2020 California Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)

Publication_Date: 20220201
Title: 2020 California Cropland Data Layer | NASS/USDA
Edition: 2020 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

USDA, NASS Marketing and Information Services Office, Washington, D.C.

Publisher: USDA, NASS

Other_Citation_Details:

NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape at <https://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>.

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

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2020 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season.

Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2016 (NLCD 2016).

Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data.

Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL.

The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

Purpose:

The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide planted acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.

Supplemental_Information:

If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.

USDA, National Agricultural Statistics Service, 2020 California Cropland Data Layer

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

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

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

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

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

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2020 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2016
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2020 CROP CLASSIFICATIONS
LAND IQ, TREE CROPS AND VINEYARDS (2014 DATA)

NOTE: The final extent of the CDL is clipped to the state boundary even though the raw input data may encompass a larger area.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20191001
Ending_Date: 20201231

Currentness_Reference: 2020 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -124.5876
East_Bounding_Coordinate: -114.1885
North_Bounding_Coordinate: 41.9743
South_Bounding_Coordinate: 32.5028

Keywords:

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

Theme:

Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: DEIMOS-1
Theme_Keyword: ISRO ResourceSat-2 LISS-3
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CropScape

Place:

Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword:

Continent > North America > United States of America > California

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: CA

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2020

Access_Constraints: None
Use_Constraints:

The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/>.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: 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:

USDA, National Agricultural Statistics Service, 2020 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only               *Correct   Accuracy      Error      Kappa
-------------------------                -------   --------     ------      -----
FSA Crops                                402,799      78.8%      21.2%      0.770

Cover                       Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                             Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                             ----     ------   --------      -----      -----   --------      -----      -----
Corn                                1     10,945      74.8%      25.2%      0.744      80.5%      19.5%      0.802
Cotton                              2     16,471      87.9%      12.1%      0.876      86.6%      13.4%      0.863
Rice                                3     73,868      97.7%       2.3%      0.975      99.1%       0.9%      0.990
Sorghum                             4        153      34.7%      65.3%      0.347      39.9%      60.1%      0.399
Sunflower                           6      4,015      78.2%      21.8%      0.781      83.3%      16.7%      0.832
Sweet Corn                         12        101      30.9%      69.1%      0.309      25.4%      74.6%      0.254
Mint                               14        173      80.5%      19.5%      0.805      92.0%       8.0%      0.920
Barley                             21      3,488      58.6%      41.4%      0.584      69.4%      30.6%      0.692
Durum Wheat                        22      1,477      37.0%      63.0%      0.368      45.1%      54.9%      0.449
Spring Wheat                       23        430      31.6%      68.4%      0.316      50.4%      49.6%      0.503
Winter Wheat                       24     19,590      71.7%      28.3%      0.708      67.4%      32.6%      0.664
Rye                                27        173      23.1%      76.9%      0.231      47.3%      52.7%      0.472
Oats                               28      2,344      39.8%      60.2%      0.395      56.9%      43.1%      0.566
Speltz                             30          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Safflower                          33      1,309      59.7%      40.3%      0.597      71.9%      28.1%      0.718
Mustard                            35          7      21.9%      78.1%      0.219     100.0%       0.0%      1.000
Alfalfa                            36     62,942      84.8%      15.2%      0.835      81.0%      19.0%      0.794
Other Hay/Non Alfalfa              37     11,748      64.7%      35.3%      0.641      71.9%      28.1%      0.714
Sugarbeets                         41      2,508      74.6%      25.4%      0.746      72.9%      27.1%      0.728
Dry Beans                          42        307      60.2%      39.8%      0.602      56.0%      44.0%      0.560
Potatoes                           43        306      54.5%      45.5%      0.545      52.7%      47.3%      0.526
Other Crops                        44        182      48.5%      51.5%      0.485      23.7%      76.3%      0.237
Sugarcane                          45         37      74.0%      26.0%      0.740     100.0%       0.0%      1.000
Sweet Potatoes                     46         37      52.9%      47.1%      0.529      84.1%      15.9%      0.841
Misc Vegs & Fruits                 47          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Watermelons                        48         12       2.1%      97.9%      0.020       7.7%      92.3%      0.077
Onions                             49      1,561      49.0%      51.0%      0.488      62.0%      38.0%      0.619
Cucumbers                          50        161      79.7%      20.3%      0.797      64.7%      35.3%      0.647
Chick Peas                         51        947      76.5%      23.5%      0.765      75.8%      24.2%      0.757
Peas                               53        133      20.7%      79.3%      0.206      58.1%      41.9%      0.581
Tomatoes                           54     17,765      88.2%      11.8%      0.879      82.7%      17.3%      0.824
Herbs                              57        115      29.0%      71.0%      0.290      75.7%      24.3%      0.756
Clover/Wildflowers                 58      2,827      86.0%      14.0%      0.860      90.4%       9.6%      0.904
Sod/Grass Seed                     59        337      36.4%      63.6%      0.363      52.2%      47.8%      0.522
Fallow/Idle Cropland               61     20,079      71.1%      28.9%      0.703      78.2%      21.8%      0.775
Cherries                           66        977      80.9%      19.1%      0.809      82.2%      17.8%      0.822
Peaches                            67         53      17.6%      82.4%      0.176      34.9%      65.1%      0.348
Apples                             68          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Grapes                             69      8,906      77.7%      22.3%      0.774      71.2%      28.8%      0.709
Christmas Trees                    70          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Other Tree Crops                   71        116      50.9%      49.1%      0.508      16.0%      84.0%      0.159
Citrus                             72        857      28.6%      71.4%      0.284      27.3%      72.7%      0.271
Pecans                             74         76      26.3%      73.7%      0.263      77.6%      22.4%      0.775
Almonds                            75     62,836      89.2%      10.8%      0.884      87.9%      12.1%      0.870
Walnuts                            76     20,732      88.0%      12.0%      0.877      90.6%       9.4%      0.904
Pears                              77        233      95.9%       4.1%      0.959      95.1%       4.9%      0.951
Aquaculture                        92          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Pistachios                        204     22,654      88.6%      11.4%      0.883      89.7%      10.3%      0.894
Triticale                         205      3,280      47.8%      52.2%      0.476      65.6%      34.4%      0.654
Carrots                           206        574      44.4%      55.6%      0.444      56.1%      43.9%      0.560
Asparagus                         207          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Garlic                            208      1,184      47.8%      52.2%      0.477      70.4%      29.6%      0.703
Cantaloupes                       209        146      14.7%      85.3%      0.146      22.9%      77.1%      0.228
Olives                            211      1,784      89.3%      10.7%      0.893      72.5%      27.5%      0.725
Oranges                           212        382      32.5%      67.5%      0.325      85.5%      14.5%      0.854
Honeydew Melons                   213        116      31.3%      68.7%      0.313      56.3%      43.7%      0.563
Broccoli                          214        191      26.2%      73.8%      0.262      34.5%      65.5%      0.344
Avocados                          215         78      81.3%      18.8%      0.812      37.0%      63.0%      0.370
Peppers                           216         20      11.6%      88.4%      0.116      36.4%      63.6%      0.364
Pomegranates                      217      1,631      97.8%       2.2%      0.978      88.2%      11.8%      0.882
Nectarines                        218          7      17.1%      82.9%      0.171      22.6%      77.4%      0.226
Greens                            219        377      28.2%      71.8%      0.281      46.5%      53.5%      0.464
Plums                             220        804      50.5%      49.5%      0.504      60.7%      39.3%      0.606
Strawberries                      221         11      34.4%      65.6%      0.344      84.6%      15.4%      0.846
Squash                            222         23      41.1%      58.9%      0.411      23.5%      76.5%      0.235
Vetch                             224          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Dbl Crop WinWht/Corn              225     11,029      68.2%      31.8%      0.676      64.0%      36.0%      0.634
Dbl Crop Oats/Corn                226      2,189      60.2%      39.8%      0.600      60.6%      39.4%      0.604
Lettuce                           227        445      16.7%      83.3%      0.166      44.8%      55.2%      0.446
Dbl Crop Triticale/Corn           228      3,956      56.2%      43.8%      0.559      69.6%      30.4%      0.694
Pumpkins                          229         36      33.0%      67.0%      0.330      92.3%       7.7%      0.923
Dbl Crop Lettuce/Cantaloup        231          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Dbl Crop Lettuce/Cotton           232          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop WinWht/Sorghum           236        472      31.9%      68.1%      0.319      50.3%      49.7%      0.502
Dbl Crop Barley/Corn              237          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Dbl Crop WinWht/Cotton            238          2       8.0%      92.0%      0.080      66.7%      33.3%      0.667
Blueberries                       242          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Cabbage                           243         67      33.5%      66.5%      0.335      69.1%      30.9%      0.691
Cauliflower                       244          6       2.4%      97.6%      0.024      10.0%      90.0%      0.100
Celery                            245          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Radishes                          246          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Turnips                           247          1      50.0%      50.0%      0.500      50.0%      50.0%      0.500

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.

The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.

Attribute_Accuracy_Explanation:

The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2016). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.

Logical_Consistency_Report:

The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.

Completeness_Report: The entire state is covered by the Cropland Data Layer.
Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Indian Space Research Organization (ISRO)
Publication_Date: 2020
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place:

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

Publisher: Indian Space Research Organization (ISRO)

Other_Citation_Details:

The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 24 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20191001
Ending_Date: 20201231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: LISS-3
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator: European Space Agency (ESA)
Publication_Date: 2020
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2020 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20191001
Ending_Date: 20201231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: SENTINEL-2
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator: Elecnor Deimos Imaging
Publication_Date: 2020
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <https://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20191001
Ending_Date: 20201231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Deimos-1
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS), Earth Resources Observation and Science (EROS)

Publication_Date: 2020
Title:

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

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The Landsat 8 OLI/TIRS data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20191001
Ending_Date: 20201231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat 8
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Publication_Date: 2009
Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NED
Source_Contribution:

spatial and attribute information used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Publication_Date: 2019
Title: National Land Cover Database 2016 (NLCD 2016)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The NLCD 2016 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2016 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The NLCD2016 Tree Canopy data layer was not published in time for use in CDL production, so the NLCD 2011 Tree Canopy data was used. More information on the NLCD 2016 and NLCD 2011 can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Publication_Date: 2019
Title: National Land Cover Database Shrubland Products
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

More information can be found at <https://www.mrlc.gov/>. Please reference the following journal article for more details on this data layer: Homer, C.G., C.L. Aldridge, D.K. Meyer, S. Schell. 2012. Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA; laying a foundation for monitoring. International Journal of Applied Earth Observation and Geoinformation 14:233-244.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: contact custserv@usgs.gov
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Shrubland Data Layer
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA), Farm Service Agency (FSA)

Publication_Date: 2020
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office

Other_Citation_Details:

Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2020

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: FSA CLU
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator: LandIQ
Publication_Date: unknown
Title: Statewide Land Use
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Sacramento, California 95811 USA
Publisher: LandIQ

Other_Citation_Details:

More information about LandIQ can be found online at <https://www.landiq.com/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2014

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: LandIQ
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Publication_Date: 2020
Title:

Lower Colorado River Water Accounting System (LCRAS) GIS data layer

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Boulder City, NV 89006-1470, USA
Publisher:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Other_Citation_Details:

The Lower Colorado River Water Accounting System (LCRAS) GIS data layer contains an annually updated record of crop types that was used to supplement the training and validation of the Cropland Data Layer. The area covered is Southern California and Southwest Arizona. For more details please reference the Bureau of Reclamation website <https://www.usbr.gov/lc/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2020

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: BLM LCRAS GIS Data
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Process_Step:

Process_Description:

OVERVIEW: *** 2020 Cropland Data Layer Re-Released February 1, 2022 *** 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:

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

2020 California Cropland Data Layer | NASS/USDA

Metadata: