ArticleAgronomy (Basel, Switzerland)2020
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring.
Article in Agronomy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.Ecological informatics · 2025Article
- "How sweet are your strawberries?": Predicting sugariness using non-destructive and affordable hardware.Frontiers in plant science · 2023Article
- 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
- Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging.Remote sensing · 2021Article
- Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.Remote sensing · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To mitigate this computational burden, we propose to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.