Satellite image time-series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time-series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time-series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land-use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state-of-the-art articles focusing on the applications, techniques, and research trends for ITSF.

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卫星图像时间序列预测文献综述：遥感方法与应用

卫星图像时间序列是由遥感图像产生的时间序列；它们通常对应于从这些图像中提取的特征或指标。随着遥感图像和处理此类数据的新方法的不断增加，图像时间序列方法已广泛用于评估时间模式检测、监测、分类、对象检测和特征估计。由于时间序列的研究范围很广，因此本文重点分析与预测图像时间序列的一个或多个属性的值相关的文章。图像时间序列预测（ITSF）问题出现在不同的学科中；大多数关注重点是通过利用自然资源促进可持续发展和尽量减少危险自然现象的致命性来提高生活质量。科学家根据应用使用不同的工具或方法来解决这些问题。本综述分析了该领域领先的最新贡献，并按应用领域和解决方案方法对它们进行分组。我们的研究结果表明，人工神经网络、回归树、支持向量回归和元胞自动机是 ITSF 最常用的方法。应用领域解决了这个问题，如可再生能源、农业和土地利用变化。本研究检索并分析了图像时间序列预测近期活动的相关信息，生成了 2009 年至 2021 年发表的该领域最相关文章的可重复列表。据作者所知，这是第一篇介绍和分析图像时间序列预测的综述。最相关的最新文章的可重复列表，重点关注 ITSF 的应用、技术和研究趋势。