Earth’s surface. Rotating 14.6 times every minute, the antenna will provide overlapping loops of 1000km giving a wide measurement swath. This means that whilst the satellite itself only has an eight day repeat cycle, SMAP will take global measurements every two to three days.

Interestingly, although antennas have previously been used in large communication satellites, this will be the first time a deployable antenna, and the first time a spinning application, have been used for scientific measurement.

The radiometer has a high soil moisture measurement accuracy, but has a spatial resolution of only 40km; whereas the SAR instrument has much higher spatial resolution of 10km, but with lower soil moisture measurement sensitivity. Combining the passive and active observations will give measurements of soil moisture at 10km, and freeze/thaw ground state at 3km. Whilst SMAP is focussed on provided on mapping Earth’s non-water surface, it’s also anticipated to provide valuable data on ocean salinity.

SMAP will provide data about soil moisture content across the world, the variability of which is not currently well understood. However, it’s vital to understanding both the water and carbon cycles that impact our weather and climate.