The confluence of rapidly growing streams of “free and open” satellite imagery at 10-30 m spatial resolution, expending libraries of sophisticated open source software components for geospatial data processing and the increase in publicly available open data sets is driving major changes in agricultural monitoring activities. In the next years, we can expect a scale step in derived crop area and status information at parcel level from the combined use of global sensors such as Landsat-8, Sentinel 1 and 2. In order to handle the unprecedented flow of such data into value adding agricultural mapping and monitoring applications, novel approaches need to be developed to ensure a globally consistent use in a “knowledge inference” context in support of, for instance, food security analysis. We demonstrate the use of Google Earth Engine (GEE) as a prototype environment that could possibly support such a context with 3 different examples.

LEMOINE Guido;  LEO Olivier; 

2020-04-27

INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS

JRC99011

https://ieeexplore.ieee.org/document/7326063,    https://publications.jrc.ec.europa.eu/repository/handle/JRC99011,   

10.1109/IGARSS.2015.7326063 (online),