2020 Michigan 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 Michigan 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 Michigan Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20200501 SCENE IDENTIFIER 771
DEIMOS-1 DATE 20200512 SCENE IDENTIFIER 7E5
DEIMOS-1 DATE 20200625 SCENE IDENTIFIER 896
DEIMOS-1 DATE 20200628 SCENE IDENTIFIER 8AE

RESOURCESAT-2 LISS-3 DATE 20191009 PATH 279
RESOURCESAT-2 LISS-3 DATE 20200531 PATH 278
RESOURCESAT-2 LISS-3 DATE 20200615 PATH 281
RESOURCESAT-2 LISS-3 DATE 20200629 PATH 279
RESOURCESAT-2 LISS-3 DATE 20200704 PATH 280
RESOURCESAT-2 LISS-3 DATE 20200821 PATH 280
RESOURCESAT-2 LISS-3 DATE 20201008 PATH 280
RESOURCESAT-2 LISS-3 DATE 20201013 PATH 281

LANDSAT 8 OLI/TIRS REAL-TIME DATE 20191009 PATH 021
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20191018 PATH 020
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200504 PATH 021
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200513 PATH 020
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200520 PATH 021
LANDSAT 8 OLI/TIRS REAL-TIME DATE 20200630 PATH 020

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USDA, NASS CROPLAND DATA LAYERS 2014-2019
USDA, NRCS NATIONAL COMMODITY CROP PRODUCTIVITY INDEX (NCCPI)

SENTINEL-2A DATE 20191008 ORBIT NUMBER 126
SENTINEL-2A DATE 20191124 ORBIT NUMBER 083
SENTINEL-2A DATE 20200512 ORBIT NUMBER 083
SENTINEL-2A DATE 20200601 ORBIT NUMBER 083
SENTINEL-2A DATE 20200611 ORBIT NUMBER 083
SENTINEL-2A DATE 20200614 ORBIT NUMBER 126
SENTINEL-2A DATE 20200617 ORBIT NUMBER 026
SENTINEL-2A DATE 20200701 ORBIT NUMBER 083
SENTINEL-2A DATE 20200707 ORBIT NUMBER 026
SENTINEL-2A DATE 20200708 ORBIT NUMBER 040
SENTINEL-2A DATE 20200724 ORBIT NUMBER 126
SENTINEL-2A DATE 20200807 ORBIT NUMBER 040
SENTINEL-2A DATE 20200820 ORBIT NUMBER 083
SENTINEL-2A DATE 20200827 ORBIT NUMBER 040
SENTINEL-2A DATE 20200916 ORBIT NUMBER 040
SENTINEL-2A DATE 20200919 ORBIT NUMBER 083
SENTINEL-2A DATE 20200926 ORBIT NUMBER 040
SENTINEL-2A DATE 20200929 ORBIT NUMBER 083
SENTINEL-2A DATE 20201009 ORBIT NUMBER 083
SENTINEL-2A DATE 20201016 ORBIT NUMBER 040
SENTINEL-2A DATE 20201108 ORBIT NUMBER 083

SENTINEL-2B DATE 20191007 ORBIT NUMBER 040
SENTINEL-2B DATE 20191026 ORBIT NUMBER 026
SENTINEL-2B DATE 20200420 ORBIT NUMBER 126
SENTINEL-2B DATE 20200427 ORBIT NUMBER 083
SENTINEL-2B DATE 20200507 ORBIT NUMBER 083
SENTINEL-2B DATE 20200520 ORBIT NUMBER 126
SENTINEL-2B DATE 20200612 ORBIT NUMBER 026
SENTINEL-2B DATE 20200613 ORBIT NUMBER 040
SENTINEL-2B DATE 20200616 ORBIT NUMBER 083
SENTINEL-2B DATE 20200629 ORBIT NUMBER 126
SENTINEL-2B DATE 20200703 ORBIT NUMBER 040
SENTINEL-2B DATE 20200706 ORBIT NUMBER 083
SENTINEL-2B DATE 20200808 ORBIT NUMBER 126
SENTINEL-2B DATE 20200811 ORBIT NUMBER 026
SENTINEL-2B DATE 20200812 ORBIT NUMBER 040
SENTINEL-2B DATE 20200815 ORBIT NUMBER 083
SENTINEL-2B DATE 20200822 ORBIT NUMBER 040
SENTINEL-2B DATE 20200825 ORBIT NUMBER 083
SENTINEL-2B DATE 20200917 ORBIT NUMBER 126
SENTINEL-2B DATE 20200921 ORBIT NUMBER 040
SENTINEL-2B DATE 20200924 ORBIT NUMBER 083
SENTINEL-2B DATE 20201001 ORBIT NUMBER 040
SENTINEL-2B DATE 20201024 ORBIT NUMBER 083
SENTINEL-2B DATE 20201031 ORBIT NUMBER 040
SENTINEL-2B DATE 20201103 ORBIT NUMBER 083
SENTINEL-2B DATE 20201106 ORBIT NUMBER 126

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2020 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2016
NOTE: The final extent of the CDL is clipped to the state boundary even though the raw input data may encompass a larger area.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Currentness_Reference: 2020 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -90.5215
East_Bounding_Coordinate: -82.5152
North_Bounding_Coordinate: 48.1444
South_Bounding_Coordinate: 41.6296
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 > Michigan
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Michigan
Place_Keyword: MI
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2020
Access_Constraints: None
Use_Constraints:
The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:
Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None
Native_Data_Set_Environment:
Microsoft Windows 7 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.7 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <https://www.mrlc.gov/>.
ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
If the following table does not display properly, then please visit this internet site <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original metadata file. 
USDA, National Agricultural Statistics Service, 2020 Michigan Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only                *Correct    Accuracy       Error       Kappa
-------------------------                 -------    --------      ------       -----
OVERALL ACCURACY**                        466,784       84.3%       15.7%       0.786

Cover                        Attribute    *Correct  Producer's    Omission                  User's  Commission      Cond'l
Type                              Code      Pixels    Accuracy       Error       Kappa    Accuracy       Error       Kappa
----                              ----      ------    --------       -----       -----    --------       -----       -----
Corn                                 1      180562       88.9%       11.1%       0.857       89.9%       10.1%       0.870
Sorghum                              4         325       33.9%       66.1%       0.338       43.4%       56.6%       0.434
Soybeans                             5      170980       86.5%       13.5%       0.829       92.0%        8.0%       0.897
Sunflower                            6          20       15.3%       84.7%       0.153       66.7%       33.3%       0.667
Sweet Corn                          12           8        8.2%       91.8%       0.082       40.0%       60.0%       0.400
Pop or Orn Corn                     13          45       60.0%       40.0%       0.600       88.2%       11.8%       0.882
Mint                                14         138       55.9%       44.1%       0.559       85.2%       14.8%       0.852
Barley                              21          52       14.0%       86.0%       0.140       27.5%       72.5%       0.275
Spring Wheat                        23          30       26.3%       73.7%       0.263       78.9%       21.1%       0.789
Winter Wheat                        24       32691       86.9%       13.1%       0.863       94.2%        5.8%       0.940
Other Small Grains                  25           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Dbl Crop WinWht/Soybeans            26          41       23.4%       76.6%       0.234       60.3%       39.7%       0.603
Rye                                 27         379       34.2%       65.8%       0.341       35.4%       64.6%       0.353
Oats                                28        1996       61.1%       38.9%       0.609       35.8%       64.2%       0.355
Millet                              29          32       47.8%       52.2%       0.478       50.0%       50.0%       0.500
Speltz                              30          49       32.9%       67.1%       0.329       77.8%       22.2%       0.778
Canola                              31          51       76.1%       23.9%       0.761       96.2%        3.8%       0.962
Flaxseed                            32           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Rape Seed                           34           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Alfalfa                             36       32007       80.0%       20.0%       0.791       83.7%       16.3%       0.829
Other Hay/Non Alfalfa               37        6815       56.8%       43.2%       0.564       74.6%       25.4%       0.743
Buckwheat                           39           1       25.0%       75.0%       0.250        5.9%       94.1%       0.059
Sugarbeets                          41       11453       86.2%       13.8%       0.860       95.5%        4.5%       0.954
Dry Beans                           42       19864       82.0%       18.0%       0.815       94.0%        6.0%       0.938
Potatoes                            43        2439       84.0%       16.0%       0.840       77.2%       22.8%       0.771
Other Crops                         44           5        3.2%       96.8%       0.032        6.3%       93.7%       0.063
Misc Vegs & Fruits                  47           3       25.0%       75.0%       0.250       75.0%       25.0%       0.750
Watermelons                         48          27       26.5%       73.5%       0.265       81.8%       18.2%       0.818
Onions                              49          32       29.1%       70.9%       0.291       66.7%       33.3%       0.667
Cucumbers                           50        1180       83.1%       16.9%       0.831       72.5%       27.5%       0.725
Peas                                53          86       60.6%       39.4%       0.606       64.2%       35.8%       0.642
Tomatoes                            54          59       55.1%       44.9%       0.551       64.8%       35.2%       0.648
Hops                                56           1       20.0%       80.0%       0.200       50.0%       50.0%       0.500
Herbs                               57           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Clover/Wildflowers                  58         300       42.3%       57.7%       0.422       39.4%       60.6%       0.394
Sod/Grass Seed                      59         163       68.5%       31.5%       0.685       69.1%       30.9%       0.691
Switchgrass                         60           0        n/a         n/a         n/a         0.0%      100.0%       0.000
Fallow/Idle Cropland                61        1876       49.2%       50.8%       0.489       30.9%       69.1%       0.306
Cherries                            66         893       84.3%       15.7%       0.843       69.0%       31.0%       0.689
Peaches                             67           3       11.5%       88.5%       0.115       15.8%       84.2%       0.158
Apples                              68         683       82.6%       17.4%       0.826       66.0%       34.0%       0.660
Grapes                              69         419       84.3%       15.7%       0.843       73.9%       26.1%       0.739
Christmas Trees                     70         412       79.1%       20.9%       0.791       53.3%       46.7%       0.533
Other Tree Crops                    71           9       25.7%       74.3%       0.257       37.5%       62.5%       0.375
Walnuts                             76           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Pears                               77           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Aquaculture                         92           3       75.0%       25.0%       0.750       20.0%       80.0%       0.200
Triticale                          205         102       45.9%       54.1%       0.459       46.6%       53.4%       0.466
Carrots                            206          86       62.3%       37.7%       0.623       57.7%       42.3%       0.577
Asparagus                          207           1        5.6%       94.4%       0.056       16.7%       83.3%       0.167
Peppers                            216          27       27.6%       72.4%       0.275       62.8%       37.2%       0.628
Greens                             219           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Plums                              220           1       14.3%       85.7%       0.143      100.0%        0.0%       1.000
Strawberries                       221           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Squash                             222          57       26.1%       73.9%       0.261       41.3%       58.7%       0.413
Vetch                              224           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Dbl Crop WinWht/Corn               225          13       23.2%       76.8%       0.232       59.1%       40.9%       0.591
Dbl Crop Triticale/Corn            228           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Pumpkins                           229          29       19.1%       80.9%       0.191       47.5%       52.5%       0.475
Dbl Crop WinWht/Sorghum            236           0        n/a         n/a         n/a         0.0%      100.0%       0.000
Dbl Crop Corn/Soybeans             241           0        0.0%      100.0%       0.000        n/a         n/a         n/a
Blueberries                        242         204       72.9%       27.1%       0.728       50.5%       49.5%       0.505
Cabbage                            243         131       74.0%       26.0%       0.740       70.8%       29.2%       0.708
Celery                             245           4        9.8%       90.2%       0.098       57.1%       42.9%       0.571
Radishes                           246           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Turnips                            247           0        0.0%      100.0%       0.000        0.0%      100.0%       0.000
Gourds                             249           0        0.0%      100.0%       0.000        n/a         n/a         n/a

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.
The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value:
Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.
Attribute_Accuracy_Explanation:
The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.
Logical_Consistency_Report:
The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.
Completeness_Report: The entire state is covered by the Cropland Data Layer.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Space Research Organization (ISRO)
Publication_Date: 2020
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place:
Indian Space Research Organisation HQ, Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231
Publisher: Indian Space Research Organization (ISRO)
Other_Citation_Details:
The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 24 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: LISS-3
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: European Space Agency (ESA)
Publication_Date: 2020
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: European Commission, Brussels (Belgium)
Publisher: Copernicus - European Commission
Other_Citation_Details:
The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: SENTINEL-2
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator: Elecnor Deimos Imaging
Publication_Date: 2020
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Elecnor Deimos Imaging, Valladolid, Spain
Publisher: Astrium GEO Information Services
Other_Citation_Details:
The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <https://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Deimos-1
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS), Earth Resources Observation and Science (EROS)
Publication_Date: 2020
Title:
Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198-001
Publisher: USGS, EROS
Other_Citation_Details:
The Landsat 8 OLI/TIRS data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20191001
Ending_Date: 20201231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat 8
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2009
Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details:
The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NED
Source_Contribution:
spatial and attribute information used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2019
Title: National Land Cover Database 2016 (NLCD 2016)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publisher: USGS, EROS Data Center
Other_Citation_Details:
The NLCD 2016 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2016 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The NLCD2016 Tree Canopy data layer was not published in time for use in CDL production, so the NLCD 2011 Tree Canopy data was used. More information on the NLCD 2016 and NLCD 2011 can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: unknown
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA), Farm Service Agency (FSA)
Publication_Date: 2020
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office
Other_Citation_Details:
Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.
Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference: ground condition, updated annually
Source_Citation_Abbreviation: FSA CLU
Source_Contribution:
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
Source_Information:
Source_Citation:
Citation_Information:
Originator:
United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center
Publication_Date: 2012
Title: National Commodity Crop Productivity Index (NCCPI) Version 2.0
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Lincoln, Nebraska USA
Publisher:
United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center
Other_Citation_Details:
The NCCPI was used as an ancillary input for the Michigan CDL. The data was resampled to 30 meters for use in CDL production. For more information about the NCCPI: <https://www.nrcs.usda.gov/>.
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: NCCPI
Source_Contribution: Ancillary input used in land cover spectral signature analysis
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: SM.NASS.RDD.GIB@usda.gov
Cloud_Cover: 0
Spatial_Data_Organization_Information:
Indirect_Spatial_Reference: Michigan
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 24000
Column_Count: 21186
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: SM.NASS.RDD.GIB@usda.gov
Contact_Instructions:
Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Resource_Description: Cropland Data Layer - Michigan 2020
Distribution_Liability:
Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GEOTIFF
Format_Version_Date: 2020
Format_Information_Content: GEOTIFF
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: <https://nassgeodata.gmu.edu/CropScape/>
Access_Instructions:
The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. It is also available free for download from the Geospatial Data Gateway website <https://datagateway.nrcs.usda.gov/>.
Fees:
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 CDL data please visit <https://nassgeodata.gmu.edu/CropScape/> or <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Technical_Prerequisites:
If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.
Metadata_Reference_Information:
Metadata_Date: 20210201
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section Staff
Contact_Address:
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 Fri Jan 22 00:18:07 2021