2020 West Virginia Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)

Publication_Date: 20210201
Title: 2020 West Virginia Cropland Data Layer | NASS/USDA
Edition: 2020 Edition
Geospatial_Data_Presentation_Form: raster digital data
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/>.

***West Virginia Cropland Data Layer specific information*** The processing for the West Virginia CDL differed from the other CDLs in that Virginia and West Virginia were grouped and treated as one classification.

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 Virginia and West Virginia Cropland Data Layers

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20200417 SCENE IDENTIFIER 6E8
DEIMOS-1 DATE 20200504 SCENE IDENTIFIER 793
DEIMOS-1 DATE 20200507 SCENE IDENTIFIER 7B1
DEIMOS-1 DATE 20200515 SCENE IDENTIFIER 80C
DEIMOS-1 DATE 20200525 SCENE IDENTIFIER 859
DEIMOS-1 DATE 20200624 SCENE IDENTIFIER 88F
DEIMOS-1 DATE 20200625 SCENE IDENTIFIER 896

RESOURCESAT-2 LISS-3 DATE 20200504 PATH 287
RESOURCESAT-2 LISS-3 DATE 20200513 PATH 284
RESOURCESAT-2 LISS-3 DATE 20200607 PATH 289
RESOURCESAT-2 LISS-3 DATE 20200710 PATH 286
RESOURCESAT-2 LISS-3 DATE 20200715 PATH 287
RESOURCESAT-2 LISS-3 DATE 20200725 PATH 289
RESOURCESAT-2 LISS-3 DATE 20200730 PATH 290
RESOURCESAT-2 LISS-3 DATE 20200818 PATH 289
RESOURCESAT-2 LISS-3 DATE 20200906 PATH 288
RESOURCESAT-2 LISS-3 DATE 20200910 PATH 284
RESOURCESAT-2 LISS-3 DATE 20200920 PATH 286
RESOURCESAT-2 LISS-3 DATE 20200930 PATH 288
RESOURCESAT-2 LISS-3 DATE 20201005 PATH 289

LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200417 PATH 014
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200422 PATH 017
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200429 PATH 018
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200510 PATH 015
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200515 PATH 018
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200607 PATH 019
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200627 PATH 015
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200729 PATH 015
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200819 PATH 018
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200826 PATH 019
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200906 PATH 016
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200915 PATH 015
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200920 PATH 018
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200922 PATH 016
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20201001 PATH 015
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20201006 PATH 018
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20201008 PATH 016

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2016 IMPERVIOUSNESS
USDA, NASS CROPLAND DATA LAYERS 2008-2019

SENTINEL-2A DATE 20200416 RELATIVE ORBIT NUMBER 140
SENTINEL-2A DATE 20200510 RELATIVE ORBIT NUMBER 054
SENTINEL-2A DATE 20200513 RELATIVE ORBIT NUMBER 097
SENTINEL-2A DATE 20200526 RELATIVE ORBIT NUMBER 140
SENTINEL-2A DATE 20200602 RELATIVE ORBIT NUMBER 097
SENTINEL-2A DATE 20200609 RELATIVE ORBIT NUMBER 054
SENTINEL-2A DATE 20200629 RELATIVE ORBIT NUMBER 054
SENTINEL-2A DATE 20200705 RELATIVE ORBIT NUMBER 140
SENTINEL-2A DATE 20200708 RELATIVE ORBIT NUMBER 040
SENTINEL-2A DATE 20200715 RELATIVE ORBIT NUMBER 140
SENTINEL-2A DATE 20200719 RELATIVE ORBIT NUMBER 054
SENTINEL-2A DATE 20200906 RELATIVE ORBIT NUMBER 040
SENTINEL-2A DATE 20200907 RELATIVE ORBIT NUMBER 054
SENTINEL-2A DATE 20200916 RELATIVE ORBIT NUMBER 040
SENTINEL-2A DATE 20200920 RELATIVE ORBIT NUMBER 097
SENTINEL-2A DATE 20200923 RELATIVE ORBIT NUMBER 140
SENTINEL-2A DATE 20201006 RELATIVE ORBIT NUMBER 040
SENTINEL-2A DATE 20201007 RELATIVE ORBIT NUMBER 054

SENTINEL-2B DATE 20200504 RELATIVE ORBIT NUMBER 040
SENTINEL-2B DATE 20200515 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200603 RELATIVE ORBIT NUMBER 040
SENTINEL-2B DATE 20200604 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200607 RELATIVE ORBIT NUMBER 097
SENTINEL-2B DATE 20200614 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200624 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200627 RELATIVE ORBIT NUMBER 097
SENTINEL-2B DATE 20200704 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200707 RELATIVE ORBIT NUMBER 097
SENTINEL-2B DATE 20200714 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200809 RELATIVE ORBIT NUMBER 140
SENTINEL-2B DATE 20200823 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20200908 RELATIVE ORBIT NUMBER 140
SENTINEL-2B DATE 20200915 RELATIVE ORBIT NUMBER 097
SENTINEL-2B DATE 20200922 RELATIVE ORBIT NUMBER 054
SENTINEL-2B DATE 20201005 RELATIVE ORBIT NUMBER 097
SENTINEL-2B DATE 20201008 RELATIVE ORBIT NUMBER 140

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: -82.7034
East_Bounding_Coordinate: -77.8150
North_Bounding_Coordinate: 40.6271
South_Bounding_Coordinate: 37.1607

Keywords:

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

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

Place:

Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword:

Continent > North America > United States of America > West Virginia

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: West Virginia
Place_Keyword: WV

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

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 Virginia and West Virginia Cropland Data Layers
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only                *Correct    Accuracy       Error       Kappa
-------------------------                 -------    --------      ------       -----
OVERALL ACCURACY**                        312,151       74.4%       25.6%       0.680

Cover                        Attribute    *Correct  Producer's    Omission                  User's  Commission      Cond'l
Type                              Code      Pixels    Accuracy       Error       Kappa    Accuracy       Error       Kappa
----                              ----      ------    --------       -----       -----    --------       -----       -----
Corn                                 1       89536       86.7%       13.3%       0.850       89.3%       10.7%       0.879
Cotton                               2       13905       87.7%       12.3%       0.875       93.5%        6.5%       0.934
Sorghum                              4         442       20.4%       79.6%       0.204       63.0%       37.0%       0.629
Soybeans                             5       84515       85.1%       14.9%       0.834       87.9%       12.1%       0.864
Sunflower                            6           1        0.7%       99.3%       0.007        5.6%       94.4%       0.055
Peanuts                             10        3572       82.6%       17.4%       0.825       97.9%        2.1%       0.979
Tobacco                             11          71       30.5%       69.5%       0.305       74.7%       25.3%       0.747
Sweet Corn                          12          44       19.2%       80.8%       0.192       40.0%       60.0%       0.400
Pop or Orn Corn                     13           0        n/a         n/a         n/a         0.0%      100.0%       0.000
Barley                              21         119       19.3%       80.7%       0.193       48.2%       51.8%       0.481
Winter Wheat                        24        1767       41.3%       58.7%       0.411       58.9%       41.1%       0.587
Dbl Crop WinWht/Soybeans            26       23453       82.4%       17.6%       0.819       86.5%       13.5%       0.861
Rye                                 27          31        4.6%       95.4%       0.046       37.3%       62.7%       0.373
Oats                                28          24        6.2%       93.8%       0.062       37.5%       62.5%       0.375
Millet                              29          79       15.8%       84.2%       0.158       65.3%       34.7%       0.653
Canola                              31           6       15.8%       84.2%       0.158       85.7%       14.3%       0.857
Alfalfa                             36        1055       34.3%       65.7%       0.342       57.9%       42.1%       0.578
Other Hay/Non Alfalfa               37       88165       73.9%       26.1%       0.700       75.0%       25.0%       0.712
Dry Beans                           42           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Potatoes                            43         228       75.7%       24.3%       0.757       76.8%       23.2%       0.768
Other Crops                         44         136       35.8%       64.2%       0.358       80.0%       20.0%       0.800
Sweet Potatoes                      46           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Misc Vegs & Fruits                  47           1        3.7%       96.3%       0.037      100.0%        0.0%       1.000
Watermelons                         48           2        6.3%       93.8%       0.062       66.7%       33.3%       0.667
Cucumbers                           50           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Clover/Wildflowers                  58           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Sod/Grass Seed                      59         279       54.2%       45.8%       0.542       77.5%       22.5%       0.775
Fallow/Idle Cropland                61        1447       32.3%       67.7%       0.321       67.3%       32.7%       0.671
Peaches                             67          59       45.0%       55.0%       0.450       75.6%       24.4%       0.756
Apples                              68         446       62.0%       38.0%       0.620       76.5%       23.5%       0.765
Grapes                              69          30       44.8%       55.2%       0.448       63.8%       36.2%       0.638
Christmas Trees                     70         139       50.0%       50.0%       0.500       85.3%       14.7%       0.853
Other Tree Crops                    71           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Pecans                              74           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Aquaculture                         92          52       41.9%       58.1%       0.419       83.9%       16.1%       0.839
Triticale                          205           2        0.9%       99.1%       0.009       12.5%       87.5%       0.125
Asparagus                          207           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Cantaloupes                        209           1        5.9%       94.1%       0.059      100.0%        0.0%       1.000
Broccoli                           214           0        n/a         n/a         n/a         0.0%      100.0%       0.000
Peppers                            216           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Greens                             219           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Squash                             222           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Dbl Crop WinWht/Corn               225         371       30.1%       69.9%       0.300       57.3%       42.7%       0.572
Dbl Crop Oats/Corn                 226           6        9.8%       90.2%       0.098       66.7%       33.3%       0.667
Dbl Crop Triticale/Corn            228         252       27.2%       72.8%       0.271       52.1%       47.9%       0.520
Pumpkins                           229         282       57.4%       42.6%       0.574       81.3%       18.7%       0.813
Dbl Crop WinWht/Sorghum            236          72       32.6%       67.4%       0.326       49.0%       51.0%       0.490
Dbl Crop Barley/Corn               237         420       32.0%       68.0%       0.319       58.7%       41.3%       0.587
Dbl Crop Soybeans/Cotton           239           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Dbl Crop Soybeans/Oats             240         160       24.7%       75.3%       0.247       63.0%       37.0%       0.630
Dbl Crop Corn/Soybeans             241           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Blueberries                        242           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Cabbage                            243          18       30.0%       70.0%       0.300       47.4%       52.6%       0.474
Turnips                            247           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Dbl Crop Barley/Soybeans           254        1015       48.3%       51.7%       0.482       73.9%       26.1%       0.738

*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:

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:

Indirect_Spatial_Reference: West Virginia
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 12711

Column_Count: 14231

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name:

Albers Conical Equal Area as used by mrlc.gov (NLCD). The official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system. FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM), Spheroid WGS84, Datum WGS84. Due to technical restrictions, the online data available free for download through the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered in UTM.

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:

 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: 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 - West Virginia 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:

Network_Resource_Name: <https://nassgeodata.gmu.edu/CropScape/>

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:

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: 20210201
Metadata_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:

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Access_Constraints: No restrictions on the distribution or use of the metadata file
Metadata_Use_Constraints: No restrictions on the distribution or use of the metadata file

2020 West Virginia Cropland Data Layer | NASS/USDA

Metadata: