Cropland Data Layer, New York, 2020

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Identification_Information

Citation:
Citation_Information:
Originator: United States Department of Agriculture (USDA)
Originator: National Agricultural Statistics Service (NASS)
Publication_Date: 20210201
Title: Cropland Data Layer, New York, 2020
Edition: 2020 Edition
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: USDA-NASS Cropland Data Layer
Issue_Identification: New York 2010
Publication_Information:
Publication_Place: USDA, NASS Marketing and Information Services Office, Washington, D.C.
Publisher: USDA, NASS
Other_Citation_Details: NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape at <https://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>.
Online_Linkage: https://cugir.library.cornell.edu/catalog/cugir-009110
Online_Linkage: https://nassgeodata.gmu.edu/CropScape/
Description:
Abstract: The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2020 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2016 (NLCD 2016). Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
Purpose: The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide planted acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
Supplemental_Information: If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. USDA, National Agricultural Statistics Service, 2020 New York Cropland Data Layer CLASSIFICATION INPUTS: DEIMOS-1 DATE 20200420 SCENE IDENTIFIER 701 DEIMOS-1 DATE 20200507 SCENE IDENTIFIER 7B1 DEIMOS-1 DATE 20200521 SCENE IDENTIFIER 83A DEIMOS-1 DATE 20200620 SCENE IDENTIFIER 87E DEIMOS-1 DATE 20200621 SCENE IDENTIFIER 882 RESOURCESAT-2 LISS-3 DATE 20191011 PATH 289 RESOURCESAT-2 LISS-3 DATE 20200715 PATH 287 RESOURCESAT-2 LISS-3 DATE 20200822 PATH 285 RESOURCESAT-2 LISS-3 DATE 20200915 PATH 285 RESOURCESAT-2 LISS-3 DATE 20200916 PATH 290 LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200503 PATH 014 LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200609 PATH 017 LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200706 PATH 014 LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200908 PATH 014 LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200915 PATH 015 USGS, NATIONAL ELEVATION DATASET USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY USGS, NATIONAL LAND COVER DATABASE 2016 IMPERVIOUSNESS USDA, NASS CROPLAND DATA LAYERS 2014-2019 SENTINEL-2A DATE 20191009 RELATIVE ORBIT NUMBER 140 SENTINEL-2A DATE 20191013 RELATIVE ORBIT NUMBER 054 SENTINEL-2A DATE 20191019 RELATIVE ORBIT NUMBER 140 SENTINEL-2A DATE 20200503 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200513 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200520 RELATIVE ORBIT NUMBER 054 SENTINEL-2A DATE 20200527 RELATIVE ORBIT NUMBER 011 SENTINEL-2A DATE 20200609 RELATIVE ORBIT NUMBER 054 SENTINEL-2A DATE 20200615 RELATIVE ORBIT NUMBER 140 SENTINEL-2A DATE 20200616 RELATIVE ORBIT NUMBER 011 SENTINEL-2A DATE 20200622 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200702 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200706 RELATIVE ORBIT NUMBER 011 SENTINEL-2A DATE 20200709 RELATIVE ORBIT NUMBER 054 SENTINEL-2A DATE 20200719 RELATIVE ORBIT NUMBER 054 SENTINEL-2A DATE 20200726 RELATIVE ORBIT NUMBER 011 SENTINEL-2A DATE 20200811 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200814 RELATIVE ORBIT NUMBER 140 SENTINEL-2A DATE 20200821 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20200904 RELATIVE ORBIT NUMBER 011 SENTINEL-2A DATE 20200920 RELATIVE ORBIT NUMBER 097 SENTINEL-2A DATE 20201010 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20191001 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20191005 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20191008 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20191011 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20191015 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20191021 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20200425 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200502 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20200505 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200512 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20200521 RELATIVE ORBIT NUMBER 140 SENTINEL-2B DATE 20200604 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200617 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20200621 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20200704 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200707 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20200710 RELATIVE ORBIT NUMBER 140 SENTINEL-2B DATE 20200823 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200912 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200915 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20200919 RELATIVE ORBIT NUMBER 011 SENTINEL-2B DATE 20200922 RELATIVE ORBIT NUMBER 054 SENTINEL-2B DATE 20200925 RELATIVE ORBIT NUMBER 097 SENTINEL-2B DATE 20201009 RELATIVE ORBIT NUMBER 011 TRAINING AND VALIDATION: USDA, FARM SERVICE AGENCY 2020 COMMON LAND UNIT DATA USGS, NATIONAL LAND COVER DATABASE 2016 NOTE: The final extent of the CDL is clipped to the state boundary even though the raw input data may encompass a larger area.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Currentness_Reference: 2020 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -79.974336
East_Bounding_Coordinate: -70.596893
North_Bounding_Coordinate: 45.838882
South_Bounding_Coordinate: 40.116635
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming
Theme_Keyword: environment
Theme_Keyword: imageryBaseMapsEarthCover
Theme:
Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword: Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands
Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: DEIMOS-1
Theme_Keyword: ISRO ResourceSat-2 LISS-3
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CropScape
Theme:
Theme_Keyword_Thesaurus: CUGIR Category
Theme_Keyword: agriculture
Theme_Keyword: environment
Theme_Keyword: landcover
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: New York
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2020
Access_Constraints: None
Use_Constraints: The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Browse_Graphic:
Browse_Graphic_File_Name: https://cugir-data.s3.amazonaws.com/00/91/10/preview.png
Browse_Graphic_File_Description: preview of the dataset
Browse_Graphic_File_Type: PNG
Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:
Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None
Native_Data_Set_Environment: Microsoft Windows 7 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.7 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <https://www.mrlc.gov/>. ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.

Data_Quality_Information

Attribute_Accuracy:
Attribute_Accuracy_Report: If the following table does not display properly, then please visit this internet site <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original metadata file. USDA, National Agricultural Statistics Service, 2020 New York Cropland Data Layer STATEWIDE AGRICULTURAL ACCURACY REPORT Crop-specific covers only *Correct Accuracy Error Kappa ------------------------- ------- -------- ------ ----- OVERALL ACCURACY** 340,658 73.0% 27.0% 0.659 Cover Attribute *Correct Producer's Omission User's Commission Cond'l Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa ---- ---- ------ -------- ----- ----- -------- ----- ----- Corn 1 147492 87.8% 12.2% 0.850 87.5% 12.5% 0.846 Sorghum 4 368 30.7% 69.3% 0.307 69.2% 30.8% 0.691 Soybeans 5 40581 76.9% 23.1% 0.756 84.7% 15.3% 0.838 Sunflower 6 2 3.9% 96.1% 0.039 50.0% 50.0% 0.500 Sweet Corn 12 911 45.2% 54.8% 0.451 77.5% 22.5% 0.774 Barley 21 235 33.9% 66.1% 0.338 65.1% 34.9% 0.651 Durum Wheat 22 0 n/a n/a n/a 0.0% 100.0% 0.000 Spring Wheat 23 35 21.7% 78.3% 0.217 55.6% 44.4% 0.555 Winter Wheat 24 15034 83.1% 16.9% 0.828 84.6% 15.4% 0.843 Dbl Crop WinWht/Soybeans 26 0 0.0% 100.0% 0.000 n/a n/a n/a Rye 27 301 18.3% 81.7% 0.183 57.6% 42.4% 0.575 Oats 28 2145 46.3% 53.7% 0.461 70.2% 29.8% 0.701 Millet 29 1 1.8% 98.2% 0.018 50.0% 50.0% 0.500 Speltz 30 38 25.3% 74.7% 0.253 65.5% 34.5% 0.655 Alfalfa 36 60803 74.2% 25.8% 0.717 76.5% 23.5% 0.742 Other Hay/Non Alfalfa 37 57240 65.7% 34.3% 0.623 69.9% 30.1% 0.668 Buckwheat 39 126 39.3% 60.7% 0.392 78.3% 21.7% 0.783 Sugarbeets 41 182 39.8% 60.2% 0.398 61.7% 38.3% 0.617 Dry Beans 42 3318 56.8% 43.2% 0.566 75.3% 24.7% 0.752 Potatoes 43 1053 60.3% 39.7% 0.602 87.5% 12.5% 0.874 Other Crops 44 5 3.1% 96.9% 0.031 41.7% 58.3% 0.417 Misc Vegs & Fruits 47 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 Onions 49 747 78.4% 21.6% 0.784 79.2% 20.8% 0.792 Cucumbers 50 122 55.5% 44.5% 0.554 78.7% 21.3% 0.787 Peas 53 712 56.9% 43.1% 0.569 74.9% 25.1% 0.749 Tomatoes 54 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 Herbs 57 0 0.0% 100.0% 0.000 n/a n/a n/a Clover/Wildflowers 58 796 30.6% 69.4% 0.305 58.6% 41.4% 0.585 Sod/Grass Seed 59 414 74.9% 25.1% 0.749 80.4% 19.6% 0.804 Fallow/Idle Cropland 61 2306 33.5% 66.5% 0.332 52.8% 47.2% 0.525 Cherries 66 16 25.4% 74.6% 0.254 69.6% 30.4% 0.696 Peaches 67 13 18.6% 81.4% 0.186 36.1% 63.9% 0.361 Apples 68 2174 71.2% 28.8% 0.711 77.7% 22.3% 0.776 Grapes 69 1745 79.1% 20.9% 0.790 88.6% 11.4% 0.886 Christmas Trees 70 58 30.5% 69.5% 0.305 57.4% 42.6% 0.574 Other Tree Crops 71 0 0.0% 100.0% 0.000 n/a n/a n/a Pears 77 12 14.1% 85.9% 0.141 60.0% 40.0% 0.600 Triticale 205 377 25.4% 74.6% 0.253 73.1% 26.9% 0.730 Carrots 206 89 69.5% 30.5% 0.695 76.7% 23.3% 0.767 Broccoli 214 16 30.2% 69.8% 0.302 88.9% 11.1% 0.889 Peppers 216 0 n/a n/a n/a 0.0% 100.0% 0.000 Greens 219 1 1.8% 98.2% 0.018 16.7% 83.3% 0.167 Plums 220 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 Strawberries 221 0 0.0% 100.0% 0.000 0.0% 100.0% 0.000 Squash 222 208 39.9% 60.1% 0.399 84.9% 15.1% 0.849 Dbl Crop WinWht/Corn 225 0 0.0% 100.0% 0.000 n/a n/a n/a Dbl Crop Triticale/Corn 228 159 33.7% 66.3% 0.337 85.9% 14.1% 0.859 Pumpkins 229 33 20.1% 79.9% 0.201 40.2% 59.8% 0.402 Dbl Crop Barley/Corn 237 0 n/a n/a n/a 0.0% 100.0% 0.000 Blueberries 242 1 2.4% 97.6% 0.024 12.5% 87.5% 0.125 Cabbage 243 789 65.3% 34.7% 0.652 87.5% 12.5% 0.875 *Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix. **The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255). FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy. The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.
Attribute_Accuracy_Explanation: The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.
Logical_Consistency_Report: The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.
Completeness_Report: The entire state is covered by the Cropland Data Layer.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report: The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Space Research Organization (ISRO)
Publication_Date: 2020
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Indian Space Research Organisation HQ, Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231
Publisher: Indian Space Research Organization (ISRO)
Other_Citation_Details: The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 24 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: LISS-3
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: European Space Agency (ESA)
Publication_Date: 2020
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: European Commission, Brussels (Belgium)
Publisher: Copernicus - European Commission
Other_Citation_Details: The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: SENTINEL-2
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: Elecnor Deimos Imaging
Publication_Date: 2020
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Elecnor Deimos Imaging, Valladolid, Spain
Publisher: Astrium GEO Information Services
Other_Citation_Details: The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <https://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Deimos-1
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: United States Geological Survey (USGS), Earth Resources Observation and Science (EROS)
Publication_Date: 2020
Title: Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198-001
Publisher: USGS, EROS
Other_Citation_Details: The Landsat 8 OLI/TIRS data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat 8
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2009
Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details: The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NED
Source_Contribution: spatial and attribute information used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2019
Title: National Land Cover Database 2016 (NLCD 2016)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details: The NLCD 2016 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2016 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The NLCD2016 Tree Canopy data layer was not published in time for use in CDL production, so the NLCD 2011 Tree Canopy data was used. More information on the NLCD 2016 and NLCD 2011 can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: United States Department of Agriculture (USDA), Farm Service Agency (FSA)
Publication_Date: 2020
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office
Other_Citation_Details: Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: FSA CLU
Source_Contribution: spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Process_Step:
Process_Description: OVERVIEW: The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates, most closely aligned with planted acres, produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.' SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships. GROUND TRUTH: As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground truth data from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground truth data provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <https://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data. INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The DEIMOS-1 and UK-DMC 2 imagery was resampled to 30 meters using cubic convolution, rigorous transformation to match the traditional Landsat spatial resolution. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2016 (NLCD 2016). Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL. ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report. PUBLIC RELEASE: The USDA, NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape <https://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.
Process_Date: 2020
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Cloud_Cover: 0

Spatial_Data_Organization_Information

Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 16925
Column_Count: 22229

Spatial_Reference_Information

Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:
Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 30
Ordinate_Resolution: 30
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257223563

Entity_and_Attribute_Information

Overview_Description:
Entity_and_Attribute_Overview: The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
Entity_and_Attribute_Detail_Citation: If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. Data Dictionary: USDA, National Agricultural Statistics Service, 2020 Cropland Data Layer Source: USDA, National Agricultural Statistics Service The following is a cross reference list of the categorization codes and land covers. Note that not all land cover categories listed below will appear in an individual state. Raster Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0 Categorization Code Land Cover "0" Background Raster Attribute Domain Values and Definitions: CROPS 1-60 Categorization Code Land Cover "1" Corn "2" Cotton "3" Rice "4" Sorghum "5" Soybeans "6" Sunflower "10" Peanuts "11" Tobacco "12" Sweet Corn "13" Pop or Orn Corn "14" Mint "21" Barley "22" Durum Wheat "23" Spring Wheat "24" Winter Wheat "25" Other Small Grains "26" Dbl Crop WinWht/Soybeans "27" Rye "28" Oats "29" Millet "30" Speltz "31" Canola "32" Flaxseed "33" Safflower "34" Rape Seed "35" Mustard "36" Alfalfa "37" Other Hay/Non Alfalfa "38" Camelina "39" Buckwheat "41" Sugarbeets "42" Dry Beans "43" Potatoes "44" Other Crops "45" Sugarcane "46" Sweet Potatoes "47" Misc Vegs & Fruits "48" Watermelons "49" Onions "50" Cucumbers "51" Chick Peas "52" Lentils "53" Peas "54" Tomatoes "55" Caneberries "56" Hops "57" Herbs "58" Clover/Wildflowers "59" Sod/Grass Seed "60" Switchgrass Raster Attribute Domain Values and Definitions: NON-CROP 61-65 Categorization Code Land Cover "61" Fallow/Idle Cropland "63" Forest "64" Shrubland "65" Barren Raster Attribute Domain Values and Definitions: CROPS 66-80 Categorization Code Land Cover "66" Cherries "67" Peaches "68" Apples "69" Grapes "70" Christmas Trees "71" Other Tree Crops "72" Citrus "74" Pecans "75" Almonds "76" Walnuts "77" Pears Raster Attribute Domain Values and Definitions: OTHER 81-109 Categorization Code Land Cover "81" Clouds/No Data "82" Developed "83" Water "87" Wetlands "88" Nonag/Undefined "92" Aquaculture Raster Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195 Categorization Code Land Cover "111" Open Water "112" Perennial Ice/Snow "121" Developed/Open Space "122" Developed/Low Intensity "123" Developed/Med Intensity "124" Developed/High Intensity "131" Barren "141" Deciduous Forest "142" Evergreen Forest "143" Mixed Forest "152" Shrubland "176" Grassland/Pasture "190" Woody Wetlands "195" Herbaceous Wetlands Raster Attribute Domain Values and Definitions: CROPS 195-255 Categorization Code Land Cover "204" Pistachios "205" Triticale "206" Carrots "207" Asparagus "208" Garlic "209" Cantaloupes "210" Prunes "211" Olives "212" Oranges "213" Honeydew Melons "214" Broccoli "215" Avocados "216" Peppers "217" Pomegranates "218" Nectarines "219" Greens "220" Plums "221" Strawberries "222" Squash "223" Apricots "224" Vetch "225" Dbl Crop WinWht/Corn "226" Dbl Crop Oats/Corn "227" Lettuce "228" Dbl Crop Triticale/Corn "229" Pumpkins "230" Dbl Crop Lettuce/Durum Wht "231" Dbl Crop Lettuce/Cantaloupe "232" Dbl Crop Lettuce/Cotton "233" Dbl Crop Lettuce/Barley "234" Dbl Crop Durum Wht/Sorghum "235" Dbl Crop Barley/Sorghum "236" Dbl Crop WinWht/Sorghum "237" Dbl Crop Barley/Corn "238" Dbl Crop WinWht/Cotton "239" Dbl Crop Soybeans/Cotton "240" Dbl Crop Soybeans/Oats "241" Dbl Crop Corn/Soybeans "242" Blueberries "243" Cabbage "244" Cauliflower "245" Celery "246" Radishes "247" Turnips "248" Eggplants "249" Gourds "250" Cranberries "254" Dbl Crop Barley/Soybeans

Distribution_Information

Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Albert R. Mann Library
Contact_Address:
Address_Type: mailing and physical
Address: Cornell University
City: Ithaca
State_or_Province: New York
Postal_Code: 14853
Contact_Voice_Telephone: 607-255-5406
Contact_Electronic_Mail_Address: mann-ref@cornell.edu
Distribution_Liability: Cornell University provides these geographic data "as is". Cornell University makes no guarantee or warranty concerning the accuracy of information contained in the geographic data. Cornell University further makes no warranty either expressed or implied, regarding the condition of the product or its fitness for any particular purpose. The burden for determining fitness for use lies entirely with the user. Although these files have been processed successfully on computers at Cornell University, no warranty is made by Cornell University regarding the use of these data on any other system, nor does the fact of distribution constitute or imply any such warranty.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GeoTIFF
Format_Information_Content: zipped geotiff
File_Decompression_Technique: zip
Transfer_Size: 39.05
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Digital_Form:
Digital_Transfer_Information:
Format_Name: metadata
Format_Information_Content: FGDC XML metadata
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Digital_Form:
Digital_Transfer_Information:
Format_Name: HTML metadata
Format_Information_Content: FGDC HTML metadata
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Digital_Form:
Digital_Transfer_Information:
Format_Name: OGC:WMS
Format_Information_Content: WMS, from GeoServer
Digital_Transfer_Option:
Online_Option:
Fees: None

Distribution_Information

Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS Customer Service
Contact_Person: USDA, NASS Customer Service Staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5038-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Contact_Instructions: Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Resource_Description: Cropland Data Layer - New York 2020
Distribution_Liability: Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GEOTIFF
Format_Version_Date: 2020
Format_Information_Content: GEOTIFF
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Access_Instructions: The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. It is also available free for download from the Geospatial Data Gateway website <https://datagateway.nrcs.usda.gov/>.
Fees: Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Ordering_Instructions: The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. If you experience problems downloading all years of CDL data through the Geospatial Data Gateway then you can try to use the 'Direct Data Download' link in the lower right-hand corner of their webpage.
Custom_Order_Process: For a list of other states and years of available CDL data please visit <https://nassgeodata.gmu.edu/CropScape/> or <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Technical_Prerequisites: If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.

Metadata_Reference_Information

Metadata_Date: 20210720
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Albert R. Mann Library
Contact_Address:
Address_Type: mailing and physical
Address: Albert R. Mann Library
City: Ithaca
State_or_Province: New York
Postal_Code: 14853
Country: USA
Contact_Voice_Telephone: 607-255-5406
Contact_Electronic_Mail_Address: mann-ref@cornell.edu
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time