6533b835fe1ef96bd129f6cf

RESEARCH PRODUCT

Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring

Pablo Morcillo-pallarésLuca PipiaSantiago BeldaJochem Verrelst

subject

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesImage processing02 engineering and technologycomputer.software_genre01 natural scienceslcsh:AgricultureKrigingTime series021101 geological & geomatics engineering0105 earth and related environmental sciences2. Zero hungerHyperparameterPixelSeries (mathematics)lcsh:SGaussian processes regressionSatellite Image Time SeriesData miningtime seriesSentinel-2optimizationAgronomy and Crop Sciencecomputercrop monitoringphenology indicators

description

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 &theta

yearjournalcountryeditionlanguage
2020-04-27Agronomy
10.3390/agronomy10050618http://dx.doi.org/10.3390/agronomy10050618