2019 Illinois 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: 20200203
Title: 2019 Illinois Cropland Data Layer | NASS/USDA
Edition: 2019 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/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

Online_Linkage: <https://nassgeodata.gmu.edu/CropScape/IL>

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2019 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 and UK2, 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 data layer from the USGS National Land Cover Database 2016 (NLCD 2016) and the tree canopy data layer from the NLCD 2011.

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:

SPECIAL NOTE FOR THE 2019 CDL PRODUCTS: Traditionally the CDL uses FSA CLU data for agricultural training with a 30 meter inward buffer applied. The inward buffering removes spectrally mixed field edge pixels from the land cover classifier. Starting with the 2019 CDL products, the inward buffer has been changed from 30 meters to 15 meters. The result is a noticeable increase in crop identification at field borders which impacts the CDL, the Cultivated Layer, and Crop Frequency Layers.

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, 2019 Illinois Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20190421 SCENE IDENTIFIER DC8
DEIMOS-1 DATE 20190428 SCENE IDENTIFIER E06
DEIMOS-1 DATE 20190505 SCENE IDENTIFIER E4D
DEIMOS-1 DATE 20190515 SCENE IDENTIFIER EAB
DEIMOS-1 DATE 20190522 SCENE IDENTIFIER EEA
DEIMOS-1 DATE 20190601 SCENE IDENTIFIER F35
DEIMOS-1 DATE 20190608 SCENE IDENTIFIER F80
DEIMOS-1 DATE 20190611 SCENE IDENTIFIER F9D
DEIMOS-1 DATE 20190618 SCENE IDENTIFIER FE9
DEIMOS-1 DATE 20190625 SCENE IDENTIFIER 030
DEIMOS-1 DATE 20190712 SCENE IDENTIFIER 0CF

RESOURCESAT-2 LISS-3 20190428 PATH 275
RESOURCESAT-2 LISS-3 20190508 PATH 277
RESOURCESAT-2 LISS-3 20190728 PATH 274
RESOURCESAT-2 LISS-3 20190802 PATH 275
RESOURCESAT-2 LISS-3 20190807 PATH 276

LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190523 PATH 024
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190601 PATH 023
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190626 PATH 022
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190719 PATH 023
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190827 PATH 024
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190829 PATH 022
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20190905 PATH 023

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATABASE 2011 TREE CANOPY
USDA, NASS CROPLAND DATA LAYERS 2008-2018

SENTINEL-2A DATE 20181122 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190504 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190514 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190531 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190610 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190613 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190630 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190703 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190720 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190723 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190802 RELATIVE ORBIT NUMBER 026
SENTINEL-2A DATE 20190809 RELATIVE ORBIT NUMBER 126
SENTINEL-2A DATE 20190829 RELATIVE ORBIT NUMBER 126

SENTINEL-2B DATE 20181018 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20181021 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20181110 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190426 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190516 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190519 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190529 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190605 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190708 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190718 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190725 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190804 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190814 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190824 RELATIVE ORBIT NUMBER 126
SENTINEL-2B DATE 20190827 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190906 RELATIVE ORBIT NUMBER 026
SENTINEL-2B DATE 20190913 RELATIVE ORBIT NUMBER 126

UK-DMC-2 DATE 20190426 SCENE IDENTIFIER C7F
UK-DMC-2 DATE 20190513 SCENE IDENTIFIER D06
UK-DMC-2 DATE 20190523 SCENE IDENTIFIER D62
UK-DMC-2 DATE 20190602 SCENE IDENTIFIER DC1
UK-DMC-2 DATE 20190705 SCENE IDENTIFIER EF9

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2019 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: 20181001
Ending_Date: 20191231

Currentness_Reference: 2019 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -91.6755
East_Bounding_Coordinate: -87.4834
North_Bounding_Coordinate: 42.4706
South_Bounding_Coordinate: 36.9893

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: UK-DMC 2
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 > Illinois

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Illinois
Place_Keyword: IL

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2019

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 2016 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3.1 <https://www.esri.com/>; Rulequest See5.0 Release 2.11 <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:

***NOTE ABOUT THE UNBUFFERED VALIDATION ACCURACY TABLES BEGINNING IN 2016: The training and validation data used to create and accuracy assess the CDL has traditionally been based on ground truth data that is buffered inward 30 meters. This was done 1) because satellite imagery (as well as the polygon reference data) in the past was not georeferenced to the same precision as now (i.e. everything "stacked" less perfectly), 2) to eliminate from training spectrally-mixed pixels at land cover boundaries, and 3) to be spatially conservative during the era when coarser 56 meter AWiFS satellite imagery was incorporated. Ultimately, all of these scenarios created "blurry" edge pixels through the seasonal time series which it was found if ignored from training in the classification helped improve the quality of CDL. However, the accuracy assessment portion of the analysis also used buffered data meaning those same edge pixels were not assessed fully with the rest of the classification. This would be inconsequential if those edge pixels were similar in nature to the rest of the scene but they are not- they tend to be more difficult to classify correctly. Thus, the accuracy assessments as have been presented are inflated somewhat. Beginning with the 2016 CDL season we are creating CDL accuracy assessments using unbuffered validation data. These "unbuffered" accuracy metrics will now reflect the accuracy of field edges which have not been represented previously. Beginning with the 2016 CDLs we published both the traditional "buffered" accuracy metrics and the new "unbuffered" accuracy assessments. The purpose of publishing both versions is to provide a benchmark for users interested in comparing the different validation methods. For the 2019 CDL season we are now only publishing the unbuffered accuracy only publishing the unbuffered accuracy assessments within the official metadata files and offer the full "unbuffered" error matrices for download on the FAQs webpage. Both metadata and FAQs are accessible at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. We plan to continue producing these unbuffered accuracy assessments for future CDLs. However, there are no plans to create these unbuffered accuracy assessments for past years. It should be noted that accuracy assessment is challenging and the CDL group has always strived to provide robust metrics of usability to the land cover community. This admission of modestly inflated accuracy measures does not render past assessments useless. They were all done consistently so comparison across years and/or states is still valid. Yet, by providing both scenarios for 2016 gives guidance on the bias. 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, 2019 Illinois Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only                 *Correct    Accuracy       Error       Kappa
-------------------------                  -------    --------      ------       -----
OVERALL ACCURACY**                         584,021       89.2%       10.8%       0.825

Cover                        Attribute    *Correct  Producer's    Omission                  User's  Commission      Cond'l
Type                              Code      Pixels    Accuracy       Error       Kappa    Accuracy       Error       Kappa
----                              ----      ------    --------       -----       -----    --------       -----       -----
Corn                                 1     282,022       92.7%        7.3%       0.894       96.4%        3.6%       0.947
Rice                                 3           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Sorghum                              4         146       29.6%       70.4%       0.295       90.1%        9.9%       0.901
Soybeans                             5     255,004       91.7%        8.3%       0.884       94.9%        5.1%       0.927
Sunflower                            6           1       20.0%       80.0%       0.200      100.0%        0.0%       1.000
Tobacco                             11           2       40.0%       60.0%       0.400      100.0%        0.0%       1.000
Sweet Corn                          12          88       36.4%       63.6%       0.364       85.4%       14.6%       0.854
Pop or Orn Corn                     13         463       71.9%       28.1%       0.719       94.7%        5.3%       0.947
Barley                              21           1       16.7%       83.3%       0.167      100.0%        0.0%       1.000
Spring Wheat                        23           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Winter Wheat                        24       1,973       61.2%       38.8%       0.611       78.8%       21.2%       0.787
Dbl Crop WinWht/Soybeans            26       9,978       82.2%       17.8%       0.820       89.6%       10.4%       0.895
Rye                                 27          52       34.7%       65.3%       0.347       88.1%       11.9%       0.881
Oats                                28         133       36.8%       63.2%       0.368       92.4%        7.6%       0.924
Millet                              29           3       15.0%       85.0%       0.150       60.0%       40.0%       0.600
Canola                              31           0        n/a         n/a         n/a         0.0%      100.0%       0.000
Alfalfa                             36       1,357       37.9%       62.1%       0.378       64.3%       35.7%       0.642
Other Hay/Non Alfalfa               37         753       13.6%       86.4%       0.134       44.6%       55.4%       0.443
Sugarbeets                          41           1       14.3%       85.7%       0.143      100.0%        0.0%       1.000
Dry Beans                           42          17       26.2%       73.8%       0.262       81.0%       19.0%       0.810
Potatoes                            43         129       64.8%       35.2%       0.648       92.8%        7.2%       0.928
Other Crops                         44          15       27.3%       72.7%       0.273       88.2%       11.8%       0.882
Misc Vegs & Fruits                  47           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Watermelons                         48           8       61.5%       38.5%       0.615       50.0%       50.0%       0.500
Peas                                53          19       32.8%       67.2%       0.328       86.4%       13.6%       0.864
Herbs                               57           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Clover/Wildflowers                  58          20       10.3%       89.7%       0.103       62.5%       37.5%       0.625
Sod/Grass Seed                      59         169       61.9%       38.1%       0.619       83.7%       16.3%       0.837
Switchgrass                         60           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Fallow/Idle Cropland                61      31,370       79.1%       20.9%       0.783       85.4%       14.6%       0.848
Peaches                             67           6       16.7%       83.3%       0.167       85.7%       14.3%       0.857
Apples                              68           1       11.1%       88.9%       0.111       50.0%       50.0%       0.500
Christmas Trees                     70           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Other Tree Crops                    71           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Walnuts                             76          26       49.1%       50.9%       0.491       72.2%       27.8%       0.722
Triticale                          205           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Cantaloupes                        209           2       50.0%       50.0%       0.500      100.0%        0.0%       1.000
Peppers                            216           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Greens                             219           6       66.7%       33.3%       0.667      100.0%        0.0%       1.000
Squash                             222           1       10.0%       90.0%       0.100      100.0%        0.0%       1.000
Dbl Crop WinWht/Corn               225          45       31.0%       69.0%       0.310       80.4%       19.6%       0.804
Dbl Crop Triticale/Corn            228           7       53.8%       46.2%       0.538       63.6%       36.4%       0.636
Pumpkins                           229         147       49.7%       50.3%       0.497       93.6%        6.4%       0.936
Dbl Crop WinWht/Sorghum            236          54       46.6%       53.4%       0.465       94.7%        5.3%       0.947
Dbl Crop Corn/Soybeans             241           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Cabbage                            243           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Gourds                             249           2       50.0%       50.0%       0.500       50.0%       50.0%       0.500
Dbl Crop Barley/Soybeans           254           0        0.0%      100.0%       0.000        n/a         n/a         n/a

*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 and DMC-UK 2 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)
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Indian Space Research Organization (ISRO)
Publication_Place:

Indian Space Research Organisation HQ, Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231

Publication_Date: 2019
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 2019 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: 20181001
Ending_Date: 20191231

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)
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Copernicus - European Commission
Publication_Place: European Commission, Brussels (Belgium)

Publication_Date: 2019
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 2019 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: 20181001
Ending_Date: 20191231

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
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Astrium GEO Information Services
Publication_Place: Elecnor Deimos Imaging, Valladolid, Spain

Publication_Date: 2019
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: 20181001
Ending_Date: 20191231

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: DMC International Imaging
Title: UK-DMC 2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Astrium GEO Information Services
Publication_Place: DMC International Imaging, Guildford, Surrey UK

Publication_Date: 2019
Other_Citation_Details:

The UK-DMC 2 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about UK-DMC 2 data can be obtained at <http://www.dmcii.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

The UK-DMC 2 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: 20181001
Ending_Date: 20191231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: UK-DMC 2
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)

Title:

Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS
Publication_Place: Sioux Falls, South Dakota 57198-001

Publication_Date: 2019
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: 20181001
Ending_Date: 20191231

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

Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: 2009
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

Title: National Land Cover Database 2016 (NLCD 2016)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: 2019
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. Preferred NLCD2006 citation: "Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2012. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."

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)

Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: USDA, FSA Aerial Photography Field Office

Publication_Place: Salt Lake City, Utah 84119-2020 USA

Publication_Date: 2019
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: 2019

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 and UK2, 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 data layer from the USGS National Land Cover Database 2016 (NLCD 2016) and the tree canopy data layer from the NLCD 2011. 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/>. See the 'Ordering Instructions' section of this metadata file for detailed download instructions. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2019
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: Illinois
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 20629

Column_Count: 11380

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. The UTM Zones are as follows: Zone 11 - California, Idaho, Nevada, Oregon, Washington; Zone 12 - Arizona, Montana, Utah; Zone 13 - Colorado, New Mexico, Wyoming; Zone 14 - Kansas, North Dakota, Nebraska, Oklahoma, South Dakota, Texas; Zone 15 - Arkansas, Iowa, Louisiana, Minnesota, Missouri; Zone 16 - Alabama, Illinois, Indiana, Kentucky, Michigan, Mississippi, Tennessee; Zone 17 - Florida, Georgia, North Carolina, Ohio, South Carolina, Virginia, West Virginia; Zone 18 - Connecticut, Delaware, Maryland, New Jersey, New York, Pennsylvania, Vermont; Zone 19 - Maine, Massachusetts, New Hampshire, Rhode Island.

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, 2019 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 - Illinois 2019
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: 2019
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/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

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: 20200203
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

2019 Illinois Cropland Data Layer | NASS/USDA

Metadata: