Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.