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.

在领土层面评估水稻产量对于战略经济决策很重要。由于涉及的因素众多，包括农业实践、物候日历和环境背景，因此很难在区域尺度上评估空间和时间的产量变化。以十米分辨率（哨兵任务）获取的新遥感数据可以提供有关这种空间变异性的信息。因此，研究目标是评估 Sentinel-2 图像在监测水稻种植系统和从农场到区域尺度的产量方面的潜力。该方法同时考虑了观察和建模。在法国东南部的卡马格地区进行了深入的农民调查。新颖之处在于使用操作工具（BVNET 和 PHENOTB）来计算叶面积指数，每天从 2016 年和 2017 年为每个稻田采集的 44 张图像中插入该生物物理变量，并从时间剖面分析中推导出关键物候参数。考虑到来自遥感的生物物理变量，在空间上使用了 STICS 作物模型。我们测试了四种模拟策略，不同之处在于遥感信息与模型的整合强度。结果表明 (1) Sentinel-2 数据允许区分早稻和晚稻品种。(2) 在区域层面绘制的物候阶段可以更好地了解农民的农业实践。(3) 遥感数据与 STICS 作物模型的同化显着改善了产量估计，并提供了有关区域尺度观察到的空间变异性的有用信息。这是第一次将 Sentinel-2 数据与 STICS 作物模型一起用于评估卡马格地区农场和区域尺度的水稻产量。所提出的方法基于免费开放数据和免费访问模型，在其他环境中很容易重现。