How to turn satellite data to insights at scale

NetCarbon, a brand new french startup company, is offering farmers a free solution for measuring and monetizing their sequestered carbon to contribute towards carbon neutrality. This solution is relying on satellite data (Sentinel 2, Landsat 8 & PlanetScope) and open-source ecosystems such as the Pangeo software stack

 

The challenge in NetCarbon’s solution is the deployment of earth observation insights at scale. And be able to shift between cloud providers or on-premise architecture if needed. The best tool for us is up-to-now PANGEO.  

 

An example of our pangeo usage will be shown in the following three points.   

1°) Connection to satellite data / Extract 

2°) Processing satellite data at scale / Transform

3°) Saving the data within a data warehouse / Load

 

First, some of the building blocks to search for satellite data based on STAC will be shown. Moreover, the stackstac package will be tested to convert STAC into xarray, allowing researchers and companies to create their datacubes with all the metadata inside. 

 

The second part of the presentation will involve the computation layer. Indeed, computations algorithms like filtering by cloud cover, applying cloud mask, computing the land surface temperature, and applying an interpolation will be run. Land surface temperature is one data needed for the NetCarbon algorithm. The result of these previous steps will lead us to retrieve a dask computation graph. This computation graph will be run at scale within the cloud, based on Dask and Coiled. 

 

To conclude, the output of the processing part (spatial and temporal mean of the land surface temperature) will be displayed within a notebook and finally, the data will be loaded into a data warehouse (google bigquery). 

 

All the steps will be demonstrated in a reproducible notebook