1. Hands on Satellite Data to Monitor Biomass
11th Financial Risks International Forum
Olivier Tournaire
Hands on Satellite Data to Monitor Biomass

2. Introduction
Monitoring crops is of high importance to assess prices of major futures contracts on grain commodities
It is possible to rely on well-know data that drives the market, but, it is also possible to take advantage of unstructured datasets to build a predictive system (ML based)
What are these unstructured datasets?
How can we use them?
How can we link them all together?

3. The importance of geolocation
Introduction
The importance of geolocation
The Earth in not round, not even like a rugby ball: it constantly changes
This drives the need to have an up to date common set of « systems » to describe its shape: these are coordinate reference systems (CRS) also called spatial reference systems (SRS)
CRS can be local, regional or global and defines:
A map projection (i.e., how to locate a point on the Earth on a projected map)
Transformations between other CRS

4. A brief history of CRS
CRS were initially defined in 1985 by … the petroleum industry (ELF)
European Petroleum Survey Group (EPSG) have build a standard now adopted by a broad community of scientific users in various domains of expertise
It is now maintained by the International Association of Oil & Gas Producers (IOGP) and implemented in an ISO standard of the Open Geospatial Consortium (OGC)
Considered as a reference in all GIS softwares

5. Data introduction Data processing Data analysis Satellite Land use
Area of interest selection
Merging data together
Data analysis
Extracting information

6. Data introduction

7. Satellite data for Earth observation
Satellite data for Earth observation and monitoring
Since nearly 20 years, a constellation of satellites is deployed to monitor the Earth from space
Onboard Aqua and Terra, a MODIS sensor is embarqued
MODIS covers a wide range of spectral bands, and some of them are well suited for vegetation (Earth coverage) monitoring
Complete daily coverage of the Earth surface (same point at same time each day)
Several resolutions, from 250x250m to 1x1km / 0.05°x0.05°

8. MODIS – Worldwide VS local scales
At global scale
An image of 7200x3600 pixels
Longitudes: 360°/0.05°=7200 pixels
Latitudes: 180°/0.05°=3600 pixels
Remind that at the equator, 1°  110km
 One pixel: ≈5.5 sq. km
At local scale
An image of 4800x4800 covers a 1°x1° area
 One pixel: ≈250 sq. m

9. NDVI from MODIS (0.05°x0.05°)
𝑁𝐷𝑉𝐼= 𝑁𝑒𝑎𝑟 𝑖𝑛𝑓𝑟𝑎 𝑟𝑒𝑑 −𝑅𝑒𝑑 𝑁𝑒𝑎𝑟 𝑖𝑛𝑓𝑟𝑎 𝑟𝑒𝑑+𝑅𝑒𝑑

10. NDVI from MODIS (250mx250m)
Winter vs Summer

11. NDVI from MODIS (250mx250m)
2012 vs 2017

12. How can we monitor crops? We need to have an idea of land use
Know where crops are located (i.e. to avoid including urban areas, water surfaces, forests …)
We can also differentiate crops
This kind of data exist
US (yearly basis)
Europe (every 4/5 years)

13. Data processing

14. How to select the areas of interest?
Let’s simply use administrative boundaries
State scale
County scale
We can also be more specific and select only agricultural fields pixels
The choice of the scale depends on
The type of analysis
The data that we want to correlate
At this point, we want to use several (related) datasources to focus on some areas where NDVI analysis will be relevant
Satellite images (namely MODIS)
Landuse image (namely Crop monitor)
Administrative boundaries

15. OK, but how can we merge all these data?
Satellite images are not just images
They come with « hidden » information about their location
This is called geolocation, and that means that we know were the information is located on Earth
This is a really powerfull information as it allows to combine different data sources based on their location
Two main types of geolocated data
Raster, i.e. images: a grid / matrix of pixels
Vector, i.e. geometry

16. Processing can be automated with geospatial ETL

17. Now, we have build some masks
We can thus focus on crops in the NDVI images
Start to build time series (restricted to vegetation growing period, i.e. April to September
Building a NDVI time serie now simply consists in:
masking the MODIS data with the selected crops (for each date)
Aggregating the values inside the mask
Week 1
Week 2
Week n …

18. Some observations on the NDVI time serie
As expected it is highly seasonal
It is (almost) stationnary, i.e., there is no trend
We can spot in 2012 a much lower high value compared to the other years

19. Data analysis

20. To spot what appened in 2012, let’s have a closer look at this year
Instead of having a single value for each date, we can try to observe the NDVI values distribution
We can also build a reference NDVI distribution for each period by taking into account all years except 2012

21. Weekly NDVI distributions can be compared over years:
With 8 days delivery imagery, we have 45 sets of observations per year
For each date, and the selected area, we have around 1.2M pixels

23. Let’s have a closer look at the grains growing period
July
August
September

24. Conclusion
It appears that mixing several datasources, at different scales, of different type, provide a powerful way to monitor crops
The previous slides only give some clues on how these data can be used to extract relevant information. There exist much more datasets that can be used for prediction and related to price movements analysis
For instance, we can focus on major crops, which have tradable futures contracts
Data availability makes the process poweful and realistic in an industrial environment
Repeatable
Robust
Reliable
Apply the principles of nowcasting?
The data used are also perfectly suited for ML / DL

25. Thank you for your attention

27. What did we do?
We have just assemble, merge, and mix coherent data (thanks to their geolocation information)