| Autor: |
Wang S; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA. sherwang@stanford.edu.; Center on Food Security and the Environment, Stanford University, Stanford, CA, USA. sherwang@stanford.edu., Di Tommaso S; Center on Food Security and the Environment, Stanford University, Stanford, CA, USA., Deines JM; Center on Food Security and the Environment, Stanford University, Stanford, CA, USA., Lobell DB; Center on Food Security and the Environment, Stanford University, Stanford, CA, USA. |
| Abstrakt: |
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 30 m 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. |
