Fuzzy clustering for the within-season estimation of cotton phenology.

ABSTRACT: Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of

SUBMITTER: Sitokonstantinou V 

PROVIDER: S-EPMC9994758 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature