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STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

Authors
  • Courault, Dominique1
  • Hossard, Laure2
  • Demarez, Valérie3
  • Dechatre, Hélène1
  • Irfan, Kamran1
  • Baghdadi, Nicolas4
  • Flamain, Fabrice1
  • Ruget, Françoise1
  • 1 INRAE-Avignon université, Domaine St Paul, Agroparc, Avignon, 84914, France , Avignon (France)
  • 2 INRAE, 2 place Pierre Viala, Montpellier, 34 060, France , Montpellier (France)
  • 3 CESBIO-UMR 5126, 18 avenue Edouard Belin, Toulouse, 31401, France , Toulouse (France)
  • 4 IRSTEA INRAE, Univ Montpellier, Montpellier, 34090, France , Montpellier (France)
Type
Published Article
Journal
Agronomy for Sustainable Development
Publisher
Springer-Verlag
Publication Date
Jul 06, 2021
Volume
41
Issue
4
Identifiers
DOI: 10.1007/s13593-021-00697-w
Source
Springer Nature
Keywords
Disciplines
  • Research Article
License
Yellow

Abstract

The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

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