Belda Santiago, Pipia Luca, Morcillo-Pallarés Pablo, Rivera-Caicedo Juan Pablo, Amin Eatidal, De Grave Charlotte, Verrelst Jochem
Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain.
CONACYT-UAN, Secretariat of Research and Postgraduate, C/3, 63173, Tepic, Mexico.
Environ Model Softw. 2020 Mar 10;127. doi: 10.1016/j.envsoft.2020.104666. eCollection 2020 May.
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，由于云层覆盖会存在缺失值。因此，需要采用填补缺口的解决方案来准确表征作物物候。本文介绍的时间序列分解与分析软件（DATimeS）扩展了已有的时间序列插值方法，采用了多种先进的机器学习拟合算法（如高斯过程回归：GPR），这些算法对于重建多季节植被时间模式特别有效。DATimeS作为一款强大的图像时间序列软件可免费获取，它能生成无云合成地图，并从常规或不规则卫星时间序列中捕捉季节性植被动态。本文描述了DATimeS的主要特性，并提供了一个使用西班牙某地区哨兵 - 2叶面积指数时间序列数据的示范案例。结果表明，GPR是具有最准确的缺口填补性能及相关不确定性的最优拟合算法。DATimeS进一步量化了多个作物季节之间的叶面积指数波动，并为特定作物类型提供了物候指标。
