Abstract. Remote sensing enables multitemporal information of the Earth’s surface and the dynamic processes that affect the environment. Given the considerable data availability, methods to summarize multitemporal datasets are needed to support the analysis. Our study introduces and compares methods to monitor temporal variations of water bodies based on multitemporal image composition. For this purpose, the presence of water at different dates is mapped applying the normalized difference water index using two encoding methods. The first one is based on the cumulative analysis of water in the pixel along time, and the second one uses the principle of binary encoding. The cumulative analysis helps to visualize more humid and dry areas, while binary encoding indicates the monthly variations of the lake surface, storing information about the dynamics of the phenomenon. The methods are compared using Landsat time series of Lake Poopó obtained between 2013 and 2019. The results showed that binary encoding allows detecting when and where severe droughts affect the water body and its recovery. In addition, it was possible to monitor the severe drought that affected the lake in 2016 and it was also noticed that its surface is still below the level registered before the drought in 2013.

中文翻译：

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使用 Landsat 8 Operational Land Imager (OLI) 数据监测水体时空变化的多时相图像编码

摘要。遥感使地球表面和影响环境的动态过程的多时信息成为可能。鉴于大量的数据可用性，需要总结多时数据集的方法来支持分析。我们的研究介绍并比较了基于多时相图像合成监测水体时间变化的方法。为此，使用两种编码方法应用归一化差异水指数映射不同日期的水的存在。第一个是基于像素中水随时间的累积分析，第二个使用二进制编码的原理。累积分析有助于可视化更潮湿和干燥的区域，而二进制编码表示湖面的每月变化，存储有关现象动态的信息。使用 2013 年至 2019 年期间获得的波波湖的 Landsat 时间序列对这些方法进行了比较。结果表明，二进制编码可以检测严重干旱影响水体及其恢复的时间和地点。此外，可以监测到 2016 年影响该湖的严重干旱，还注意到其表面仍低于 2013 年干旱前记录的水平。