CropScape and Cropland Data Layer - Other CDL Citations

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Other CDL Citations
2020
  • Feng, Hongli; Tong Wang; David A. Hennessy. 2020. Over-perception of land use changes and biased beliefs: some evidence based on a farm level data. Iowa State University, Department of Economics. February 19, 2020, Ames, Iowa. https://www.econ.iastate.edu/files/events/files/perception_and_land_use_2020_02_18.pdf.
  • Homer, Collin; Jon Dewitz; Suming Jin; George Xian; Catherine Costello; Patrick Danielson; Leila Gass; Michelle Funk; James Wickham; Stephen Stehman; Roger Auch; Kurt Riitters. 2020. Conterminous United States land cover change patterns 2001-2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 162, 2020, Pages 184-199, ISSN 0924-2716. https://doi.org/10.1016/j.isprsjprs.2020.02.019.
  • Konduri, Venkata Shashank; Jitendra Kumar; William W. Hargrove; Forrest M. Hoffman; Auroop R. Ganguly, 2020. Mapping crops within the growing season across the United States, Remote Sensing of Environment, Volume 251, 2020, 112048, ISSN 0034-4257. (https://doi.org/10.1016/j.rse.2020.112048).
  • Pearson, Randall Ph.D; Joshua Pritsolas; Ken Copenhaver; Steffen Mueller Ph.D. 2020. Assessment of the National Resources Inventory (NRI), the Census of Agriculture, the Cropland Data Layer (CDL), and Demand Drivers for Quantifying Land Cover/Use Change. The Energy Resources Center at the University of Illinois at Chicago. March 25, 2020, Chicago, Illinois. http://www.erc.uic.edu/assets/img/documents/LUC_Report_Version_3_25_2020_Updated.pdf.
  • Wang, S., Di Tommaso, S., Deines, J.M. et al. 2020. Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive. Sci Data 7, 307 (2020). https://doi.org/10.1038/s41597-020-00646-4
  • Yang, Jia; Bo Tao; Hao Shi; Ying Ouyang; Shufen Pan; Wei Ren; Chaoqun Lu. 2020. Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction. International Journal of Applied Earth Observation and Geoinformation, Volume 91, 2020, 102151, ISSN 0303-2434. https://doi.org/10.1016/j.jag.2020.102151
  • Zhang, Chen; Liping Di; Zhengwei Yang; Li Lin; Pengyu Hao. 2020. AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environmental Modelling & Software, Available online 13 March 2020, 104694 In Press, Journal Pre-proof. https://doi.org/10.1016/j.envsoft.2020.104694.
2019
  • Alemu, W.G.; Henebry, G.M.; 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. 2019, 11, 2550. https://doi.org/10.3390/rs11212550
  • Rahman, M.S.; Di, L.; Yu, E.; Zhang, C.; Mohiuddin, H. 2019. In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification. Agriculture 2019, 9, 17. https://doi.org/10.3390/agriculture9010017
  • Sun, Peijun; Yaozhong Pan; Jinshui Zhang (2019) Comparison of upscaling cropland and non-cropland map using uncertainty weighted majority rule-based and the majority rule-based aggregation methods, Geocarto International, 34:2, 149-163, DOI: 10.1080/10106049.2017.1377773
  • Sun, Ziheng; Liping Di; Hui Fang (2019) Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series, International Journal of Remote Sensing, 40:2, 593-614, DOI: 10.1080/01431161.2018.1516313
2018
  • Nguyen, Lan H.; Deepak R. Joshi; David E. Clay; Geoffrey M. Henebry. 2018. Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier, Remote Sensing of Environment, 111017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.12.016
2017
  • Friesz, Aaron M.; Bruce K. Wylie; and Daniel M. Howard, 2017. Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier. Remote Sensing Letters, Volume 8, 2017 - Issue 4. Pages 389-398. Published online: 03 Jan 2017. http://dx.doi.org/10.1080/2150704X.2016.1271469.
  • Gao, Feng, Martha C. Anderson, Xiaoyang Zhang, Zhengwei Yang, Joseph G. Alfieri, William P. Kustas, Rick Mueller, David M. Johnson, John H. Prueger, Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery, Remote Sensing of Environment, Volume 188, January 2017, Pages 9-25, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.11.004. URL: http://www.sciencedirect.com/science/article/pii/S0034425716304369.
  • Lark, Tyler; Rick Mueller; Dave Johnson; Holly Gibbs. 2017. Measuring land-use and land-cover change using the U.S. Department of Agriculture’s Cropland Data Layer: Cautions and recommendations. International Journal of Applied Earth Observations and Geoinformation 62, October 2017, Pages 224-235. DOI information: 10.1016/j.jag.2017.06.007.
  • Mladenova, I.E., et al., 2017. "Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 4, pp. 1328-1343, April 2017. doi: 10.1109/JSTARS.2016.2639338. URL: http://ieeexplore.ieee.org/document/7811273/.
  • Moody, D.I., Brumby, S.P., Chartrand, R., Keisler, R., Longbotham, N., Mertes, C., Skillman, S.W., & Warren, M.S. (2017) Crop classification using temporal stacks of multispectral satellite imagery. Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery SSIII, 101980G.
  • Wimberly, Michael, Larry L.Janssen, David A.Hennessy, Moses Luri Niaz M. Chowdhury, Hongli Feng, 2017, Cropland expansion and grassland loss in the eastern Dakotas: New insights from a farm-level survey. Land Use Policy, Volume 63, April 2017, pp. 160-173.
  • Wright, Christopher K, Ben Larson, Tyler J Lark, and Holly K Gibbs, 2017. Recent grassland losses are concentrated around U.S. ethanol refineries. Environmental Research Letters, Volume 12, Number 4, Pages 044001. DOI: https://doi.org/10.1088/1748-9326/aa6446.
2016
  • Arora, Gaurav; Peter T. Wolter; Hongli Feng; David A. Hennessy. 2016. Characterizing land use changes in the Dakotas using historical satellite sensor data: 1984-2015. Proceedings of the 3rd Biennial Conference on the Conservation of America’s Grasslands. September 29-October 1, 2015, Fort Collins, CO. Washington, DC: National Wildlife Federation. Pages 9-11.
  • Arora, G., Wolter, P.T., Feng, H., & Hennessy, D.A. (2016) Land use and policy in Iowa’s Loess Hills Region. Sustainable Agricultural Research, 5(4), 30-45.
  • Arora, G., Wolter, P.T., Feng, H., & Hennessy, D.A. (2016) Role of ethanol plants in Dakotas land use change: Incorporating flexible trends in difference-in-difference framework with remotely sensed data. Center for Agricultural and Rural Development Working Paper #16WP 564.
  • Kipka, Holm, et al. 2016. "Development of the Land-use and Agricultural Management Practice web-Service (LAMPS) for generating crop rotations in space and time." Soil and Tillage Research 155 (2016): 233-249.
  • Motamed, Mesbah, Lihong McPhail, Ryan Williams; Corn Area Response to Local Ethanol Markets in the United States: A Grid Cell Level Analysis, American Journal of Agricultural Economics, Volume 98, Issue 3, 1 April 2016, Pages 726–743.
  • Otkin, Jason A., Martha C. Anderson, Christopher Hain, Mark Svoboda, David Johnson, Richard Mueller, Tsegaye Tadesse, Brian Wardlow, Jesslyn Brown. 2016. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought, Agricultural and Forest Meteorology, Volumes 218–219, 15 March 2016, Pages 230-242, ISSN 0168-1923, https://doi.org/10.1016/j.agrformet.2015.12.065. URL: http://www.sciencedirect.com/science/article/pii/S0168192315300265.
  • Varmaghani, A., and W. E. Eichinger. "Early-Season Classification of Corn and Soybean Using Bayesian Discriminant Analysis on Satellite Images." Agronomy Journal (2016).
2015
  • Laingen, Chris, 2015. Measuring Cropland Change: A Cautionary Tale. Papers in Applied Geography, Volume 1, Issue 1, 2015. http://www.tandfonline.com/doi/full/10.1080/23754931.2015.1009305.
  • Larsen, Ashley E.; Brandon T. Hendrickson; Nicholas Dedeic; Andrew J. MacDonald, 2015. Taken as a given: Evaluating the accuracy of remotely sensed crop data in the USA. Agricultural Systems, Volume 141, December 2015, Pages 121-125. http://www.sciencedirect.com/science/article/pii/S0308521X15300391.
  • Reitsma, Kurtis D.; David E. Clay; Sharon A. Clay; Barry H. Dunn; Cheryl Reese, 2015. Does the U.S. Cropland Data Layer Provide an Accurate Benchmark for Land-Use Change Estimates? Agronomy Journal, Volume 108, Issue 1, 2016, pp. 266-272. doi:10.2134/agronj2015.0288.
  • Reitsma, K.D.; B.H. Dunn; U. Mishra; S.A. Clay; T. DeSutter; D.E. Clay, 2015. Land-Use Change Impact on Soil Sustainability in a Climate and Vegetation Transition Zone. Agronomy Journal Vol. 107, No. 6, pp. 2363-2372. doi:10.2134/agronj15.0152. https://dl.sciencesocieties.org/publications/aj/abstracts/107/6/2363.
2014
  • Brown, J. Christopher, Eric Hanley, Jason Bergtold, Marcelus Caldas, Vijay Barve, Dana Peterson, Ryan Callihan, Jane Gibson, Benjamin Gray, Nathan Hendricks, Nathaniel Brunsell, Kevin Dobbs, Jude Kastens, Dietrich Earnhart, 2014. Ethanol plant location and intensification vs. extensification of corn cropping in Kansas. Applied Geography Volume 53, September 2014, Pages 141–148.
  • Ji, Y., S. Rabotyagov, C. Kling, 2014. Crop Choice and Rotational Effects: A Dynamic Model of Land Use in Iowa in Recent Years. Agricultural & Applied Economics Association's 2014 AAEA Annual Meeting, Minneapolis, MN, July 27-29, 2014.
  • Johnston, Carol A., 2014. Agricultural expansion: land use shell game in the U.S. Northern Plains. Landscape Ecology, January 2014, Volume 29, Issue 1, pp 81-95.
  • Li, Z., Liu, S., Tan, Z., Bliss, N.B., Young, C.J., West, T.O., Ogle, S.M., 2014. Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States. Ecological Modeling, Volume 277, 10 April 2014, Pages 1-12.
  • Long, J.A., Lawrence, R.L., Miller, P.R., Marshall, L.A., 2014. Changes in field-level cropping sequences: Indicators of shifting agricultural practices. Agriculture, Ecosystems and Environment Volume 189, 1 May 2014, Pages 11-20.
  • Sahajpal, Ritvik, Xuesong Zhang, Roberto C. Izaurralde, Ilya Gelfand, and George C. Hurtt. Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt. Computers and Electronics in Agriculture 108 (2014): 173-182.
  • Stern, Alan, Doraiswamy, P., and Hunt, R., 2014. Comparison of different MODIS data product collections over an agricultural area. Remote Sensing Letters, Volume 5, Issue 1, 2 January 2014, Pages 1-9.
  • Wu, Z., Thenkabail, P.S., Verdin, J.P., 2014. Automated cropland classification algorithm (ACCA) for California using multi-sensor remote sensing. Photogrammetric Engineering and Remote Sensing, Volume 80, Issue 1, January 2014, Pages 81-90.
  • Yan, L., Roy, D.P., 2014. Automated crop field extraction from multi-temporal Web Enabled Landsat Data. Remote Sensing of Environment, Volume 144, 25 March 2014, Pages 42-64.
  • Yang, Yubin, Lloyd T. Wilson, Jing Wang, 2014. Reconciling field size distributions of the US NASS (National Agricultural Statistics Service) cropland data. Computers and Electronics in Agriculture, Volume 109, November 2014, Pages 232–246.
  • Yost, M.A., M.P. Russelle, J.A. Coulter, P.V. Bolstad, and A.C. Jenks, 2014. Geographic trends in alfalfa stand age and crops that follow alfalfa. North Central Extension-Industry Soil Fertility Conference. 2013. Vol. 29. Des Moines, IA.
2013
  • Bandaru, Varaprasad, Tristram O. West, Daniel M. Ricciuto, R. César Izaurralde, 2013. Estimating crop net primary production using national inventory data and MODIS-derived parameters, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 80, June 2013, pp. 61-71, ISSN 0924-2716.
  • Fitzgerald, Timothy and Grant Zimmerman, 2013. Agriculture in the Tongue River Basin: Output, Water Quality, and Implications. Agricultural Marketing Policy Paper No. 39, May 2013.
  • Gao, F., Shuai, Y., He, T., Schaaf, C.B., Masek, J.G., Wang, Z., 2013. Influence of angular effects and adjustment on medium resolution sensors for crop monitoring (Conference Paper). Nature 493, pp. 514 - 517.
  • Gelfand, Ilya, Sahajpal, R., Zhang, X., Izaurralde, R., Gross, K., and Robertson, G., 2013. Sustainable bioenergy production from marginal lands in the US Midwest. Nature 493, pp. 514 - 517.
  • Hendricks, Nathan P., Sumathy Sinnathamby, Kyle Douglas-Mankin, Aaron Smith, Daniel A. Sumner, and Dietrich H. Earnhart, 2013. The Environmental Effects of Crop Price Increases: Nitrogen Losses in the U.S. Corn Belt. Report available through the University of California Davis.
  • Howard, Daniel, Wylie, B. and Tieszen, L., 2013. Crop classification modeling using remote sensing and environmental data in the Greater Platte River Basin, USA. International Journal of Remote Sensing, 33(19): pp. 6094-6108.
  • Ines, Amor, Narendra Das, James Hansen, Eni Njoku, 2013. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment 138(7) pp. 149-164.
  • Johnston, Carol, 2013. Wetland Losses Due to Row Crop Expansion in the Dakota Prairie Pothole Region. Wetlands, 33: pp. 175-182.
  • Muth Jr., D.J., Bryden, K., and Nelson, R., 2013. Sustainable agricultural residue removal for bioenergy: A spatially comprehensive US national assessment. Applied Energy 102, pp. 403-417.
  • Plourde, James, Pijanowski, B., and Pekin, B., 2013. Evidence for increased monoculture cropping in the Central United States. Agriculture, Ecosystems & Environment, 165(15): pp 50-59.
  • Potter, Christopher, 2013. Ten years of vegetation change in Northern California marshlands detected using Landsat satellite image analysis. Journal of Water Resource and Protection, 5: pp. 485-494.
  • Rashford, Benjamin, Albeke, S., and Lewis, D., 2013. Modeling Grassland Conversion: Challenges of Using Satellite Imagery Data. American Journal Agricultural Economics, 95(2): pp. 404-411.
  • Wart, Justin van, K. Christian Kersebaum, Shaobing Peng, Maribeth Milner, Kenneth G. Cassman, 2013. Estimating crop yield potential at regional to national scales. Field Crops Research 143 (2013) 34–43.
  • Wright, Christopher, and Wimberly, W., 2013. Recent land use change in the Western Corn Belt threatens grasslands and wetlands. Proceedings of the National Academy of Sciences (USA), 110(10): pp. 4134-4139.
  • Yu, G., Di, L., Zhang, B., Shao, Y., Shrestha, R., Kang, L., 2013. Remote-sensing-based flood damage estimation using crop condition profiles (Conference Paper). 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013, Article number 6621908, Pages 205-210.
  • Yun, Seong Do and Benjamin M. Gramig, 2013. Spatially Explicit Dynamically Optimal Provision of Ecosystem Services: An Application to Biological Control of Soybean Aphid. Selected Paper prepared for presentation at the Agricultural & Applied Economics Association’s 2013 AAEA & CAES Joint Annual Meeting, Washington, DC, August 4-6, 2013. Http://ageconsearch.umn.edu/bitstream/150744/2/AAEA2013_Yun_and_Gramig.pdf.
  • Zheng, Baojuan, James Campbell, Yang Shao, and Randolph Wynne, 2013. Broad-scale monitoring of tillage practices using sequential Landsat imagery. Soil Science Society of America Journal: Vol. 77 No. 5, p. 1755-1764. 09/20/2013.
  • Zhong, Liheng, Peng Gong, Gregory Biging, 2013. Efficient corn and soybean mapping with temporal extendibility: A multi-year experiment using Landsat imagery. Remote Sensing of Environment 140(8) pp. 1-13.
2012
  • Belden, Jason, Hanson, B., McMurry, S., Smith, L., and Haukos, D., 2012. Assessment of the effects of farming and conservation programs on pesticide deposition in High Plains wetlands Environmental Science & Technology 46(6), pp. 3424-3432.
  • Brown, J.C., Kastens, J.H., Coutinho, A.C., Victoria, D.D.C., Bishop, C.R., 2012. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment Volume 130, 5 March 2013, Pages 39-50.
  • Cibin, R., Chaubey, I., and Engel, B., 2012. Simulated watershed scale impacts of corn stover removal for biofuel on hydrology and water quality. Hydrological Processes 26: pp. 1629 - 1641.
  • Faber, Scott and Soren Rundquist, 2012. Plowed Under Report from the Environmental Working Group: pp. 1-12.
  • Han, W., Yang, Z., Di, L., and Mueller, R., 2012. "CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support" Computers and Electronics in Agriculture Vol. 84, June, pp. 111- 123 , 2012.
  • Herdy, Claire, Luvall, J., Cooksey, K., Brenton, J., Barrick, B., Padgett-Vasquesz, S., 2012. Alabama Disasters: Leveraging NASA EOS to explore the environmental and economic impact of the April 27 tornado outbreak. 5th Wernher von Braun Memorial Symposium; Huntsville, AL. pp. 1-9.
  • Kutz, Frederick, Morgan, J., Monn, J., and Petrey, C., 2012. Geospatial approaches to characterizing agriculture in the Chincoteague Bay Subbasin. Environmental Monitoring and Assessment, 184(2): pp. 679-692.
  • Melton, F.S., Johnson, L.F., Lund, C.P., Pierce, L.L., Michaelis, A.R., Hiatt, S.H., Guzman, A., Adhikari, D.D., Purdy, A.J., Rosevelt, C., Votava, P., Trout, T.J., Temesgen, B., Frame, K., Sheffner, E.J., Nemani, R.R., 2012. Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Volume 5, Issue 6, 2012, Article number 6375772, Pages 1709-1721.
  • Painter, Kathleen, Donlon, H., and Kane, S., 2013. Results of a 2012 survey of Idaho oilseed producers. Agricultural Economics Extension Series. No 13-01.
  • Stern, Alan, Doraiswamy, P., and Hunt, R., 2012. Changes of crop rotation in Iowa determined from the United States Department of Agriculture, National Agricultural Statistics Service cropland data layer product. Journal of Applied Remote Sensing, 6(1): pp. 1- 16.
2011
  • Hartz, Laura, Boettner, F., and Clingerman, J., 2011. Greenbrier Valley Local Food: The Possibilities and Potential. Greenbrier Valley Economic Development Corp: pp. 1 - 34.
  • U.S. Department of Energy. 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. R.D. Perlack and B.J. Stokes (Leads), ORNL/TM-2011/224. Oak Ridge National Laboratory, Oak Ridge, TN. 227p.
  • Yang, Yubin, Wilson, L., Wang, J., and Li, X., 2011. Development of an Integrated Cropland and Soil Data Management System for Cropping System Applications. Computers and Electronics in Agriculture, 76: pp. 105-118.
2010 and Older
  • Becker-Reshef, I, E. Vermote, M. Lindeman, and C. Justice, 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6), pp. 1312-1323.
  • Chang, Jiyul, Matthew Hansen, Kyle Pittman, Mark Carroll, and Charlene DiMiceli, 2007. Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets. Agronomy Journal, 99: pp. 1654-1664.
  • Lunetta, Ross, Shao, Y., Ediriwickrema, J., and Lyon J., 2010. Monitoring Agricultural Cropping Patterns across the Laurentian Great Lakes Basin Using MODIS-NDVI Data. International Journal of Applied Earth Observation and Geoinformation, 12: pp. 81-88.
  • Pittman, Kyle, Matthew Hansen, Inbal Becker-Reshef, Peter Potapov and Christopher Justice, 2010. Estimating Global Cropland Extent with Multi-year MODIS Data. Remote Sensing 2(7): pp. 1844-1863.
  • Schaaf, Dionn, Linz, G., Doetkott, C., Lutman, M., and Bleier, W., 2008. Non-blackbird Avian Occurrence and Abundance in North Dakota Sunflower Fields, The Prairie Naturalist 40(3/4): September/December.
  • Shao, Y., R. Lunetta, J. Ediriwickrema, J. Iiames, 2010. Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data. Photogrammetric Engineering and Remote Sensing, 75(1), pp. 73-84.
  • Thompson, Aaron and Prokopy, L., 2009. Tracking urban sprawl: Using spatial data to inform farmland preservation policy. Land Use Policy, 26(2): pp. 194-202.
  • Wardlow, B., S. Egbert, J. Kastens, 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290-310.
  • West, Tristram, Brandt, C., Baskaran, L., Hellwinckel, C., Mueller, R., Bernacchi, C., Bandaru, V., Yang, B., Wilson, B., Marland, G., Nelson, R., De La Torre Ugarte, D., and Post, W., 2010. Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting. Ecological Applications 20(4): pp. 1074-1086.
  • Xian, G., Homer, C., Fry, J., 2009. Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113(6), 1133-1147.
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Last Modified: 09/25/2020