D. Lobell, Walter T. Dado, J. Deines, S. D. Tommaso, Sherrie Wang
{"title":"Landsat-Based Reconstruction of Corn and Soybean Yield Histories in the United States Since 1999","authors":"D. Lobell, Walter T. Dado, J. Deines, S. D. Tommaso, Sherrie Wang","doi":"10.1109/IGARSS39084.2020.9323792","DOIUrl":null,"url":null,"abstract":"The open Landsat archive provides a consistent view of the Earth's surface for much longer than most currently available agricultural datasets throughout the world. Here we present a summary of recent work to extend pixel-level (30 m) maps of corn and soy areas and yields back to 1999 across the entire Corn Belt of the United States. We find consistent performance back in time, as judged by comparison with county level statistics. The approaches presented here can be readily extended to other regions and to incorporate other sensors.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS39084.2020.9323792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The open Landsat archive provides a consistent view of the Earth's surface for much longer than most currently available agricultural datasets throughout the world. Here we present a summary of recent work to extend pixel-level (30 m) maps of corn and soy areas and yields back to 1999 across the entire Corn Belt of the United States. We find consistent performance back in time, as judged by comparison with county level statistics. The approaches presented here can be readily extended to other regions and to incorporate other sensors.