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DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection
Environmental Modelling & Software ( IF 4.552 ) Pub Date : 2020-02-29 , DOI: 10.1016/j.envsoft.2020.104666
Santiago Belda; Luca Pipia; Pablo Morcillo-Pallarés; Juan Pablo Rivera-Caicedo; Eatidal Amin; Charlotte de Grave; Jochem Verrelst

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.
更新日期:2020-03-02

 

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