DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. (May 2020)

• Record Type: Journal Article
• Authors: Belda, Santiago; Pipia, Luca; Morcillo-Pallarés, Pablo; Rivera-Caicedo, Juan Pablo; Amin, Eatidal; De Grave, Charlotte; Verrelst, Jochem
• Is Part Of: Environmental modelling & software. Volume 127(2020)
• Publication Date: 2020-05
• Subjects: Gap-filling -- Machine learning -- Vegetation phenology -- Remote sensing; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104666
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Spatiotemporally gap-filled remote sensing data is required for phenology studies. Machine learning regression algorithms can serve to fill gaps of time series data. A GUI time series toolbox was developed to fill gaps and quantify phenology trends. DATimeS′ machine learning methods offer versatility for multi-year irregular data. GPR promising for time series reconstruction: flexible, accurate and uncertainties.