In-season forecasting of within-field grain yield from Sentinel-2 time series data

In-season forecasting of within-field grain yield from Sentinel-2 time series data

Precise knowledge of cropland productivity is relevant for farmers to enable optimizing managing practices; particularly with the perspective of anticipating crop yield ahead of harvest. The current availability of high spatiotemporal resolution Sentinel-2 satellite data offers a unique opportunity...

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Bibliographic Details
Main Authors:	Eatidal Amin, Luca Pipia, Santiago Belda, Gregor Perich, Lukas Valentin Graf, Helge Aasen, Shari Van Wittenberghe, José Moreno, Jochem Verrelst
Format:	Article
Language:	English
Published:	Elsevier 2024-02-01
Series:	International Journal of Applied Earth Observations and Geoinformation
Subjects:	Sentinel-2 Crop yield forecasting Machine learning Gaussian process regression (GPR) Time series gap-filling Growing degree days (GDD)
Online Access:	http://www.sciencedirect.com/science/article/pii/S1569843223004600

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