Open AccessBig Data from Space for Precision Agriculture Applications
Author(s) -
Francesca Bovolo,
Lorenzo Bruzzone,
Diego Fernàndez–Prieto,
Claudia Paris,
Yady Tatiana SolanoCorrea,
Espen Volden,
M. Zanetti
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/509/1/012004
Subject(s) - satellite , land cover , scale (ratio) , satellite constellation , remote sensing , big data , macro , computer science , agriculture , constellation , precision agriculture , focus (optics) , agricultural land , satellite imagery , land use , geography , cartography , data mining , engineering , civil engineering , archaeology , physics , optics , astronomy , programming language , aerospace engineering
This paper presents an approach for precision agriculture large scale applications based on the analysis of big data consisting in Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project [1]. To focus only on agricultural areas, images are first filtered based on a land cover (LC) map that is generated by updating available old maps by means of recent images. Then S2 SITS are used to analyse agricultural areas. Two macro challenges are therefore considered: (i) automatic update of LC maps and generation of agricultural areas mask; and (ii) unsupervised multi-temporal (MT) fine characterization of land plots.