{"id":"doi:10.1016/j.rse.2021.112408","kind":"article","title":"Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach","authors":["Yuchi Ma","Zhou Zhang","Yanghui Kang","Mutlu Ozdogan A"],"author_count":4,"year":2021,"published":"2021-03-30","venue":"Remote Sensing of Environment","cited_by":292,"doi":"10.1016/j.rse.2021.112408","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.rse.2021.112408","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.rse.2021.112408","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3146049777","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Remote Sensing in Agriculture"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0).","topics":[{"slug":"ai-weather-climate","name":"AI Weather & Climate Modeling","domain_slug":"energy"}],"cited_in_dossiers":[]}