In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.

中文翻译：

---

对卡马格地区稻田应用的前哨 1 号和 2 号图像的农业实践进行评估，以开发农场类型


在全球变化的背景下，有效管理可用资源已成为最重要的主题之一，特别是对于可持续作物发展而言。许多问题涉及法国东南部卡马格稻作系统的演变，该系统在控制土壤盐分方面发挥着至关重要的作用。它们的表面积从 2010 年的 20 000 公顷大幅减少到 2014 年的 14 000 公顷。新哨兵卫星的到来使得评估这些作物的演变成为可能。本研究的目的是提出可操作的方法，根据多光谱数据的分类，准确评估主要农作物、水稻、小麦和草地的表面积；绘制农业实践图（播种和收获残留物燃烧）；并根据遥感数据计算的变量详细阐述农场类型，以更好地了解农业策略。对 2016 年和 2017 年以高空间分辨率（10 m）获取的密集时间序列哨兵图像进行了分析。土地利用分类获得了令人满意的精度，正确分类的田地率为 88%。根据研究年份的不同，估算播种日期的准确度从 8 到 12 天不等，并且正确识别了烧毁区域 (80%)。农场类型允许在地区一级聚集农场。