A brief overview of satellite imagery for yield estimation in agroecosystem

enhancing the accurate and timely yield prediction of crops is a crucial component for food security and the sustainable development of the agricultural sector. For many years, crop yield prediction was primarily focused on individual optical satellite data classification, which can be vulnerable due to data limitations. Currently, multi-source satellite imagery, crop models, and machine learning techniques are increasing attention. The application of machine learning techniques provides precise crop yield prediction. This article presents an overview of remote sensing techniques for crop yield estimation, which is valuable for economic planning and ensuring food security.