ArticleEnvironmental modelling & software : with environment data news2020
DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection.
Article in Environmental modelling & software : with environment data news, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Evaluating Sentinel-2 gap filling techniques for cloud removal and data reconstruction.Scientific reports · 2026Article
- Generation of High-Resolution Time-Series NDVI Images for Monitoring Heterogeneous Crop Fields.Sensors (Basel, Switzerland) · 2025Article
- Research on the application of remote sensing image super-resolution reconstruction techniques in crop phenology extraction.Frontiers in plant science · 2025Article
- Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China.Plants (Basel, Switzerland) · 2024Article
- Changes in Onset of Vegetation Growth on Svalbard, 2000-2020.Remote sensing · 2022Article
- Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI.Remote sensing · 2022Article
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.Remote sensing · 2022Article
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.Remote sensing · 2021Article
- Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery.Remote sensing · 2021Article
- Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.Remote sensing · 2021Article
- Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring.Agronomy (Basel, Switzerland) · 2020Article
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Authors and funding
7 authors.
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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.
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Registered trials
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