Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regular or irregular satellite time series. This work describes the main features of DATimeS, and provides a demonstration case using Sentinel-2 Leaf Area Index time series data over a Spanish site. GPR resulted as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties. DATimeS further quantified LAI fluctuations among multiple crop seasons and provided phenological indicators for specific crop types.

光学遥感数据通常是不连续的，由于云层覆盖而存在缺失值。因此，需要填补空白的解决方案来准确表征作物物候。这里介绍的时间序列分解和分析软件（DATimeS）通过多种先进的机器学习拟合算法（例如高斯过程回归：GPR）扩展了已建立的时间序列插值方法，对于重建多季节植被时间模式特别有效。 DATimeS 作为一款功能强大的图像时间序列软件免费提供，可生成无云复合地图并从规则或不规则的卫星时间序列捕获季节性植被动态。这项工作描述了 DATimeS 的主要功能，并提供了使用西班牙站点上的 Sentinel-2 叶面积指数时间序列数据的演示案例。探地雷达是一种最佳拟合算法，具有最准确的间隙填充性能和相关的不确定性。 DATimeS 进一步量化了多个作物季节之间的 LAI 波动，并提供了特定作物类型的物候指标。