This paper presents an approach for large-scale precise mapping of agricultural fields based on the analysis of 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. The goal is to design a flexible and automatic processing chain able to perform mapping in massive data. Here we focus on precision agriculture products generation at country level. In particular, the Country of study is Italy and the application goal is precision agriculture of single crop fields. To achieve this goal, two macro challenges are considered: (i) download and pre-processing of S2 SITS, and (ii) multi-temporal (MT) fine characterization of agricultural fields. Both challenges are addressed in an automatic way by exploiting and/or updating state-of-the-art methodologies. Promising results have been obtained over years 2017 and 2018 for Italy.

Large-Scale Precise Mapping of Agricultural Fields in Sentinel-2 Satellite Image Time Series / Solano-Correa, Yady Tatiana; Carcereri, Daniel; Bovolo, Francesca; Bruzzone, Lorenzo. - ELETTRONICO. - (2020), pp. 2288-2291. (Intervento presentato al convegno IEEE 2020 Int. Geoscience and Remote Sensing Symposium tenutosi a Waikoloa, HI, USA - Virtual Meeting nel 26 Sept.-2 Oct. 2020) [10.1109/IGARSS39084.2020.9323150].

Large-Scale Precise Mapping of Agricultural Fields in Sentinel-2 Satellite Image Time Series

Solano-Correa, Yady Tatiana;Carcereri, Daniel;Bovolo, Francesca;Bruzzone, Lorenzo
2020-01-01

Abstract

This paper presents an approach for large-scale precise mapping of agricultural fields based on the analysis of 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. The goal is to design a flexible and automatic processing chain able to perform mapping in massive data. Here we focus on precision agriculture products generation at country level. In particular, the Country of study is Italy and the application goal is precision agriculture of single crop fields. To achieve this goal, two macro challenges are considered: (i) download and pre-processing of S2 SITS, and (ii) multi-temporal (MT) fine characterization of agricultural fields. Both challenges are addressed in an automatic way by exploiting and/or updating state-of-the-art methodologies. Promising results have been obtained over years 2017 and 2018 for Italy.
2020
IEEE 2020 Int. Geoscience and Remote Sensing Symposium
Piscataway, NJ USA
IEEE
978-1-7281-6374-1
Solano-Correa, Yady Tatiana; Carcereri, Daniel; Bovolo, Francesca; Bruzzone, Lorenzo
Large-Scale Precise Mapping of Agricultural Fields in Sentinel-2 Satellite Image Time Series / Solano-Correa, Yady Tatiana; Carcereri, Daniel; Bovolo, Francesca; Bruzzone, Lorenzo. - ELETTRONICO. - (2020), pp. 2288-2291. (Intervento presentato al convegno IEEE 2020 Int. Geoscience and Remote Sensing Symposium tenutosi a Waikoloa, HI, USA - Virtual Meeting nel 26 Sept.-2 Oct. 2020) [10.1109/IGARSS39084.2020.9323150].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/293983
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