Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive

被引：69

作者：

Wang, Sherrie ^{[1
,2
]}

Di Tommaso, Stefania ^{[2
]}

Deines, Jillian M. ^{[2
]}

Lobell, David B. ^{[2
]}

机构：

[1] Stanford Univ, Inst Computat & Math Engn, Stanford, CA 94305 USA

[2] Stanford Univ, Ctr Food Secur & Environm, Stanford, CA 94305 USA

来源：

SCIENTIFIC DATA | 2020年 / 7卷 / 01期

关键词：

UNITED-STATES; WINTER-WHEAT; YIELD; CROPLAND; SCIENCE; IMPACT; MODEL; NDVI;

D O I：

10.1038/s41597-020-00646-4

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download online

引用

页数：14