Sentinel-2 Model
This week Sentinel-2A celebrated its 4th birthday, as it was launched on the 23rd June 2015 from French Guiana. It was followed by its twin, Sentinel-2B, which was launched almost two years later on the 7th March 2017. These two optical satellites are both in sun-synchronous orbits at an altitude of 786 km, but they are 180° apart which allows for the optimisation of coverage and temporal resolution.
They both carry an identical wide-swath high-resolution Multispectral Imager (MSI) instrument 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.
It’s estimated that the two satellites collect, store and download around 1.6 TB of data on each orbit, and the data has a variety of uses from the mission objectives including
- Land observation: Vegetation, soil and water cover, inland waterways and coastal areas
- Land use and change detection maps
- Providing support in generating land cover
- Disaster relief support
- Climate change monitoring
The fact that Copernicus data from the Sentinel satellites is free-to-access means that a lot of companies are building products and value chains based on the Sentinel data and Pixalytics is no different. We have products and services built using all the different Sentinel satellites, but specifically with Sentinel-2 data we have:
- Normalized difference vegetation index (NDVI)
- Leaf Area Index (LAI)
- Ocean colour and other water optical properties
On the topic of NDVI, we saw an interesting report on the problems of using NDVI with measuring elephant’s food abundance – I mean what’s not to like about reports covering elephants, NDVI and food! Published in last month’s edition of BioTropica, the Journal of the Association for Tropical Biology and Conservation was a paper entitled ‘NDVI is not reliable as a surrogate of forage abundance for a large herbivore in tropical forest habitat’ by Guatam et al.
The research showed that NDVI was not a reliable measure for the food availability for Asian elephants in a southern Indian tropical forest, because NDVI has a negative correlation with foodstuffs preferred by elephants – grasses, sedges and rushes.
The research was led by Hansraj Gautam from the Evolutionary and Organismal Biology Unit of Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) and contained a number of sampled tracks from 2011 in both the dry and wet seasons across multiple forest types. They found the abundance of grasses was low where NDVI is high, and the abundance is high when NDVI is low. This negative correlation is the influence of canopy cover and shrub population, which are both positively correlated for NDVI. This means where there is a dense canopy or shrub population NDVI cannot be relied upon as a good measure of the abundance of grasses, and therefore food availability for animals such as elephants.
NDVI s a robust approach, has a reduced sensitivity to errors such as atmospheric correction, and is easy to calculate. However, it also has its limitations which is why it’s important to also work on more complex approaches based on the radiative transfer modelling of light interacting with planets. This is happening both in the commercial and research companies but places greater reliance on the pre-processing of satellite data and the collection of suitable ground-based data.
It’s interesting to see researchers testing a popular and well-used indicator in specific circumstances. We don’t currently provide any services for elephants, but it is worth knowing about these discoveries in case we have opportunities in the future!
