@inproceedings{shrestha_crop_2017,
	title = {Crop {Fraction} {Layer} ({CFL}) datasets derived through {MODIS} and {LandSat} for the continental {US} from year 2000–2016},
	doi = {10.1109/Agro-Geoinformatics.2017.8047068},
	abstract = {With ever growing population and shrinkage of agricultural land, food security is an extremely important research topic. Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product. The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. Additionally these CFL datasets are made accessible to the end-user through web-based application.},
	booktitle = {2017 6th {International} {Conference} on {Agro}-{Geoinformatics}},
	author = {Shrestha, R. and Di, L. and Yu, E. G. and Rahman, M. S. and Lin, L. and Hu, L. and Tang, J.},
	month = aug,
	year = {2017},
	pages = {1--7},
	file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/NFSM9UT7/8047068.html:text/html}
}

{"_id":"kdRzuwq9XtqKJR36D","bibbaseid":"shrestha-di-yu-rahman-lin-hu-tang-cropfractionlayercfldatasetsderivedthroughmodisandlandsatforthecontinentalusfromyear20002016-2017","downloads":0,"creationDate":"2019-01-23T19:47:22.622Z","title":"Crop Fraction Layer (CFL) datasets derived through MODIS and LandSat for the continental US from year 2000–2016","author_short":["Shrestha, R.","Di, L.","Yu, E. G.","Rahman, M. S.","Lin, L.","Hu, L.","Tang, J."],"year":2017,"bibtype":"inproceedings","biburl":"http://crop.csiss.gmu.edu/data/csiss_pub.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Crop Fraction Layer (CFL) datasets derived through MODIS and LandSat for the continental US from year 2000–2016","doi":"10.1109/Agro-Geoinformatics.2017.8047068","abstract":"With ever growing population and shrinkage of agricultural land, food security is an extremely important research topic. Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product. The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. 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Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product. The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. Additionally these CFL datasets are made accessible to the end-user through web-based application.},\n\tbooktitle = {2017 6th {International} {Conference} on {Agro}-{Geoinformatics}},\n\tauthor = {Shrestha, R. and Di, L. and Yu, E. G. and Rahman, M. S. and Lin, L. and Hu, L. and Tang, J.},\n\tmonth = aug,\n\tyear = {2017},\n\tpages = {1--7},\n\tfile = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/NFSM9UT7/8047068.html:text/html}\n}\n\n","author_short":["Shrestha, R.","Di, L.","Yu, E. G.","Rahman, M. 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