2024 Cropland Data Layer

Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
Publication_Date: 20250227
Title: 2024 Cropland Data Layer
Edition: 2024 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:
Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079. Data available free for download at <https://croplandcros.scinet.usda.gov/>. Frequently Asked Questions at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.
Online_Linkage: <https://croplandcros.scinet.usda.gov/>
Description:
Abstract:
The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.
New for the 2024 10-meter CDL, the crop classification utilized remote sensing data from harmonized Sentinel-2 MSI Level-2A, Landsat 8, and Landsat 9 Level-2 Collection 2 Tier-1 products, providing surface reflectance (SR) data across multiple spectral bands, including GREEN, RED, NIR, SWIR1, SWIR2, and RedEdge bands 1-4. To mitigate cloud cover, 10-day median composites of surface reflectance and NDVI were created from the cloud-masked Landsat-Sentinel multi-sensor data for the growing season of 2024. An impervious layer from USGS NLCD 2021 and a digital elevation model from USGS 3DEP were also included ancillary input variables. In addition, mixed sampling strategies and localized training and were applied to the 2024 10m CDL production. Additional information: Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079.
The 2024 CDL has a spatial resolution of 10 meters and was produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected throughout the growing season. Additional ancillary inputs were used to supplement and improve the land cover classification including the United States Geological Survey (USGS) 3D Elevation Program (3DEP) Elevation Dataset (NED), and the USGS National Land Cover Database imperviousness data layer. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. Some CDL states incorporate additional crop-specific ground reference obtained from the following non-FSA sources which are detailed in the 'Lineage' Section of this metadata: US Bureau of Reclamation, NASS Citrus Data Layer (internal use only), California Department of Water Resources, Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy, Cornell University grape/vineyard data, Utah Department of Water Resources, and Washington State Department of Agriculture. The 2019 NLCD was used as non-agricultural training and validation data for the 2024 CDL. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view a complete list of imagery, ancillary inputs, and ground reference used for a specific state and year.
Purpose:
The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide supplemental 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:
The data is available free for download through CroplandCROS at <https://croplandcros.scinet.usda.gov/>. Metadata, Frequently Asked Questions (FAQs), and the most current year of data is available free for download at the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20231001
Ending_Date: 20241231
Currentness_Reference: 2024 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: annual updates
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -127.8873
East_Bounding_Coordinate: -74.1585
North_Bounding_Coordinate: 47.9580
South_Bounding_Coordinate: 23.1496
Keywords:
Theme:
Theme_Keyword_Thesaurus: NGDA Portfolio Themes
Theme_Keyword: National Geospatial Data Asset
Theme_Keyword: Land Use Land Cover Theme
Theme_Keyword: NGDA
Theme_Keyword: NGDA109
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: farming
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CroplandCROS
Place:
Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword: Continent > North America > United States of America
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: USA
Place_Keyword: CONUS
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2024
Access_Constraints: none
Use_Constraints:
The USDA NASS Cropland Data Layer and the data offered on the CroplandCROS website 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.
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 10 Enterprise; Google Earth Engine <https://earthengine.google.com/>; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>.
The 2024 CDL is the first time using Google Earth Engine to produce the land cover classification. 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. The CDL methodology from 2007 to 2023 used Rulequest See5.0 software to create a decision-tree based classifier. The NLCD Mapping Tool was used to apply the See5.0 decision-tree via ERDAS Imagine. Pre-2007 CDLs were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Please visit the CDL FAQs at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to verify the methodology used for a specific state and year.
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
If the following table does not display properly, then please visit the CDL Metadata webpage at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original file. Accuracy at the individual state-level can be viewed at the CDL Metadata webpage. 
USDA National Agricultural Statistics Service, 2024 Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only               *Correct   Accuracy      Error      Kappa
-------------------------                -------   --------     ------      -----
OVERALL ACCURACY**                     2,763,127      77.5%      22.5%      0.733

Cover                       Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                             Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                             ----     ------   --------      -----      -----   --------      -----      -----
Corn                                1    926,684      92.6%       7.4%      0.923      92.3%       7.7%      0.919
Cotton                              2    118,322      85.2%      14.8%      0.852      86.2%      13.8%      0.861
Rice                                3     35,276      96.1%       3.9%      0.961      91.3%       8.7%      0.912
Sorghum                             4     60,299      79.9%      20.1%      0.799      69.8%      30.2%      0.697
Soybeans                            5    830,135      91.2%       8.8%      0.908      91.6%       8.4%      0.912
Sunflower                           6      7,181      82.8%      17.2%      0.828      82.8%      17.2%      0.828
Peanuts                            10     18,182      90.7%       9.3%      0.906      74.8%      25.2%      0.747
Tobacco                            11        329      37.9%      62.1%      0.379      70.3%      29.7%      0.703
Sweet Corn                         12      1,389      56.2%      43.8%      0.562      56.6%      43.4%      0.566
Pop or Orn Corn                    13      1,298      82.7%      17.3%      0.827      52.3%      47.7%      0.523
Mint                               14        402      82.5%      17.5%      0.825      76.9%      23.1%      0.769
Barley                             21     16,542      74.3%      25.7%      0.743      65.1%      34.9%      0.650
Durum Wheat                        22     16,332      72.0%      28.0%      0.720      62.6%      37.4%      0.625
Spring Wheat                       23    110,760      82.9%      17.1%      0.828      86.7%      13.3%      0.866
Winter Wheat                       24    204,900      87.1%      12.9%      0.869      81.1%      18.9%      0.809
Other Small Grains                 25         90      33.2%      66.8%      0.332      54.5%      45.5%      0.545
Dbl Crop WinWht/Soybeans           26     36,376      78.4%      21.6%      0.783      81.6%      18.4%      0.815
Rye                                27      2,895      54.7%      45.3%      0.546      36.1%      63.9%      0.360
Oats                               28      6,701      57.3%      42.7%      0.573      36.2%      63.8%      0.362
Millet                             29      5,076      64.7%      35.3%      0.647      52.3%      47.7%      0.523
Speltz                             30         29      15.4%      84.6%      0.154      30.9%      69.1%      0.309
Canola                             31     28,077      93.8%       6.2%      0.938      89.1%      10.9%      0.891
Flaxseed                           32      1,003      61.2%      38.8%      0.612      51.4%      48.6%      0.514
Safflower                          33      1,363      75.4%      24.6%      0.754      77.2%      22.8%      0.772
Rape Seed                          34         25      25.5%      74.5%      0.255      50.0%      50.0%      0.500
Mustard                            35      1,657      82.9%      17.1%      0.829      73.3%      26.7%      0.733
Alfalfa                            36     88,352      79.6%      20.4%      0.795      60.3%      39.7%      0.601
Other Hay/Non Alfalfa              37     13,256      46.8%      53.2%      0.464       8.3%      91.7%      0.082
Camelina                           38        165      44.6%      55.4%      0.446      39.0%      61.0%      0.390
Buckwheat                          39        311      57.9%      42.1%      0.579      71.0%      29.0%      0.710
Sugarbeets                         41     11,233      95.6%       4.4%      0.956      91.1%       8.9%      0.911
Dry Beans                          42     14,668      80.6%      19.4%      0.806      77.0%      23.0%      0.770
Potatoes                           43      8,489      89.7%      10.3%      0.897      87.1%      12.9%      0.871
Other Crops                        44        731      46.0%      54.0%      0.460      54.3%      45.7%      0.543
Sugarcane                          45     13,498      88.5%      11.5%      0.885      90.0%      10.0%      0.900
Sweet Potatoes                     46        935      83.8%      16.2%      0.838      77.6%      22.4%      0.776
Misc Vegs & Fruits                 47         62       9.8%      90.2%      0.098      24.5%      75.5%      0.245
Watermelons                        48        268      45.9%      54.1%      0.459      55.4%      44.6%      0.554
Onions                             49        923      70.1%      29.9%      0.701      76.8%      23.2%      0.768
Cucumbers                          50        306      56.5%      43.5%      0.565      68.0%      32.0%      0.680
Chick Peas                         51      5,280      77.7%      22.3%      0.777      81.7%      18.3%      0.817
Lentils                            52     10,012      82.2%      17.8%      0.822      77.6%      22.4%      0.776
Peas                               53     10,544      81.8%      18.2%      0.818      73.0%      27.0%      0.730
Tomatoes                           54      1,893      78.0%      22.0%      0.780      86.1%      13.9%      0.861
Caneberries                        55         62      51.2%      48.8%      0.512      49.2%      50.8%      0.492
Hops                               56        594      88.9%      11.1%      0.889      81.5%      18.5%      0.815
Herbs                              57        615      53.2%      46.8%      0.532      38.7%      61.3%      0.387
Clover/Wildflowers                 58        490      41.7%      58.3%      0.417      34.8%      65.2%      0.348
Sod/Grass Seed                     59      4,068      60.1%      39.9%      0.600      49.6%      50.4%      0.496
Switchgrass                        60         33      18.8%      81.3%      0.187      30.0%      70.0%      0.300
Fallow/Idle Cropland               61     68,594      84.1%      15.9%      0.840      67.7%      32.3%      0.676
Shrubland                          64     18,064      72.3%      27.7%      0.723      49.2%      50.8%      0.492
Cherries                           66        805      52.9%      47.1%      0.529      47.0%      53.0%      0.470
Peaches                            67        597      45.5%      54.5%      0.455      48.4%      51.6%      0.484
Apples                             68      2,715      63.5%      36.5%      0.635      74.8%      25.2%      0.747
Grapes                             69     10,441      71.9%      28.1%      0.719      76.3%      23.7%      0.763
Christmas Trees                    70         68       9.7%      90.3%      0.097      14.3%      85.7%      0.143
Other Tree Crops                   71        275      30.8%      69.2%      0.308      44.0%      56.0%      0.440
Citrus                             72      3,974      58.5%      41.5%      0.585      75.1%      24.9%      0.751
Pecans                             74      2,706      76.6%      23.4%      0.766      49.9%      50.1%      0.499
Almonds                            75     23,975      86.3%      13.7%      0.863      86.5%      13.5%      0.865
Walnuts                            76      5,826      88.7%      11.3%      0.887      72.3%      27.7%      0.723
Pears                              77        293      56.0%      44.0%      0.560      52.6%      47.4%      0.526
Aquaculture                        92      4,555      85.3%      14.7%      0.853      80.7%      19.3%      0.807
Pistachios                        204      8,607      88.9%      11.1%      0.889      85.7%      14.3%      0.857
Triticale                         205      2,559      46.5%      53.5%      0.465      25.7%      74.3%      0.257
Carrots                           206        147      54.6%      45.4%      0.546      56.5%      43.5%      0.565
Asparagus                         207         17      34.0%      66.0%      0.340      54.8%      45.2%      0.548
Garlic                            208        144      66.4%      33.6%      0.664      76.6%      23.4%      0.766
Cantaloupes                       209         57      34.8%      65.2%      0.348      60.0%      40.0%      0.600
Prunes                            210        245      54.1%      45.9%      0.541      36.5%      63.5%      0.365
Olives                            211        479      73.6%      26.4%      0.736      45.7%      54.3%      0.457
Oranges                           212      3,617      61.0%      39.0%      0.610      58.5%      41.5%      0.585
Honeydew Melons                   213          2      10.5%      89.5%      0.105      11.1%      88.9%      0.111
Broccoli                          214         61      36.7%      63.3%      0.367      47.7%      52.3%      0.477
Avocados                          215        353      69.9%      30.1%      0.699      48.9%      51.1%      0.489
Peppers                           216        108      38.3%      61.7%      0.383      54.5%      45.5%      0.545
Pomegranates                      217        206      83.7%      16.3%      0.837      51.2%      48.8%      0.512
Nectarines                        218          1       5.3%      94.7%      0.053      14.3%      85.7%      0.143
Greens                            219         63      39.1%      60.9%      0.391      40.9%      59.1%      0.409
Plums                             220         20      14.6%      85.4%      0.146       5.7%      94.3%      0.057
Strawberries                      221         26      15.7%      84.3%      0.157      45.6%      54.4%      0.456
Squash                            222         51      24.4%      75.6%      0.244      45.1%      54.9%      0.451
Apricots                          223          5       7.5%      92.5%      0.075       6.2%      93.8%      0.062
Vetch                             224         64      57.7%      42.3%      0.577      59.8%      40.2%      0.598
Dbl Crop WinWht/Corn              225      1,796      46.1%      53.9%      0.460      48.5%      51.5%      0.485
Dbl Crop Oats/Corn                226        315      47.9%      52.1%      0.479      48.8%      51.2%      0.488
Lettuce                           227         97      41.6%      58.4%      0.416      27.1%      72.9%      0.271
Dbl Crop Triticale/Corn           228      1,841      47.0%      53.0%      0.469      61.8%      38.2%      0.618
Pumpkins                          229        226      36.3%      63.7%      0.363      57.1%      42.9%      0.571
Dbl Crop Lettuce/Durum Wht        230          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop Lettuce/Cantaloupe       231         56      48.7%      51.3%      0.487      90.3%       9.7%      0.903
Dbl Crop Lettuce/Cotton           232         97      68.3%      31.7%      0.683      84.3%      15.7%      0.843
Dbl Crop Lettuce/Barley           233          1      20.0%      80.0%      0.200      25.0%      75.0%      0.250
Dbl Crop WinWht/Sorghum           236      1,725      56.4%      43.6%      0.564      33.3%      66.7%      0.333
Dbl Crop Barley/Corn              237        187      39.0%      61.0%      0.390      57.9%      42.1%      0.579
Dbl Crop WinWht/Cotton            238        407      32.9%      67.1%      0.329      18.2%      81.8%      0.182
Dbl Crop Soybeans/Cotton          239          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop Soybeans/Oats            240        160      29.5%      70.5%      0.295      37.0%      63.0%      0.370
Dbl Crop Corn/Soybeans            241         31      32.6%      67.4%      0.326      48.4%      51.6%      0.484
Blueberries                       242        368      45.4%      54.6%      0.454      39.1%      60.9%      0.391
Cabbage                           243         90      49.2%      50.8%      0.492      46.9%      53.1%      0.469
Cauliflower                       244         10      28.6%      71.4%      0.286      20.4%      79.6%      0.204
Celery                            245          6      14.3%      85.7%      0.143      25.0%      75.0%      0.250
Radishes                          246         28      46.7%      53.3%      0.467      50.9%      49.1%      0.509
Turnips                           247         19      38.8%      61.2%      0.388      48.7%      51.3%      0.487
Eggplants                         248          1      10.0%      90.0%      0.100      50.0%      50.0%      0.500
Gourds                            249          3      17.6%      82.4%      0.176      60.0%      40.0%      0.600
Cranberries                       250         18      17.6%      82.4%      0.176      69.2%      30.8%      0.692
Dbl Crop Barley/Soybeans          254        433      47.5%      52.5%      0.475      63.0%      37.0%      0.630

*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. 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.
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. 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 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 Continental United States unless noted otherwise in the 'Completeness Report' section of this metadata file.
Completeness_Report: The 2024 CDL covers the Continental United States.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 and 9 OLI/TIRS imagery uses the Collection 2 Level-1 specifications. Please reference the metadata on the USGS Glovis website for the positional accuracy of each Landsat scene. The Sentinel 2A and 2B imagery uses using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please reference the metadata on the Copernicus website for positional accuracy details.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: European Space Agency (ESA)
Publication_Date: 2024
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.
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: 20231001
Ending_Date: 20241231
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:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS)
Publication_Date: 2024
Title:
Landsat 8 and 9 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 and 9 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: 20231001
Ending_Date: 20241231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat 8 and Landsat 9
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) National Geospatial Program
Publication_Date: 2024
Title: 3D Elevation Program (3DEP)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS
Other_Citation_Details:
The 3D Elevation Program (3DEP) is used as an ancillary data source in the production of the Cropland Data Layer. More information can be found at <https://www.usgs.gov/3d-elevation-program>. 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: 24000
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: 3DEP
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: 2021
Title: National Land Cover Database 2019 (NLCD 2019)
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 2019 land cover was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2021 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 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: 2024
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: 2024
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
Source_Information:
Source_Citation:
Citation_Information:
Originator: California Department of Water Resources (DWR)
Publication_Date: 2024
Title: Statewide Land Use 2023 (Provisional)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Sacramento, California 94236-0001 USA
Publisher: California Department of Water Resources (DWR)
Other_Citation_Details:
(California only dataset) The California Department of Water Resources Land Use Program data is used as additional crop-specific ground reference training and validation for tree crops and vineyards in California. More information about California Department of Water Resources Land Use Program can be found online at <https://data.cnra.ca.gov/dataset/statewide-crop-mapping> and <https://www.landiq.com/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2023
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: LandIQ
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Department of Interior, Bureau of Reclamation, Lower Colorado Region
Publication_Date: 2024
Title:
Lower Colorado River Water Accounting System (LCRAS) GIS data layer
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Boulder City, NV 89006-1470, USA
Publisher:
United States Department of Interior, Bureau of Reclamation, Lower Colorado Region
Other_Citation_Details:
(Arizona and California only dataset) The Lower Colorado River Water Accounting System (LCRAS) GIS data layer contains an annually updated record of crop types that was used to supplement the training and validation of the Cropland Data Layer. The area covered is Southern California and Southwest Arizona. For more details, please reference the Bureau of Reclamation website <https://www.usbr.gov/lc/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: LCRAS GIS Data
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
Publication_Date: 2024
Title: USDA NASS Citrus Grove Data Layer
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Maitland, Florida 32751-7057 USA
Publisher: USDA NASS Florida Field Office
Other_Citation_Details:
(Florida only dataset) The Citrus Grove Data Layer is used as additional citrus training and validation ground reference data. Access to the USDA National Agricultural Statistics Service (NASS) Citrus Grove Data Layer is unpublished, for internal NASS use only.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: NASS Citrus Grove Data Layer
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: Florida Department of Agriculture and Consumer Services
Publication_Date: 2020
Title:
Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Tallahassee, Florida 32399-0800 USA
Publisher: Florida Department of Agriculture and Consumer Services
Other_Citation_Details:
(Florida only dataset) The Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase provides additional training and validation ground reference for Florida specialty tree crops. More information about this data set can be found online at <https://www.fdacs.gov/Agriculture-Industry/Water/Agricultural-Water-Supply-Planning>.
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
Source_Citation_Abbreviation: FSAID
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: Cornell Cooperative Extension, Lake Erie Regional Grape Program
Publication_Date: 2024
Title: GIS Mapping of Lake Erie Vineyards
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Portland, NY, 14769 USA
Publisher: Lake Erie Regional Grape Program at CLEREL - Cornell University
Other_Citation_Details:
(New York, Ohio and Pennsylvania only dataset) The Lake Erie Vineyards GIS data provides additional training and validation data for vineyards. More information can be found at <https://lergp.cce.cornell.edu/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Lake Erie Vineyards GIS data
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: Utah Division of Water Resources
Publication_Date: 2024
Title: Agriculture Check Polygons
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Salt Lake City, Utah 84116 USA
Publisher: Utah Division of Water Resources
Other_Citation_Details:
(Utah only dataset) The Utah Division of Water Resources Agriculture Check Polygon data provides additional training and validation data for Utah's cropland.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: Utah DWR Agriculture Check Polygons
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: Washington State Department of Agriculture (WSDA)
Publication_Date: 2024
Title: WSDA Crop Geodatabase
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Olympia, WA 98504-2560 USA
Publisher: Washington State Department of Agriculture
Other_Citation_Details:
(Washington only dataset) The WSDA Crop Geodatabase provides additional training and validation data for Washington's orchards, vineyards and small acreage crops. More information about the WSDA Crop Geodatabase can be found at <https://agr.wa.gov/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: WSDA Crop Geodatabase
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Process_Step:
Process_Description:
OVERVIEW: NEW 10-METER CDL: The crop classification utilized remote sensing data from harmonized Sentinel-2 MSI Level-2A, Landsat 8, and Landsat 9 Level-2 Collection 2 Tier-1 products, providing surface reflectance (SR) data across multiple spectral bands, including GREEN, RED, NIR, SWIR1, SWIR2, and RedEdge bands 1-4. To mitigate cloud cover, 10-day median composites of surface reflectance and NDVI were created from the cloud-masked Landsat-Sentinel multi-sensor data for the growing season of 2024. An impervious layer from USGS NLCD 2021 and a digital elevation model from USGS 3DEP were also included ancillary input variables. In addition, mixed sampling strategies and localized training and were applied to the 2024 10m CDL production. Additional information: Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079.
FOR MORE TECHNICAL DETAILS AND PROGRAM HISTORY: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php> 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 CDL acreage estimates, which most closely aligned with planted acres, are not simple pixel counting but regression estimates using NASS survey data. It is more of an 'Adjusted Census by Satellite.'
SOFTWARE: New for the 2024 CDL a random forest classifier in Google Earth Engine was used to create the classification. 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.
RANDOM FOREST CLASSIFIER: The 2024 Cropland Data Layer uses a random forest classifier approach. This is a departure from previous CDLs (2008-2023) that used a decision tree classifier using See5 software. Older CDLs (pre-2007) had limited ground reference training and less satellite imagery inputs and used a maximum likelihood classifier approach.
GROUND TRUTH: As with the maximum likelihood method and decision tree classifiers, random forest is a supervised classification technique. Thus, it relies on having a sample of known ground reference areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground reference from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground reference 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 2024 CDL has a spatial resolution of 10 meters and was produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected throughout the growing season. Additional ancillary inputs were used to supplement and improve the land cover classification including the United States Geological Survey (USGS) 3D Elevation Program (3DEP) data and the USGS National Land Cover Database imperviousness data. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The USGS NLCD is used as non-agricultural training and validation data. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view complete lists of imagery, ancillary inputs and training and validation used for a specific state and year.
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. Please visit the CDL FAQs and metadata webpages at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> to view or download full accuracy reports by state and year.
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 CroplandCROS <https://croplandcros.scinet.usda.gov/> 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: 2024
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA NASS, Spatial Analysis Research Section
Contact_Person: USDA NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Cloud_Cover: 0
Spatial_Data_Organization_Information:
Indirect_Spatial_Reference: Continental United States
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 289567
Column_Count: 461431
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area as used by mrlc.gov (NLCD)
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: 10
Ordinate_Resolution: 10
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257223563
Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
Entity_and_Attribute_Detail_Citation:
If the following table does not display properly, then please visit the following website to view the original metadata at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. 
 Data Dictionary: USDA National Agricultural Statistics Service, 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
          "62"       Pasture/Grass
          "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 CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Resource_Description: 2024 Cropland Data Layer
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 maintains a Frequently Asked Questions (FAQ's) section 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: 2024
Format_Information_Content: GEOTIFF
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: <https://croplandcros.scinet.usda.gov/>
Access_Instructions:
The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>.
Fees:
The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>, the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>, and the NASS CDL website <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/>. Distribution questions can be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Ordering_Instructions:
The CDL is available online and free for download at CroplandCROS <https://croplandcros.scinet.usda.gov/>, the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>, and the NASS CDL website <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/>. Distribution questions can 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 CroplandCROS <https://croplandcros.scinet.usda.gov/>.
Metadata_Reference_Information:
Metadata_Date: 20250227
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:
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
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
Generated by mp version 2.9.50 on Tue Mar 04 10:48:41 2025