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

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?

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

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

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

Data introduction

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°

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

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

NDVI from MODIS (250mx250m) Winter vs Summer

NDVI from MODIS (250mx250m) 2012 vs 2017

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)

Data processing

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

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

Processing can be automated with geospatial ETL

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 …

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

Data analysis

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

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

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

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

Thank you for your attention

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