Publications

Belda, S; Pipia, L; Morcillo-Pallares, P; Rivera-Caicedo, JP; Amin, E; De Grave, C; Verrelst, J (2020). DATimeS: A machine learning time series GUI toolbox for gap -filling and vegetation phenology trends detection. ENVIRONMENTAL MODELLING & SOFTWARE, 127, 104666.

Abstract
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.

DOI:
10.1016/j.envsoft.2020.104666

ISSN:
1364-8152