{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:02:34Z","timestamp":1781269354131,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000139","name":"U.S. Environmental Protection Agency","doi-asserted-by":"publisher","award":["EPA Order No. EP-15-C-000044"],"award-info":[{"award-number":["EPA Order No. EP-15-C-000044"]}],"id":[{"id":"10.13039\/100000139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) provides spatially explicit information about crop production area and has served as a prevalent data source for characterizing cropland change in the U.S. in the last decade. Understanding the accuracy of the CDL is paramount because of the reliance on it for management and policy making. This study examined the characteristics of the CDL from 2007 to 2017 using comparisons to other USDA datasets. The results showed when examining the cropland area for the same year, the CDL produced comparable trends with other datasets (R2 &gt; 0.95), but absolute area differed. The estimated area of cropland changes from 2007 to 2012, 2008 to 2012 and 2012 to 2017 varied from weak to moderate correlation between the CDL and the tabular data (R2 = 0.005~0.63). Differences in area of cropland change varied widely between data sources with the CDL estimating much larger change area. A series of image processing techniques designed to improve the confidence in cropland change estimated using the CDL reduced the area of estimated cropland change. The techniques also, unexpectedly, lowered the correlation in change estimated between the CDL and the tabular datasets. Estimated land cover change area varied widely based on analyses applied and could reverse from increasing to declining area in cropland. Further analyses showed unlikely change scenarios when comparing different year combinations. The authors recommend the CDL only be used for land cover change analysis if the error can be estimated and is within change estimates.<\/jats:p>","DOI":"10.3390\/ijgi10050281","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T04:30:11Z","timestamp":1619670611000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Examining the Characteristics of the Cropland Data Layer in the Context of Estimating Land Cover Change"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0778-8550","authenticated-orcid":false,"given":"Ken","family":"Copenhaver","sequence":"first","affiliation":[{"name":"CropGrower LLC, Tampa, FL 33606, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Hamada","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory, Environmental Science Division, Lemont, IL 60439, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Mueller","sequence":"additional","affiliation":[{"name":"Energy Resources Center, Bio-Fuels and Bio-Energy Program, College of Engineering, Chicago Campus, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2065-5106","authenticated-orcid":false,"given":"Jennifer B.","family":"Dunn","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biological Engineering, Evanston Campus, Northwestern University, Evanston, IL 60208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/S2095-3119(16)61396-5","article-title":"Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers","volume":"16","author":"Boryan","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_3","unstructured":"Johnson, D., Mueller, R., and Willis, P. (October, January 2). The utility of the Cropland Data Layer for monitoring US grassland extent. Proceedings of the 3rd Biennial Conference on the Conservation of America\u2019s Grasslands, Washington, DC, USA."},{"key":"ref_4","unstructured":"(2021, February 10). Solutions, Decision Innovation. Multi-State Land Use Study: Estimated Land Use Changes 2007\u20132012. Urbandale, IA. 2013; 50322. Available online: http:\/\/www.decision-innovation.com\/webres\/File\/docs\/130715%20Multi-State%20Land%20Use%20Report.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10980-013-9947-0","article-title":"Agricultural expansion: Land use shell game in the U.S. Northern Plains","volume":"29","author":"Johnston","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alemu, W.G., Henebry, G.M., and Melesse, A.M. (2019). Land Surface Phenologies and Seasonalities in the US Prairie Pothole Region Coupling AMSR Passive Microwave Data with the USDA Cropland Data Layer. Remote. Sens., 11.","DOI":"10.3390\/rs11212550"},{"key":"ref_7","unstructured":"Augustine, D., Davidson, A., Dickinson, K., and Van Pelt, B. (2019). Thinking Like a Grassland: Challenges and Opportunities for Biodiversity Conservation in the Great Plains of North America. Rangel. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1353\/gpr.2016.0019","article-title":"Plowprint: Tracking Cumulative Cropland Expansion to Target Grassland Conservation","volume":"26","author":"Gage","year":"2016","journal-title":"Great Plains Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s10980-019-00806-x","article-title":"Effects of cropland encroachment on prairie pothole wetlands: Numbers, density, size, shape, and structural connectivity","volume":"34","author":"Johnston","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.biocon.2017.02.028","article-title":"Land cover dynamics influence distribution of breeding birds in the Great Plains, USA","volume":"209","author":"Scholtz","year":"2017","journal-title":"Biol. Conserv."},{"key":"ref_11","unstructured":"Wang, T., Ayesh, A., Hennessy, D., and Feng, H. (2021, February 10). Cropland Reflux: Trends in and Locations of Land Use Change in the Dakotas, 2007 to 2012 and 2012 to 2017. 2018 July 11. (No. 1354-2018-4564). Available online: http:\/\/dx.doi.org\/10.22004\/ag.econ.274938."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1016\/j.envpol.2019.06.054","article-title":"Impact of land cover on groundwater quality in the Upper Floridan Aquifer in Florida, United States","volume":"252","author":"Bawa","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.biombioe.2019.03.003","article-title":"Biofuel impact on food prices index and land use change","volume":"124","author":"Shrestha","year":"2019","journal-title":"Biomass Bioenergy"},{"key":"ref_14","unstructured":"(2020, September 10). CropScape. Available online: https:\/\/nassgeodata.gmu.edu\/CropScape\/."},{"key":"ref_15","unstructured":"Dunn, J., Mueller, S., and Eaton, L. (2021, February 20). Comments on: Cropland Expansion Outpaces Agricultural and Biofuel Policies in the United States. Argonne National Lab Publication, Available online: https:\/\/greet.es.anl.gov\/publication-comments-cropland-expansion.2015."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1002\/bbb.1750","article-title":"Measured extent of agricultural expansion depends on analysis technique","volume":"11","author":"Dunn","year":"2017","journal-title":"Biofuels Bioprod. Biorefining"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"E2863","DOI":"10.1073\/pnas.1306646110","article-title":"Cultivated hay and fallow\/idle cropland confound analysis of grassland conversion in the Western Corn Belt","volume":"110","author":"Kline","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","first-page":"32","article-title":"Use of Remote Sensing to Measure Land cover Change from Biofuel Production","volume":"2","author":"Mueller","year":"2009","journal-title":"Bull. Program Arms Control Disarm. Int. Secur."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.2134\/agronj15.0152","article-title":"Land-Use Change Impact on Soil Sustainability in a Climate and Vegetation Transition Zone","volume":"107","author":"Reitsma","year":"2015","journal-title":"Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lark, T.J., Salmon, J.M., and Gibbs, H.K. (2021, February 10). Cropland Expansion Outpaces Agricultural and Biofuel Policies in the United States. Available online: https:\/\/iopscience.iop.org\/article\/10.1088\/1748-9326\/10\/4\/044003\/meta.","DOI":"10.1088\/1748-9326\/10\/4\/044003"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, C., Di, L., Lin, L., and Guo, L. (2019, January 16\u201319). Extracting Trusted Pixels from Historical Cropland Data Layer Using Crop Rotation Patterns: A Case Study in Nebraska, USA. Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2019.8820236"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, P., and Congalton, R.G. (2019, January 16\u201319). Comparing the impact of mapping error on the representation of landscape pattern on upscaled agricultural maps. Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2019.8820256"},{"key":"ref_23","unstructured":"(2020, September 10). Ag Data Commons, Available online: https:\/\/data.nal.usda.gov\/dataset\/cropscape-cropland-data-layer."},{"key":"ref_24","unstructured":"(2020, September 10). USDA Census of Agriculture, Available online: https:\/\/www.nass.usda.gov\/AgCensus\/."},{"key":"ref_25","unstructured":"(2020, September 10). USDA NRCS National Resources Inventory, Available online: https:\/\/www.nrcs.usda.gov\/wps\/portal\/nrcs\/main\/national\/technical\/nra\/nri\/."},{"key":"ref_26","unstructured":"U.S. Department of Agriculture, National Agricultural Statistics Service (USDA, NASS) (2021, February 10). 2012 Census of Agriculture Vol. 1: Part 51, Chapter 1, AC-12-A-51, United States Summary and State Data, Available online: https:\/\/www.nass.usda.gov\/AgCensus\/."},{"key":"ref_27","unstructured":"U.S. Department of Agriculture, National Agricultural Statistics Service (USDA, NASS) (2021, February 10). Crop Production, 2013 Summary, Available online: https:\/\/www.nass.usda.gov\/."},{"key":"ref_28","unstructured":"U.S. Department of Agriculture, National Agricultural Statistics Service (USDA, NASS) (2021, February 10). Crop Production, 2012 Summary, Available online: https:\/\/www.nass.usda.gov\/."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/5\/281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:01Z","timestamp":1760162101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/5\/281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,29]]},"references-count":28,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["ijgi10050281"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10050281","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,29]]}}}