Imperial College London Researcher Provides New Data on Agriculture (Fast Dynamic Time Warping and Hierarchical Clustering with Multispectral and Synthetic Aperture Radar Temporal Analysis for Unsupervised Winter Food Crop Mapping).

Researchers at Imperial College London have developed a new method, FastDTW-HC, combining Fast Dynamic Time Warping and Hierarchical Clustering, to classify winter food crop varieties without the need for ground truth data. This approach utilizes Earth Observation data, including multispectral and Synthetic Aperture Radar data, to analyze phenological patterns of crops like barley, wheat, and rapeseed in Norfolk, UK. The study aims to extend this analysis to a regional scale in the future, offering a sustainable prediction of agricultural yields. For more information, readers can access the full article in Agriculture journal. [Extracted from the article]