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Dataset Open Access

Corn-Soy Data Layer

Wang, Sherrie; Di Tommaso, Stefania; Deines, Jillian; Lobell, David

Dataset Abstract:

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 on Zenodo.

 

Summary of Methods:

Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the pixel_qa band provided with Landsat Surface Reflectance products.

 

Map Legend:

  • 0 = outside study area
  • 1 = corn
  • 5 = soy
  • 9 = other crop
  • 255 = non-crop (masked by NLCD)

Values were chosen to be consistent with CDL values when possible.

 

Usage Notes:

We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R2 with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl.

Files (3.8 GB)
Name Size
1999_CSDL_v01.tif
md5:c79256f4f1308236db9878b006b37711
190.4 MB Download
2000_CSDL_v01.tif
md5:66974b543edfc1e8694f8a1e986e42bf
188.8 MB Download
2001_CSDL_v01.tif
md5:366fbd4a82072c63a3c7dda961e62616
188.5 MB Download
2002_CSDL_v01.tif
md5:c79256f4f1308236db9878b006b37711
190.4 MB Download
2003_CSDL_v01.tif
md5:8de271c42c39d4e2bb8d8fab73d3ad05
188.7 MB Download
2004_CSDL_v01.tif
md5:57048fb86c9a34b1d7eb506762cac05f
190.9 MB Download
2005_CSDL_v01.tif
md5:45ca1789a4d181c367100dbedfd3dd89
186.9 MB Download
2006_CSDL_v01.tif
md5:45f6f7971a64987e57f9db04b32cd009
185.4 MB Download
2007_CSDL_v01.tif
md5:a935f9cd63e3803d232c9b82980acfc2
185.3 MB Download
2008_CSDL_v01.tif
md5:92b508a89133375f0ee0fef18a9d8b1b
186.5 MB Download
2009_CSDL_v01.tif
md5:77eb00910ad6d1427061bee75a49fc65
188.6 MB Download
2010_CSDL_v01.tif
md5:13d98f7fa450d430e7fdb818b68d8fd3
185.8 MB Download
2011_CSDL_v01.tif
md5:058dcb9b6cf55e0eb286ec690cdbdb31
187.8 MB Download
2012_CSDL_v01.tif
md5:f32f8fe955ad4044b2c33e3606feeeda
194.1 MB Download
2013_CSDL_v01.tif
md5:0706b065d32481bfed925bfd3e3bcc8f
192.8 MB Download
2014_CSDL_v01.tif
md5:4c3c97e9a034e8b208bde3ca52714594
188.0 MB Download
2015_CSDL_v01.tif
md5:3bfb0ee31f954e7cf570fb74e53a63e3
187.3 MB Download
2016_CSDL_v01.tif
md5:f11fc7c7b89ae84665423a4a301fc4b0
186.3 MB Download
2017_CSDL_v01.tif
md5:8a973c237d8a09a6de0f9ce400670a86
188.1 MB Download
2018_CSDL_v01.tif
md5:3b2eb30c0958005bfa0ebe991103979e
190.2 MB Download
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