3 Ways Earth Observation is Tackling Food Security

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

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

One of the key global challenges is food security. A number of reports issued last week, coinciding with World Food Day on the 16th October, demonstrated how Earth Observation (EO) could play a key part in tackling this.

Climate change is a key threat to food security. The implications were highlighted by the U.S. Geological Survey (USGS) report who described potential changes to suitable farmland for rainfed crops. Rainfed farming accounts for approximately 75 percent of global croplands, and it’s predicated that these locations will change in the coming years. Increased farmland will be available in North America, western Asia, eastern Asia and South America, whilst there will be a decline in Europe and the southern Great Plains of the US.

The work undertaken by USGS focussed on looking at the impact of temperature extremes and the associated changes in seasonality of soil moisture conditions. The author of the study, John Bradford said “Our results indicate the interaction of soil moisture and temperature extremes provides a powerful yet simple framework for understanding the conditions that define suitability for rainfed agriculture in drylands.” Soil moisture is a product that Pixalytics is currently working on, and its intriguing to see that this measurement could be used to monitor climate change.

Given that this issue may require farmers to change crops, work by India’s Union Ministry of Agriculture to use remote sensing data to identify areas best suited for growing different crops is interesting. The Coordinated Horticulture Assessment and Management using geoinformatics (CHAMAN) project has used data collected by satellites, including the Cartosat Series and RESOURCESAT-1, to map 185 districts in relation to the best conditions for growing bananas, mangos, citrus fruits, potatoes, onions, tomatoes and chilli peppers.

The results for eight states in the north east of the country will be presented in January, with the remainder a few months later, identifying the best crop for each district. Given that India is already the second largest producer of fruit and vegetables in the world, this is a fascinating strategic development to their agriculture industry.

The third report was the announcement of a project between the University of Queensland and the Chinese Academy of Sciences which hopes to improve the accuracy of crop yield predictions. EO data with an improved spatial, and temporal, resolution is being used alongside biophysical information to try to predict crop yield at a field scale in advance of the harvest. It is hoped that this project will produce an operational product through this holistic approach.

These are some examples of the way in which EO data is changing the way we look at agriculture, and potential help provide improved global food security in the future.

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.

Supporting Soil Fertility From Space

Sentinel-2 pseudo-true colour composite from 2016 with a Kompsat-3 Normalized Difference Vegetation Index (NDVI) product from 2015 inset. Sentinel data courtesy of ESA/Copernicus.

Last Tuesday I was at the academic launch event for the Tru-Nject project at Cranfield University. Despite the event’s title, it was in fact an end of project meeting. Pixalytics has been involved in the project since July 2015, when we agreed to source and process high resolution satellite Earth Observation (EO) imagery for them.

The Tru-Nject project is funded via Innovate UK. It’s official title is ‘Tru-Nject: Proximal soil sensing based variable rate application of subsurface fertiliser injection in vegetable/ combinable crops’. The focus is on modelling soil fertility within fields, to enable fertiliser to be applied in varying amounts using point-source injection technology which reduces the nitrogen loss to the atmosphere when compared with spreading fertiliser on the soil surface.

To do this the project created soil fertility maps from a combination of EO products, physical sampling and proximal soil sensing – where approximately 15 000 georeferenced hyperspectral spectra are collected using an instrument connected to a tractor. These fertility maps are then interpreted by an agronomist, who decides on the relative application of fertiliser.

Initial results have shown that applying increased fertiliser to areas of low fertility improves overall yield when compared to applying an equal amount of fertiliser everywhere, or applying more fertiliser to high yield areas.

Pixalytics involvement in the work focussed on acquiring and processing, historical, and new, sub 5 metre optical satellite imagery for two fields, near Hull and York. We have primarily acquired data from the Kompsat satellites operated by the Korea Aerospace Research Institute (KARI), supplemented with WorldView data from DigitalGlobe. Once we’d acquired the imagery, we processed it to:

  • remove the effects of the atmosphere, termed atmospheric correction, and then
  • converted them to maps of vegetation greenness

The new imagery needed to coincide with a particular stage of crop growth, which meant the satellite data acquisition period was narrow. This led to a pleasant surprise for Dave George, Tru-Nject Project Manager, who said, “I never believed I’d get to tell a satellite what to do.’ To ensure that we collected data on specific days we did task the Kompsat satellites each year.

Whilst we were quite successful with the tasking the combination of this being the UK, and the fact that the fields were relatively small, meant that some of the images were partly affected by cloud. Where this occurred we gap-filled with Copernicus Sentinel-2 data, it has coarser spatial resolution (15m), but more regular acquisitions.

In addition, we also needed to undertake vicarious adjustment to ensure that we produced consistent products over time whilst the data came from different sensors with different specifications. As we cannot go to the satellite to measure its calibration, vicarious adjustment is a technique which uses ground measurements and algorithms to not only cross-calibrate the data, but also adjusts for errors in the atmospheric correction.

An example of the work is at the top, which shows a Sentinel-2 pseudo-true colour composite from 2016 with a Kompsat-3 Normalized Difference Vegetation Index (NDVI) product from 2015 inset. The greener the NDVI product the more green the vegetation is, although the two datasets were collected in different years so the planting within the field varies.

We’ve really enjoyed working with Stockbridge Technology Centre Ltd (STC), Manterra Ltd, and Cranfield University, who were the partners in the project. Up until last week all the work was done via telephone and email, and so it was great to finally meet them in-person, hear about the successful project and discuss ideas for the future.