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A simplified method for water depth mapping over crops during flood based on Copernicus and DTM open data
Agricultural Water Management ( IF 6.7 ) Pub Date : 2022-04-11 , DOI: 10.1016/j.agwat.2022.107642
De Petris Samuele , Ghilardi Federica , Sarvia Filippo , Borgogno-Mondino Enrico

After an extreme rainy event agricultural fields can be submerged by water. Stagnant water can be generated by river’ flooding or by soil saturation causing different damage level to crops. In this work, the flood event occurred on 3rd October 2020 in NW Italy along the Sesia river was assessed with special concern about damages affecting rice crop fields. A method was proposed aimed at detecting flooded areas and giving an estimate of water depth (WD) based on free available Copernicus data (Sentinel-1 and Sentinel-2) and digital terrain model (DTM). In particular, Sentinel-1 pre- and post-event images were compared by differencing (ΔVV). ΔVV was processed at pixel level to detect submerged areas through the thresholding Otsu’s method. A simplified morphological analysis was then performed by DTM tessellation to map WD. A further step aimed at classifying submerged areas was achieved based on DTM and a proximity analysis, making possible to separate areas where water was related to soil saturation from areas where water was coming from the river. Corine Land Cover 2018 level-3 and NDVI from a Sentinel-2 pre-event image were used to map crops that were still to be harvested at the time of flood. These were the ones that were considered while estimating the potential economic loss. A total of 255 ha of rice that still to be harvested were submerged but only 211 ha were affected by river overflow. Using local rice yield and price the resulting economic loss was about 2,200,000 €.



中文翻译:

基于哥白尼和DTM开放数据的洪水期农作物水深测绘简化方法

极端降雨事件后,农田可能被水淹没。河流泛滥或土壤饱和会产生积水,从而对农作物造成不同程度的损害。在这项工作中,对 2020 年 10 月 3 日发生在意大利西北部 Sesia 河沿岸的洪水事件进行了评估,特别关注影响稻田的损害。提出了一种基于免费可用的哥白尼数据(Sentinel-1 和 Sentinel-2)和数字地形模型来检测洪水区域并估计水深 (WD) 的方法(DTM)。特别是,通过差分 (ΔVV) 比较了 Sentinel-1 事件前后的图像。ΔVV 在像素级进行处理,通过阈值 Otsu 方法检测淹没区域。然后通过 DTM 镶嵌进行简化的形态分析以绘制 WD。基于 DTM 和邻近分析实现了旨在对淹没区域进行分类的进一步步骤,从而可以将水与土壤饱和度相关的区域与水来自河流的区域分开。Corine Land Cover 2018 level-3 和来自 Sentinel-2 事件前图像的 NDVI 用于绘制洪水时仍有待收获的作物的地图。这些是在估计潜在经济损失时考虑的因素。仍有 255 公顷仍有待收割的稻米被淹没,但只有 211 公顷受到河流溢流的影响。使用当地的稻米产量和价格造成的经济损失约为 2,200,000 欧元。

更新日期:2022-04-11
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