2022 California Cropland Data Layer | USDA NASS

Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
Publication_Date: 20230130
Title: 2022 California Cropland Data Layer | USDA NASS
Edition: 2022 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 CroplandCROS <https://croplandcros.scinet.usda.gov/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>.
Online_Linkage: <https://croplandcros.scinet.usda.gov/>
Description:
Abstract:
The USDA NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2022 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from Landsat 8 and 9 OLI/TIRS, ISRO ResourceSat-2 LISS-3, and ESA SENTINEL-2A and -2B collected during the current growing season.
Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the 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, 2022 California Cropland Data Layer

CLASSIFICATION INPUTS:
LANDSAT 8/9 DATE 20220427 PATH/ORBIT 042
LANDSAT 8/9 DATE 20220516 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220519 PATH/ORBIT 044
LANDSAT 8/9 DATE 20220524 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220528 PATH/ORBIT 043
LANDSAT 8/9 DATE 20220709 PATH/ORBIT 041
LANDSAT 8/9 DATE 20220710 PATH/ORBIT 040
LANDSAT 8/9 DATE 20220711 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220714 PATH/ORBIT 044
LANDSAT 8/9 DATE 20220719 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220721 PATH/ORBIT 045
LANDSAT 8/9 DATE 20220807 PATH/ORBIT 044
LANDSAT 8/9 DATE 20220828 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220830 PATH/ORBIT 045
LANDSAT 8/9 DATE 20220905 PATH/ORBIT 039
LANDSAT 8/9 DATE 20220915 PATH/ORBIT 045
LANDSAT 8/9 DATE 20220924 PATH/ORBIT 044

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2019 IMPERVIOUSNESS
USDA, NASS CROPLAND DATA LAYERS 2016-2021
USDA, NASS TREE CROP AND VINEYARD MASKS BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)

SENTINEL 2A/2B DATE 20211016 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20211028 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20211125 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20211126 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20211128 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20220418 PATH/ORBIT 027
SENTINEL 2A/2B DATE 20220420 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220424 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220427 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20220501 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220504 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220505 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220514 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220516 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220531 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220610 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220621 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20220629 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220630 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220707 PATH/ORBIT 027
SENTINEL 2A/2B DATE 20220711 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20220713 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220715 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220806 PATH/ORBIT 027
SENTINEL 2A/2B DATE 20220812 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220828 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220901 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20220905 PATH/ORBIT 027
SENTINEL 2A/2B DATE 20220907 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220908 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220909 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20220922 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20220923 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20220927 PATH/ORBIT 127
SENTINEL 2A/2B DATE 20221003 PATH/ORBIT 070
SENTINEL 2A/2B DATE 20221006 PATH/ORBIT 113
SENTINEL 2A/2B DATE 20221014 PATH/ORBIT 013
SENTINEL 2A/2B DATE 20221017 PATH/ORBIT 127

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2022 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2019
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2022 CROP CLASSIFICATIONS
LAND IQ, TREE CROPS AND VINEYARDS (2019 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: 20211001
Ending_Date: 20221231
Currentness_Reference: 2022 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: ISRO ResourceSat-2 LISS-3
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 > California
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: CA
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2022
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 CroplandCROS <https://croplandcros.scinet.usda.gov/>.
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; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 2.8 <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 (pre-2007) 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, 2022 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only               *Correct   Accuracy      Error      Kappa
-------------------------                -------   --------     ------      -----
OVERALL ACCURACY**                       415,515      81.4%      18.6%      0.799

Cover                       Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                             Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                             ----     ------   --------      -----      -----   --------      -----      -----
Corn                                1     10,159      71.1%      28.9%      0.707      75.8%      24.2%      0.755
Cotton                              2     16,843      91.6%       8.4%      0.914      87.3%      12.7%      0.871
Rice                                3     34,676      97.6%       2.4%      0.975      98.5%       1.5%      0.984
Sorghum                             4        194      31.9%      68.1%      0.319      45.5%      54.5%      0.455
Sunflower                           6      2,884      74.2%      25.8%      0.741      78.4%      21.6%      0.784
Sweet Corn                         12        163      26.8%      73.2%      0.267      41.2%      58.8%      0.411
Pop or Orn Corn                    13          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Mint                               14        258      74.1%      25.9%      0.741      71.7%      28.3%      0.717
Barley                             21      2,821      58.2%      41.8%      0.581      71.9%      28.1%      0.718
Durum Wheat                        22      2,562      72.4%      27.6%      0.723      81.8%      18.2%      0.817
Spring Wheat                       23        521      44.8%      55.2%      0.448      60.2%      39.8%      0.602
Winter Wheat                       24     21,314      69.1%      30.9%      0.681      68.2%      31.8%      0.672
Rye                                27        433      30.5%      69.5%      0.304      50.5%      49.5%      0.505
Oats                               28      3,728      56.7%      43.3%      0.564      62.9%      37.1%      0.626
Canola                             31          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Flaxseed                           32          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Safflower                          33      7,501      85.7%      14.3%      0.856      88.9%      11.1%      0.888
Mustard                            35          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Alfalfa                            36     60,887      91.1%       8.9%      0.904      85.5%      14.5%      0.845
Other Hay/Non Alfalfa              37     11,559      62.9%      37.1%      0.623      72.6%      27.4%      0.721
Sugarbeets                         41      2,277      84.3%      15.7%      0.843      82.9%      17.1%      0.828
Dry Beans                          42        303      40.9%      59.1%      0.409      75.4%      24.6%      0.754
Potatoes                           43        391      49.1%      50.9%      0.490      65.2%      34.8%      0.651
Other Crops                        44        406      54.6%      45.4%      0.546      68.9%      31.1%      0.689
Sweet Potatoes                     46         79      41.8%      58.2%      0.418      90.8%       9.2%      0.908
Misc Vegs & Fruits                 47          9      31.0%      69.0%      0.310       1.7%      98.3%      0.017
Watermelons                        48        250      46.0%      54.0%      0.459      57.6%      42.4%      0.576
Onions                             49      2,914      72.4%      27.6%      0.723      82.5%      17.5%      0.825
Cucumbers                          50        197      80.7%      19.3%      0.807      55.5%      44.5%      0.555
Chick Peas                         51         56      25.0%      75.0%      0.250      35.2%      64.8%      0.352
Lentils                            52          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Peas                               53          1       0.6%      99.4%      0.006       1.9%      98.1%      0.019
Tomatoes                           54     22,849      86.5%      13.5%      0.861      86.8%      13.2%      0.865
Herbs                              57         68      15.0%      85.0%      0.150      51.1%      48.9%      0.511
Clover/Wildflowers                 58      2,304      90.0%      10.0%      0.900      92.9%       7.1%      0.928
Sod/Grass Seed                     59        333      31.0%      69.0%      0.309      77.8%      22.2%      0.778
Fallow/Idle Cropland               61     64,122      87.7%      12.3%      0.868      90.5%       9.5%      0.897
Cherries                           66        590      83.9%      16.1%      0.839      59.2%      40.8%      0.591
Peaches                            67        169      48.4%      51.6%      0.484      39.3%      60.7%      0.393
Apples                             68          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Grapes                             69      8,365      91.6%       8.4%      0.915      84.4%      15.6%      0.843
Christmas Trees                    70          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Other Tree Crops                   71        151      61.1%      38.9%      0.611      55.1%      44.9%      0.551
Citrus                             72      1,732      88.8%      11.2%      0.888      70.5%      29.5%      0.705
Pecans                             74         98      43.9%      56.1%      0.439      77.2%      22.8%      0.772
Almonds                            75     59,322      91.9%       8.1%      0.914      90.4%       9.6%      0.897
Walnuts                            76     16,953      89.1%      10.9%      0.889      91.1%       8.9%      0.909
Pears                              77        170      95.5%       4.5%      0.955      83.3%      16.7%      0.833
Aquaculture                        92          1      25.0%      75.0%      0.250     100.0%       0.0%      1.000
Pistachios                        204     24,393      89.6%      10.4%      0.893      90.9%       9.1%      0.907
Triticale                         205      3,149      43.4%      56.6%      0.431      52.5%      47.5%      0.521
Carrots                           206        594      44.4%      55.6%      0.443      56.7%      43.3%      0.566
Garlic                            208      1,764      78.9%      21.1%      0.789      73.7%      26.3%      0.737
Cantaloupes                       209        276      30.9%      69.1%      0.309      49.2%      50.8%      0.492
Prunes                            210          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Olives                            211      1,757      88.0%      12.0%      0.880      85.9%      14.1%      0.859
Oranges                           212        500      59.9%      40.1%      0.599      93.5%       6.5%      0.935
Honeydew Melons                   213        159      62.1%      37.9%      0.621      45.3%      54.7%      0.453
Broccoli                          214        130      37.5%      62.5%      0.374      47.8%      52.2%      0.478
Avocados                          215         43      69.4%      30.6%      0.694      32.6%      67.4%      0.326
Peppers                           216         64      35.4%      64.6%      0.354      77.1%      22.9%      0.771
Pomegranates                      217      1,462      97.9%       2.1%      0.979      85.7%      14.3%      0.857
Nectarines                        218          3      14.3%      85.7%      0.143      18.8%      81.3%      0.187
Greens                            219        273      46.6%      53.4%      0.466      51.1%      48.9%      0.511
Plums                             220        556      40.9%      59.1%      0.408      60.0%      40.0%      0.600
Strawberries                      221          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Squash                            222          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Dbl Crop WinWht/Corn              225     10,413      63.0%      37.0%      0.624      64.0%      36.0%      0.634
Dbl Crop Oats/Corn                226      2,581      65.0%      35.0%      0.649      68.6%      31.4%      0.685
Lettuce                           227        588      29.7%      70.3%      0.297      56.2%      43.8%      0.561
Dbl Crop Triticale/Corn           228      5,814      63.5%      36.5%      0.632      67.6%      32.4%      0.673
Pumpkins                          229         42      30.0%      70.0%      0.300      49.4%      50.6%      0.494
Dbl Crop Lettuce/Cantaloupe       231          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop Lettuce/Cotton           232          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop WinWht/Sorghum           236        225      23.2%      76.8%      0.232      52.7%      47.3%      0.526
Dbl Crop Barley/Corn              237         58      36.5%      63.5%      0.365      82.9%      17.1%      0.829
Dbl Crop WinWht/Cotton            238          6       4.3%      95.7%      0.043      30.0%      70.0%      0.300
Blueberries                       242          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Cabbage                           243         50      20.7%      79.3%      0.207      75.8%      24.2%      0.758
Cauliflower                       244          3       5.5%      94.5%      0.055       6.7%      93.3%      0.067
Celery                            245          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Radishes                          246          0       0.0%     100.0%      0.000       n/a        n/a        n/a

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.
The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database. 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. 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 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).
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Space Research Organization (ISRO)
Publication_Date: 2022
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 2022 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: 20211001
Ending_Date: 20221231
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: 2022
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 2022 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: 20211001
Ending_Date: 20221231
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: 2022
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: 20211001
Ending_Date: 20221231
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) 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: 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 2019 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The Tree Canopy data was not available with the NLCD 2019, so the NLCD 2016 Tree Canopy data was used as an ancillary input. 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: 2022
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: 2022
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: 2022
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 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: 2019
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: 2022
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: 2022
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
Process_Step:
Process_Description:
OVERVIEW: 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 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 (pre-2007) 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 Landsat 8 and 9 OLI/TIRS, ISRO ResourceSat-2 LISS-3, and ESA SENTINEL-2A and -2B collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the 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 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: 2022
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: 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). FOR GEOSPATIAL DATA GATEWAY USERS: Due to technical restrictions, the downloadable CDL data available on the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered as Universal Transverse Mercator (UTM), Spheroid WGS84, Datum WGS84.
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, 2022 Cropland Data Layer

 Source: USDA National Agricultural Statistics Service

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

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

 Categorization Code   Land Cover
         "0"       Background

 Raster
 Attribute Domain Values and Definitions: CROPS 1-60

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

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

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

 Raster
 Attribute Domain Values and Definitions: CROPS 66-80

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

 Raster
 Attribute Domain Values and Definitions: OTHER 81-109

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

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

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

 Raster
 Attribute Domain Values and Definitions: CROPS 195-255

 Categorization Code   Land Cover
         "204"       Pistachios
         "205"       Triticale
         "206"       Carrots
         "207"       Asparagus
         "208"       Garlic
         "209"       Cantaloupes
         "210"       Prunes
         "211"       Olives
         "212"       Oranges
         "213"       Honeydew Melons
         "214"       Broccoli
         "215"       Avocados
         "216"       Peppers
         "217"       Pomegranates
         "218"       Nectarines
         "219"       Greens
         "220"       Plums
         "221"       Strawberries
         "222"       Squash
         "223"       Apricots
         "224"       Vetch
         "225"       Dbl Crop WinWht/Corn
         "226"       Dbl Crop Oats/Corn
         "227"       Lettuce
         "228"       Dbl Crop Triticale/Corn
         "229"       Pumpkins
         "230"       Dbl Crop Lettuce/Durum Wht
         "231"       Dbl Crop Lettuce/Cantaloupe
         "232"       Dbl Crop Lettuce/Cotton
         "233"       Dbl Crop Lettuce/Barley
         "234"       Dbl Crop Durum Wht/Sorghum
         "235"       Dbl Crop Barley/Sorghum
         "236"       Dbl Crop WinWht/Sorghum
         "237"       Dbl Crop Barley/Corn
         "238"       Dbl Crop WinWht/Cotton
         "239"       Dbl Crop Soybeans/Cotton
         "240"       Dbl Crop Soybeans/Oats
         "241"       Dbl Crop Corn/Soybeans
         "242"       Blueberries
         "243"       Cabbage
         "244"       Cauliflower
         "245"       Celery
         "246"       Radishes
         "247"       Turnips
         "248"       Eggplants
         "249"       Gourds
         "250"       Cranberries
         "254"       Dbl Crop Barley/Soybeans
Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA NASS Customer Service
Contact_Person: USDA NASS Customer Service Staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5038-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Contact_Instructions:
Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://croplandcros.scinet.usda.gov/> 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 2022
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: 2022
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:
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://croplandcros.scinet.usda.gov/> 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 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: 20230130
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 Jan 17 14:54:58 2023