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Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Authors
  • Garioud, Anatol
  • Giordano, Sébastien
  • Valero, Silvia
  • Mallet, Clément
Publication Date
Aug 05, 2019
Identifiers
DOI: 10.1109/Multi-Temp.2019.8866914
OAI: oai:HAL:hal-02387167v1
Source
HAL-SHS
Keywords
Language
English
License
Unknown
External links

Abstract

Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection .

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