This comparison provides valuable insights and methodologies for remote sensing and agricultural researchers. It offers advanced techniques for crop yield estimation, vegetation classification, and yield variability assessment using a combination of Sentinel 2 and other multispectral imagery, machine learning, NDVI, and crop growth models. This interdisciplinary approach enhances the accuracy and efficiency of crop monitoring, benefiting both fields by advancing agricultural practices and remote sensing applications.
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