Mapping all cocoa growing areas
Until recently, it has been impossible to accurately and cost-effectively map cocoa distribution of entire countries at farm level detail, because cocoa trees show up as forest in traditional satellite images, and the use of spatially detailed imagery is found expensive especially where smallholders are concerned .
New approach: combining local expertise, satellite data and AI
For our new approach we used freely available Sentinel-1 and 2 satellite imagery and built on scientific methods developed in Ghana by Benefoh et aland Asubonteng et al . While engaging local knowledge and feedback from local experts, we improved these methods with the combined use of imagery at 10m detail, advanced radar imagery that can better capture forest and cocoa structure , and new machine learning techniques that achieve higher accuracy than previously possible.
Cloud free observations guaranteed
There’s even more to it though. Before we could map cocoa, we needed to solve the problem of semi-persistent cloud and haze cover over West Africa. One way of dealing with this is to use Sentinel-1 radar that can ‘see’ through the clouds and haze. Secondly, we had to implement an automated cloud removal and tedious haze correction process to clean up all usable pixels of Sentinel-2 imagery collected every 5 days throughout the entire year.
Global scale mapping at farm level detail
Leveraging the power of cloud computing, powered by Google Compute Engine, we used over 400 Gb of Sentinel-1 and 2 data to generate an initial draft map of the entire cocoa landscape of Ghana for the year 2018 at 10m detail.