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Can I read DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection on EtoBox?
DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection by Belda, Santiago; Pipia, Luca; Morcillo-Pallarés, Pablo; Rivera-Caicedo, Juan Pablo; Amin, Eatidal; de Grave, Charlotte; Verrelst, Jochem is a Environmental Science article available to read on EtoBox.
What is DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection about?
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.
Who reads DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Belda, Santiago; Pipia, Luca; Morcillo-Pallarés, Pablo; Rivera-Caicedo, Juan Pablo; Amin, Eatidal; de Grave, Charlotte; Verrelst, Jochem
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 1364-8152)
- Published
- 2020
- Language
- EN
- Field
- Environmental Science (Physical Sciences)