2020 California Cropland Data Layer | NASS/USDA

Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)
Publication_Date: 20210201
Title: 2020 California Cropland Data Layer | NASS/USDA
Edition: 2020 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place:
USDA, NASS Marketing and Information Services Office, Washington, D.C.
Publisher: USDA, NASS
Other_Citation_Details:
NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape at <https://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>.
Online_Linkage: <https://nassgeodata.gmu.edu/CropScape/>
Description:
Abstract:
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2020 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season.
Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2016 (NLCD 2016).
Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data.
Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL.
The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
Purpose:
The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide planted acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
Supplemental_Information:
If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.
USDA, National Agricultural Statistics Service, 2020 California Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20200423 SCENE IDENTIFIER 720
DEIMOS-1 DATE 20200507 SCENE IDENTIFIER 7B4
DEIMOS-1 DATE 20200513 SCENE IDENTIFIER 7F6
DEIMOS-1 DATE 20200520 SCENE IDENTIFIER 835
DEIMOS-1 DATE 20200524 SCENE IDENTIFIER 855
DEIMOS-1 DATE 20200620 SCENE IDENTIFIER 880
DEIMOS-1 DATE 20200624 SCENE IDENTIFIER 893
DEIMOS-1 DATE 20200630 SCENE IDENTIFIER 8C1

RESOURCESAT-2 LISS-3 DATE 20200421 PATH 246
RESOURCESAT-2 LISS-3 DATE 20200506 PATH 249
RESOURCESAT-2 LISS-3 DATE 20200627 PATH 245
RESOURCESAT-2 LISS-3 DATE 20200707 PATH 247
RESOURCESAT-2 LISS-3 DATE 20200726 PATH 246
RESOURCESAT-2 LISS-3 DATE 20200727 PATH 251
RESOURCESAT-2 LISS-3 DATE 20200731 PATH 247
RESOURCESAT-2 LISS-3 DATE 20200806 PATH 253
RESOURCESAT-2 LISS-3 DATE 20200824 PATH 247
RESOURCESAT-2 LISS-3 DATE 20200829 PATH 248
RESOURCESAT-2 LISS-3 DATE 20200903 PATH 249
RESOURCESAT-2 LISS-3 DATE 20200907 PATH 245
RESOURCESAT-2 LISS-3 DATE 20200912 PATH 246
RESOURCESAT-2 LISS-3 DATE 20200922 PATH 248
RESOURCESAT-2 LISS-3 DATE 20201001 PATH 245

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2016 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATABASE 2016 SHRUBLAND COMPONENT PRODUCTS
USDA, NASS CROPLAND DATA LAYERS 2014-2019
USDA, NASS TREE CROP AND VINEYARD MASKS BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)

SENTINEL-2A DATE 20191103 RELATIVE ORBIT NUMBER 070
SENTINEL-2A DATE 20200425 RELATIVE ORBIT NUMBER 127
SENTINEL-2A DATE 20200504 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200603 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200614 RELATIVE ORBIT NUMBER 127
SENTINEL-2A DATE 20200630 RELATIVE ORBIT NUMBER 070
SENTINEL-2A DATE 20200703 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200707 RELATIVE ORBIT NUMBER 027
SENTINEL-2A DATE 20200714 RELATIVE ORBIT NUMBER 127
SENTINEL-2A DATE 20200717 RELATIVE ORBIT NUMBER 027
SENTINEL-2A DATE 20200802 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200803 RELATIVE ORBIT NUMBER 127
SENTINEL-2A DATE 20200812 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200815 RELATIVE ORBIT NUMBER 013
SENTINEL-2A DATE 20200823 RELATIVE ORBIT NUMBER 127
SENTINEL-2A DATE 20200901 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200921 RELATIVE ORBIT NUMBER 113
SENTINEL-2A DATE 20200928 RELATIVE ORBIT NUMBER 070

SENTINEL-2B DATE 20191105 RELATIVE ORBIT NUMBER 027
SENTINEL-2B DATE 20191112 RELATIVE ORBIT NUMBER 127
SENTINEL-2B DATE 20191118 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20191121 RELATIVE ORBIT NUMBER 113
SENTINEL-2B DATE 20200416 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200526 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200701 RELATIVE ORBIT NUMBER 013
SENTINEL-2B DATE 20200725 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200729 RELATIVE ORBIT NUMBER 127
SENTINEL-2B DATE 20200801 RELATIVE ORBIT NUMBER 027
SENTINEL-2B DATE 20200804 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200808 RELATIVE ORBIT NUMBER 127
SENTINEL-2B DATE 20200827 RELATIVE ORBIT NUMBER 113
SENTINEL-2B DATE 20200830 RELATIVE ORBIT NUMBER 013
SENTINEL-2B DATE 20200831 RELATIVE ORBIT NUMBER 027
SENTINEL-2B DATE 20200903 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200913 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200916 RELATIVE ORBIT NUMBER 113
SENTINEL-2B DATE 20200917 RELATIVE ORBIT NUMBER 127
SENTINEL-2B DATE 20200923 RELATIVE ORBIT NUMBER 070
SENTINEL-2B DATE 20200927 RELATIVE ORBIT NUMBER 127
SENTINEL-2B DATE 20200930 RELATIVE ORBIT NUMBER 027

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2020 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2016
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2020 CROP CLASSIFICATIONS
LAND IQ, TREE CROPS AND VINEYARDS (2014 DATA)
NOTE: The final extent of the CDL is clipped to the state boundary even though the raw input data may encompass a larger area.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Currentness_Reference: 2020 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -124.5876
East_Bounding_Coordinate: -114.1885
North_Bounding_Coordinate: 41.9743
South_Bounding_Coordinate: 32.5028
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010
Theme:
Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:
Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands
Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: DEIMOS-1
Theme_Keyword: ISRO ResourceSat-2 LISS-3
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CropScape
Place:
Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword:
Continent > North America > United States of America > California
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: CA
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2020
Access_Constraints: None
Use_Constraints:
The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: HQ_RDD_GIB@usda.gov
Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:
Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None
Native_Data_Set_Environment:
Microsoft Windows 7 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.7 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <https://www.mrlc.gov/>.
ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.
USDA, National Agricultural Statistics Service, 2020 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only                *Correct  Accuracy   Error  Kappa
-------------------------                 -------  --------  ------  -----
OVERALL ACCURACY**                        405,686     80.8%   19.2%  0.790

Cover                       Attribute    *Correct  Producer's  Omission           User's  Commission  Cond'l
Type                             Code      Pixels    Accuracy     Error  Kappa  Accuracy       Error   Kappa
----                             ----      ------    --------     -----  -----  --------       -----   -----
Corn                                1       11077       76.4%     23.6%  0.760     80.0%       20.0%   0.797
Cotton                              2       16096       91.8%      8.2%  0.916     89.6%       10.4%   0.894
Rice                                3       75500       97.8%      2.2%  0.976     99.2%        0.8%   0.991
Sorghum                             4         155       34.3%     65.7%  0.343     39.7%       60.3%   0.397
Sunflower                           6        3971       78.0%     22.0%  0.779     84.0%       16.0%   0.840
Sweet Corn                         12         104       30.0%     70.0%  0.299     30.5%       69.5%   0.305
Mint                               14         163       78.7%     21.3%  0.787     92.1%        7.9%   0.921
Barley                             21        3627       58.9%     41.1%  0.587     69.4%       30.6%   0.692
Durum Wheat                        22        1202       53.9%     46.1%  0.538     67.6%       32.4%   0.676
Spring Wheat                       23         462       34.1%     65.9%  0.340     53.4%       46.6%   0.533
Winter Wheat                       24       20027       71.8%     28.2%  0.709     67.9%       32.1%   0.669
Rye                                27         173       23.3%     76.7%  0.233     47.9%       52.1%   0.479
Oats                               28        2326       39.1%     60.9%  0.388     57.1%       42.9%   0.569
Speltz                             30           0        0.0%    100.0%  0.000      n/a         n/a     n/a
Safflower                          33        1306       58.8%     41.2%  0.587     70.2%       29.8%   0.701
Mustard                            35           9       31.0%     69.0%  0.310    100.0%        0.0%   1.000
Alfalfa                            36       61562       88.2%     11.8%  0.873     86.1%       13.9%   0.850
Other Hay/Non Alfalfa              37       12147       65.2%     34.8%  0.646     72.5%       27.5%   0.719
Sugarbeets                         41        2552       75.1%     24.9%  0.750     81.4%       18.6%   0.813
Dry Beans                          42         334       64.5%     35.5%  0.645     57.4%       42.6%   0.574
Potatoes                           43         338       55.7%     44.3%  0.557     62.2%       37.8%   0.622
Other Crops                        44         179       47.4%     52.6%  0.473     34.0%       66.0%   0.339
Sugarcane                          45          25       75.8%     24.2%  0.758     96.2%        3.8%   0.962
Sweet Potatoes                     46          37       58.7%     41.3%  0.587     80.4%       19.6%   0.804
Misc Vegs & Fruits                 47           0        0.0%    100.0%  0.000      0.0%      100.0%   0.000
Watermelons                        48          11        2.1%     97.9%  0.021      7.6%       92.4%   0.075
Onions                             49        1515       47.9%     52.1%  0.478     71.3%       28.7%   0.712
Cucumbers                          50         192       83.8%     16.2%  0.838     65.5%       34.5%   0.655
Chick Peas                         51         965       76.1%     23.9%  0.761     75.7%       24.3%   0.757
Peas                               53         134       20.4%     79.6%  0.204     56.8%       43.2%   0.568
Tomatoes                           54       18173       88.1%     11.9%  0.878     83.1%       16.9%   0.827
Herbs                              57         103       27.5%     72.5%  0.275     67.8%       32.2%   0.678
Clover/Wildflowers                 58        2906       86.8%     13.2%  0.868     90.3%        9.7%   0.903
Sod/Grass Seed                     59         321       36.8%     63.2%  0.367     53.1%       46.9%   0.531
Fallow/Idle Cropland               61       20216       72.1%     27.9%  0.714     78.9%       21.1%   0.782
Cherries                           66         986       78.2%     21.8%  0.782     80.4%       19.6%   0.804
Peaches                            67          52       20.5%     79.5%  0.205     34.2%       65.8%   0.342
Apples                             68           0        n/a       n/a    n/a       0.0%      100.0%   0.000
Grapes                             69        8988       88.1%     11.9%  0.879     76.1%       23.9%   0.759
Christmas Trees                    70           0        0.0%    100.0%  0.000      n/a         n/a     n/a
Other Tree Crops                   71          91       44.4%     55.6%  0.444     50.6%       49.4%   0.505
Citrus                             72         150       90.9%      9.1%  0.909      9.3%       90.7%   0.093
Pecans                             74          69       23.8%     76.2%  0.238     71.9%       28.1%   0.719
Almonds                            75       63919       89.3%     10.7%  0.885     87.9%       12.1%   0.869
Walnuts                            76       21031       88.2%     11.8%  0.879     90.6%        9.4%   0.903
Pears                              77         236       97.1%      2.9%  0.971     95.2%        4.8%   0.952
Pistachios                        204       22956       88.5%     11.5%  0.882     89.7%       10.3%   0.894
Triticale                         205        3447       49.6%     50.4%  0.493     66.6%       33.4%   0.664
Carrots                           206         570       43.1%     56.9%  0.430     62.6%       37.4%   0.625
Garlic                            208        1183       48.9%     51.1%  0.489     76.0%       24.0%   0.760
Cantaloupes                       209         146       17.9%     82.1%  0.179     31.7%       68.3%   0.316
Olives                            211        1765       90.6%      9.4%  0.906     84.4%       15.6%   0.843
Oranges                           212         391       33.1%     66.9%  0.330     85.9%       14.1%   0.859
Honeydew Melons                   213         101       26.2%     73.8%  0.262     63.5%       36.5%   0.635
Broccoli                          214         193       44.7%     55.3%  0.446     38.4%       61.6%   0.384
Avocados                          215          85       79.4%     20.6%  0.794     41.7%       58.3%   0.417
Peppers                           216          30       19.9%     80.1%  0.199     52.6%       47.4%   0.526
Pomegranates                      217        1732       98.0%      2.0%  0.980     88.1%       11.9%   0.881
Nectarines                        218          14       20.3%     79.7%  0.203     36.8%       63.2%   0.368
Greens                            219         377       33.9%     66.1%  0.339     54.4%       45.6%   0.543
Plums                             220         813       50.8%     49.2%  0.508     60.9%       39.1%   0.608
Strawberries                      221           8       27.6%     72.4%  0.276     80.0%       20.0%   0.800
Squash                            222          28       41.8%     58.2%  0.418     41.8%       58.2%   0.418
Dbl Crop WinWht/Corn              225       11354       67.6%     32.4%  0.670     64.9%       35.1%   0.642
Dbl Crop Oats/Corn                226        2107       60.5%     39.5%  0.603     59.9%       40.1%   0.597
Lettuce                           227         432       29.7%     70.3%  0.296     51.4%       48.6%   0.513
Dbl Crop Triticale/Corn           228        3910       55.4%     44.6%  0.552     69.2%       30.8%   0.689
Pumpkins                          229          28       28.9%     71.1%  0.289     90.3%        9.7%   0.903
Dbl Crop Lettuce/Cantaloupe       231           0        n/a       n/a    n/a       0.0%      100.0%   0.000
Dbl Crop WinWht/Sorghum           236         510       33.9%     66.1%  0.338     51.3%       48.7%   0.512
Dbl Crop Barley/Corn              237           0        0.0%    100.0%  0.000      0.0%      100.0%   0.000
Dbl Crop WinWht/Cotton            238           5       19.2%     80.8%  0.192     83.3%       16.7%   0.833
Blueberries                       242           0        0.0%    100.0%  0.000      n/a         n/a     n/a
Cabbage                           243          68       32.5%     67.5%  0.325     70.1%       29.9%   0.701
Cauliflower                       244           2        4.1%     95.9%  0.041      2.5%       97.5%   0.025
Celery                            245           0        0.0%    100.0%  0.000      n/a         n/a     n/a
Turnips                           247           1       16.7%     83.3%  0.167    100.0%        0.0%   1.000

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.
The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value:
Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.
Attribute_Accuracy_Explanation:
The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.
Logical_Consistency_Report:
The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.
Completeness_Report: The entire state is covered by the Cropland Data Layer.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Space Research Organization (ISRO)
Publication_Date: 2020
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place:
Indian Space Research Organisation HQ, Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231
Publisher: Indian Space Research Organization (ISRO)
Other_Citation_Details:
The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 24 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: LISS-3
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: European Space Agency (ESA)
Publication_Date: 2020
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: European Commission, Brussels (Belgium)
Publisher: Copernicus - European Commission
Other_Citation_Details:
The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: SENTINEL-2
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: Elecnor Deimos Imaging
Publication_Date: 2020
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Elecnor Deimos Imaging, Valladolid, Spain
Publisher: Astrium GEO Information Services
Other_Citation_Details:
The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <https://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Deimos-1
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS), Earth Resources Observation and Science (EROS)
Publication_Date: 2020
Title:
Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198-001
Publisher: USGS, EROS
Other_Citation_Details:
The Landsat 8 OLI/TIRS data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat 8
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2009
Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details:
The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NED
Source_Contribution:
spatial and attribute information used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2019
Title: National Land Cover Database 2016 (NLCD 2016)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details:
The NLCD 2016 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2016 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The NLCD2016 Tree Canopy data layer was not published in time for use in CDL production, so the NLCD 2011 Tree Canopy data was used. More information on the NLCD 2016 and NLCD 2011 can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2019
Title: National Land Cover Database Shrubland Products
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:
More information can be found at <https://www.mrlc.gov/>. Please reference the following journal article for more details on this data layer: Homer, C.G., C.L. Aldridge, D.K. Meyer, S. Schell. 2012. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA; laying a foundation for monitoring. International Journal of Applied Earth Observation and Geoinformation 14:233-244.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: contact custserv@usgs.gov
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Shrubland Data Layer
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA), Farm Service Agency (FSA)
Publication_Date: 2020
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office
Other_Citation_Details:
Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: FSA CLU
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: LandIQ
Publication_Date: unknown
Title: Statewide Land Use
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Sacramento, California 95811 USA
Publisher: LandIQ
Other_Citation_Details:
More information about LandIQ can be found online at <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: 2014
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: 2020
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:
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: 2020
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: BLM LCRAS GIS Data
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Process_Step:
Process_Description:
OVERVIEW: The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates, most closely aligned with planted acres, produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.'
SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine.
DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships.
GROUND TRUTH: As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground truth data from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground truth data provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <https://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data.
INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season. The DEIMOS-1 and UK-DMC 2 imagery was resampled to 30 meters using cubic convolution, rigorous transformation to match the traditional Landsat spatial resolution. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2016 (NLCD 2016). Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL.
ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report.
PUBLIC RELEASE: The USDA, NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape <https://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.
Process_Date: 2020
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: HQ_RDD_GIB@usda.gov
Cloud_Cover: 0
Spatial_Data_Organization_Information:
Indirect_Spatial_Reference: California
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 35841
Column_Count: 29767
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name:
Albers Conical Equal Area as used by mrlc.gov (NLCD). The official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system. FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM), Spheroid WGS84, Datum WGS84. Due to technical restrictions, the online data available free for download through the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered in UTM.
Albers_Conical_Equal_Area:
Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 30
Ordinate_Resolution: 30
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257223563
Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
Entity_and_Attribute_Detail_Citation:
If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.
 Data Dictionary: USDA, National Agricultural Statistics Service, 2020 Cropland Data Layer

 Source: USDA, National Agricultural Statistics Service

 The following is a cross reference list of the categorization codes and land covers.
 Note that not all land cover categories listed below will appear in an individual state.

 Raster
 Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0

 Categorization Code   Land Cover
         "0"       Background

 Raster
 Attribute Domain Values and Definitions: CROPS 1-60

 Categorization Code   Land Cover
           "1"       Corn
           "2"       Cotton
           "3"       Rice
           "4"       Sorghum
           "5"       Soybeans
           "6"       Sunflower
          "10"       Peanuts
          "11"       Tobacco
          "12"       Sweet Corn
          "13"       Pop or Orn Corn
          "14"       Mint
          "21"       Barley
          "22"       Durum Wheat
          "23"       Spring Wheat
          "24"       Winter Wheat
          "25"       Other Small Grains
          "26"       Dbl Crop WinWht/Soybeans
          "27"       Rye
          "28"       Oats
          "29"       Millet
          "30"       Speltz
          "31"       Canola
          "32"       Flaxseed
          "33"       Safflower
          "34"       Rape Seed
          "35"       Mustard
          "36"       Alfalfa
          "37"       Other Hay/Non Alfalfa
          "38"       Camelina
          "39"       Buckwheat
          "41"       Sugarbeets
          "42"       Dry Beans
          "43"       Potatoes
          "44"       Other Crops
          "45"       Sugarcane
          "46"       Sweet Potatoes
          "47"       Misc Vegs & Fruits
          "48"       Watermelons
          "49"       Onions
          "50"       Cucumbers
          "51"       Chick Peas
          "52"       Lentils
          "53"       Peas
          "54"       Tomatoes
          "55"       Caneberries
          "56"       Hops
          "57"       Herbs
          "58"       Clover/Wildflowers
          "59"       Sod/Grass Seed
          "60"       Switchgrass

 Raster
 Attribute Domain Values and Definitions: NON-CROP 61-65

 Categorization Code   Land Cover
          "61"       Fallow/Idle Cropland
          "63"       Forest
          "64"       Shrubland
          "65"       Barren

 Raster
 Attribute Domain Values and Definitions: CROPS 66-80

 Categorization Code   Land Cover
          "66"       Cherries
          "67"       Peaches
          "68"       Apples
          "69"       Grapes
          "70"       Christmas Trees
          "71"       Other Tree Crops
          "72"       Citrus
          "74"       Pecans
          "75"       Almonds
          "76"       Walnuts
          "77"       Pears

 Raster
 Attribute Domain Values and Definitions: OTHER 81-109

 Categorization Code   Land Cover
          "81"       Clouds/No Data
          "82"       Developed
          "83"       Water
          "87"       Wetlands
          "88"       Nonag/Undefined
          "92"       Aquaculture

 Raster
 Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195

 Categorization Code   Land Cover
         "111"       Open Water
         "112"       Perennial Ice/Snow
         "121"       Developed/Open Space
         "122"       Developed/Low Intensity
         "123"       Developed/Med Intensity
         "124"       Developed/High Intensity
         "131"       Barren
         "141"       Deciduous Forest
         "142"       Evergreen Forest
         "143"       Mixed Forest
         "152"       Shrubland
         "176"       Grassland/Pasture
         "190"       Woody Wetlands
         "195"       Herbaceous Wetlands

 Raster
 Attribute Domain Values and Definitions: CROPS 195-255

 Categorization Code   Land Cover
         "204"       Pistachios
         "205"       Triticale
         "206"       Carrots
         "207"       Asparagus
         "208"       Garlic
         "209"       Cantaloupes
         "210"       Prunes
         "211"       Olives
         "212"       Oranges
         "213"       Honeydew Melons
         "214"       Broccoli
         "215"       Avocados
         "216"       Peppers
         "217"       Pomegranates
         "218"       Nectarines
         "219"       Greens
         "220"       Plums
         "221"       Strawberries
         "222"       Squash
         "223"       Apricots
         "224"       Vetch
         "225"       Dbl Crop WinWht/Corn
         "226"       Dbl Crop Oats/Corn
         "227"       Lettuce
         "228"       Dbl Crop Triticale/Corn
         "229"       Pumpkins
         "230"       Dbl Crop Lettuce/Durum Wht
         "231"       Dbl Crop Lettuce/Cantaloupe
         "232"       Dbl Crop Lettuce/Cotton
         "233"       Dbl Crop Lettuce/Barley
         "234"       Dbl Crop Durum Wht/Sorghum
         "235"       Dbl Crop Barley/Sorghum
         "236"       Dbl Crop WinWht/Sorghum
         "237"       Dbl Crop Barley/Corn
         "238"       Dbl Crop WinWht/Cotton
         "239"       Dbl Crop Soybeans/Cotton
         "240"       Dbl Crop Soybeans/Oats
         "241"       Dbl Crop Corn/Soybeans
         "242"       Blueberries
         "243"       Cabbage
         "244"       Cauliflower
         "245"       Celery
         "246"       Radishes
         "247"       Turnips
         "248"       Eggplants
         "249"       Gourds
         "250"       Cranberries
         "254"       Dbl Crop Barley/Soybeans
Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: 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: HQ_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 - California 2020
Distribution_Liability:
Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (HQ_RDD_GIB@usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GEOTIFF
Format_Version_Date: 2020
Format_Information_Content: GEOTIFF
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: <https://nassgeodata.gmu.edu/CropScape/>
Access_Instructions:
The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. It is also available free for download from the Geospatial Data Gateway website <https://datagateway.nrcs.usda.gov/>.
Fees:
Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Ordering_Instructions:
The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. If you experience problems downloading all years of CDL data through the Geospatial Data Gateway then you can try to use the 'Direct Data Download' link in the lower right-hand corner of their webpage.
Custom_Order_Process:
For a list of other states and years of available data please visit: <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. 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.
Technical_Prerequisites:
If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.
Metadata_Reference_Information:
Metadata_Date: 20210201
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section Staff
Contact_Address:
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: HQ_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 Fri Jan 22 00:18:04 2021