2023 California Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)

Publication_Date: 20240131
Title: 2023 California Cropland Data Layer | USDA NASS
Edition: 2023 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

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

Publisher: USDA NASS

Other_Citation_Details:

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

Online_Linkage: <https://croplandcros.scinet.usda.gov/>

Description:

Abstract:

The USDA NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2023 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected during the current growing season.

Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the NLCD 2016.

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

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

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

Purpose:

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

Supplemental_Information:

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

USDA National Agricultural Statistics Service, 2023 California Cropland Data Layer

CLASSIFICATION INPUTS:
LANDSAT 8 DATE 20230428 PATH/ORBIT 044
LANDSAT 8 DATE 20230511 PATH/ORBIT 039
LANDSAT 8 DATE 20230527 PATH/ORBIT 039
LANDSAT 8 DATE 20230617 PATH/ORBIT 042
LANDSAT 8 DATE 20230701 PATH/ORBIT 044
LANDSAT 8 DATE 20230825 PATH/ORBIT 045
LANDSAT 8 DATE 20230905 PATH/ORBIT 042
LANDSAT 8 DATE 20230907 PATH/ORBIT 040

LANDSAT 9 DATE 20230417 PATH/ORBIT 039
LANDSAT 9 DATE 20230427 PATH/ORBIT 045
LANDSAT 9 DATE 20230508 PATH/ORBIT 042
LANDSAT 9 DATE 20230513 PATH/ORBIT 045
LANDSAT 9 DATE 20230522 PATH/ORBIT 044
LANDSAT 9 DATE 20230524 PATH/ORBIT 042
LANDSAT 9 DATE 20230531 PATH/ORBIT 043
LANDSAT 9 DATE 20230620 PATH/ORBIT 039
LANDSAT 9 DATE 20230810 PATH/ORBIT 044
LANDSAT 9 DATE 20230830 PATH/ORBIT 040
LANDSAT 9 DATE 20230906 PATH/ORBIT 041
LANDSAT 9 DATE 20230911 PATH/ORBIT 044

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2019 IMPERVIOUSNESS
USDA, NASS CROPLAND DATA LAYERS 2017-2022

SENTINEL 2A DATE 20221021 PATH/ORBIT 113
SENTINEL 2A DATE 20221025 PATH/ORBIT 027
SENTINEL 2A DATE 20221113 PATH/ORBIT 013
SENTINEL 2A DATE 20230419 PATH/ORBIT 113
SENTINEL 2A DATE 20230420 PATH/ORBIT 127
SENTINEL 2A DATE 20230426 PATH/ORBIT 070
SENTINEL 2A DATE 20230513 PATH/ORBIT 027
SENTINEL 2A DATE 20230516 PATH/ORBIT 070
SENTINEL 2A DATE 20230519 PATH/ORBIT 113
SENTINEL 2A DATE 20230520 PATH/ORBIT 127
SENTINEL 2A DATE 20230618 PATH/ORBIT 113
SENTINEL 2A DATE 20230621 PATH/ORBIT 013
SENTINEL 2A DATE 20230628 PATH/ORBIT 113
SENTINEL 2A DATE 20230705 PATH/ORBIT 070
SENTINEL 2A DATE 20230715 PATH/ORBIT 070
SENTINEL 2A DATE 20230804 PATH/ORBIT 070
SENTINEL 2A DATE 20230810 PATH/ORBIT 013
SENTINEL 2A DATE 20230827 PATH/ORBIT 113
SENTINEL 2A DATE 20230828 PATH/ORBIT 127
SENTINEL 2A DATE 20230830 PATH/ORBIT 013
SENTINEL 2A DATE 20230831 PATH/ORBIT 027
SENTINEL 2A DATE 20230906 PATH/ORBIT 113
SENTINEL 2A DATE 20230913 PATH/ORBIT 070

SENTINEL 2B DATE 20221013 PATH/ORBIT 070
SENTINEL 2B DATE 20221019 PATH/ORBIT 013
SENTINEL 2B DATE 20221030 PATH/ORBIT 027
SENTINEL 2B DATE 20221106 PATH/ORBIT 127
SENTINEL 2B DATE 20221112 PATH/ORBIT 070
SENTINEL 2B DATE 20221115 PATH/ORBIT 113
SENTINEL 2B DATE 20230421 PATH/ORBIT 070
SENTINEL 2B DATE 20230424 PATH/ORBIT 113
SENTINEL 2B DATE 20230603 PATH/ORBIT 113
SENTINEL 2B DATE 20230614 PATH/ORBIT 127
SENTINEL 2B DATE 20230617 PATH/ORBIT 027
SENTINEL 2B DATE 20230704 PATH/ORBIT 127
SENTINEL 2B DATE 20230707 PATH/ORBIT 027
SENTINEL 2B DATE 20230710 PATH/ORBIT 070
SENTINEL 2B DATE 20230713 PATH/ORBIT 113
SENTINEL 2B DATE 20230714 PATH/ORBIT 127
SENTINEL 2B DATE 20230726 PATH/ORBIT 013
SENTINEL 2B DATE 20230803 PATH/ORBIT 127
SENTINEL 2B DATE 20230806 PATH/ORBIT 027
SENTINEL 2B DATE 20230812 PATH/ORBIT 113
SENTINEL 2B DATE 20230829 PATH/ORBIT 070
SENTINEL 2B DATE 20230908 PATH/ORBIT 070
SENTINEL 2B DATE 20230912 PATH/ORBIT 127
SENTINEL 2B DATE 20230921 PATH/ORBIT 113

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2023 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2019
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2023 CROP CLASSIFICATIONS
LAND IQ, TREE CROPS AND VINEYARDS (2021 PROVISIONAL 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: 20221001
Ending_Date: 20231231

Currentness_Reference: 2023 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: NGDA Portfolio Themes
Theme_Keyword: National Geospatial Data Asset
Theme_Keyword: Land Use Land Cover Theme
Theme_Keyword: NGDA
Theme_Keyword: NGDA109

Theme:

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

Theme:

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

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

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

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CroplandCROS

Place:

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

Continent > North America > United States of America > California

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: CA

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2023

Access_Constraints: None
Use_Constraints:

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

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Data_Set_Credit: USDA National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 10 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <https://www.mrlc.gov/>.

ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions (pre-2007) of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

USDA National Agricultural Statistics Service, 2023 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only             *Correct  Accuracy       Error      Kappa
-------------------------              -------   --------     ------      -----
FSA Crops                              406,915      80.7%      19.3%      0.791

Cover                     Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                           Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                           ----     ------   --------      -----      -----   --------      -----      -----
Corn                              1     12,269      74.9%      25.1%      0.745      76.6%      23.4%      0.762
Cotton                            2     13,057      88.9%      11.1%      0.888      85.3%      14.7%      0.851
Rice                              3     77,964      98.2%       1.8%      0.981      99.4%       0.6%      0.994
Sorghum                           4        420      43.2%      56.8%      0.432      73.0%      27.0%      0.730
Sunflower                         6      2,329      79.1%      20.9%      0.791      84.9%      15.1%      0.848
Sweet Corn                       12        255      64.4%      35.6%      0.644      62.3%      37.7%      0.623
Pop or Orn Corn                  13         11      21.2%      78.8%      0.212      44.0%      56.0%      0.440
Mint                             14          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Barley                           21      2,956      57.8%      42.2%      0.576      68.5%      31.5%      0.683
Durum Wheat                      22        923      56.6%      43.4%      0.565      72.8%      27.2%      0.728
Spring Wheat                     23        611      46.0%      54.0%      0.459      52.4%      47.6%      0.523
Winter Wheat                     24     18,503      68.8%      31.2%      0.679      69.0%      31.0%      0.682
Rye                              27        993      47.2%      52.8%      0.471      64.9%      35.1%      0.648
Oats                             28      3,397      55.6%      44.4%      0.553      66.6%      33.4%      0.664
Canola                           31          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Flaxseed                         32          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Safflower                        33      1,298      55.3%      44.7%      0.552      74.8%      25.2%      0.748
Alfalfa                          36     53,468      89.5%      10.5%      0.888      84.7%      15.3%      0.838
Other Hay/Non Alfalfa            37     14,897      68.3%      31.7%      0.676      76.4%      23.6%      0.759
Camelina                         38          2      16.7%      83.3%      0.167     100.0%       0.0%      1.000
Sugarbeets                       41      2,138      82.7%      17.3%      0.826      75.7%      24.3%      0.756
Dry Beans                        42        306      35.7%      64.3%      0.356      84.8%      15.2%      0.848
Potatoes                         43        551      64.8%      35.2%      0.648      67.9%      32.1%      0.678
Other Crops                      44        522      43.9%      56.1%      0.438      72.1%      27.9%      0.721
Sweet Potatoes                   46         61      92.4%       7.6%      0.924      67.8%      32.2%      0.678
Misc Vegs & Fruits               47          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Watermelons                      48        113      26.4%      73.6%      0.264      33.8%      66.2%      0.338
Onions                           49      2,414      63.9%      36.1%      0.638      77.4%      22.6%      0.774
Cucumbers                        50        100      38.3%      61.7%      0.383      48.1%      51.9%      0.481
Chick Peas                       51        194      45.4%      54.6%      0.454      57.7%      42.3%      0.577
Peas                             53         16      13.3%      86.7%      0.133      15.8%      84.2%      0.158
Tomatoes                         54     22,012      88.6%      11.4%      0.883      84.9%      15.1%      0.845
Herbs                            57        143      36.3%      63.7%      0.363      35.1%      64.9%      0.351
Clover/Wildflowers               58      1,941      91.9%       8.1%      0.919      93.2%       6.8%      0.932
Sod/Grass Seed                   59        274      36.6%      63.4%      0.366      55.1%      44.9%      0.551
Fallow/Idle Cropland             61     30,191      68.4%      31.6%      0.672      82.9%      17.1%      0.821
Cherries                         66        463      74.0%      26.0%      0.739      68.5%      31.5%      0.685
Peaches                          67        194      54.0%      46.0%      0.540      47.9%      52.1%      0.479
Apples                           68          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Grapes                           69      7,917      91.0%       9.0%      0.909      85.8%      14.2%      0.857
Other Tree Crops                 71      1,185      87.6%      12.4%      0.876      52.2%      47.8%      0.522
Citrus                           72      1,587      86.9%      13.1%      0.869      69.7%      30.3%      0.697
Pecans                           74         27      34.2%      65.8%      0.342      62.8%      37.2%      0.628
Almonds                          75     57,934      92.0%       8.0%      0.915      90.6%       9.4%      0.900
Walnuts                          76     14,954      89.5%      10.5%      0.893      89.5%      10.5%      0.894
Pears                            77         29      96.7%       3.3%      0.967      49.2%      50.8%      0.492
Aquaculture                      92          2      20.0%      80.0%      0.200     100.0%       0.0%      1.000
Open Water                      111      7,868      93.2%       6.8%      0.931      93.3%       6.7%      0.932
Perennial Ice/Snow              112         32      31.4%      68.6%      0.314      55.2%      44.8%      0.552
Developed/Open Space            121     13,504      91.9%       8.1%      0.917      73.8%      26.2%      0.734
Developed/Low Intensity         122      8,473      98.5%       1.5%      0.985      80.6%      19.4%      0.804
Developed/Med Intensity         123     11,177      99.8%       0.2%      0.998      94.7%       5.3%      0.946
Developed/High Intensity        124      3,682      99.9%       0.1%      0.999      98.1%       1.9%      0.980
Barren                          131     24,644      88.5%      11.5%      0.882      88.2%      11.8%      0.879
Deciduous Forest                141        211      10.9%      89.1%      0.108      30.4%      69.6%      0.303
Evergreen Forest                142     99,839      90.2%       9.8%      0.890      86.5%      13.5%      0.848
Mixed Forest                    143      4,589      43.3%      56.7%      0.429      61.9%      38.1%      0.615
Shrubland                       152    230,121      93.0%       7.0%      0.906      90.3%       9.7%      0.871
Grassland/Pasture               176     41,015      78.4%      21.6%      0.773      81.9%      18.1%      0.809
Woody Wetlands                  190        576      26.6%      73.4%      0.265      48.2%      51.8%      0.480
Herbaceous Wetlands             195      2,613      52.8%      47.2%      0.526      61.9%      38.1%      0.617
Pistachios                      204     25,097      91.4%       8.6%      0.911      87.9%      12.1%      0.876
Triticale                       205      4,216      47.3%      52.7%      0.469      61.2%      38.8%      0.608
Carrots                         206        845      52.8%      47.2%      0.527      51.7%      48.3%      0.516
Garlic                          208      1,289      76.5%      23.5%      0.765      63.7%      36.3%      0.637
Cantaloupes                     209        223      26.7%      73.3%      0.267      37.7%      62.3%      0.376
Prunes                          210          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Olives                          211      1,566      78.3%      21.7%      0.782      85.7%      14.3%      0.856
Oranges                         212        439      56.1%      43.9%      0.561      88.0%      12.0%      0.880
Honeydew Melons                 213         47      30.5%      69.5%      0.305      30.1%      69.9%      0.301
Broccoli                        214        138      37.1%      62.9%      0.371      37.7%      62.3%      0.377
Avocados                        215         76      63.3%      36.7%      0.633      45.5%      54.5%      0.455
Peppers                         216         65      38.9%      61.1%      0.389      41.9%      58.1%      0.419
Pomegranates                    217      1,583      95.0%       5.0%      0.950      87.1%      12.9%      0.871
Nectarines                      218         20      19.4%      80.6%      0.194      62.5%      37.5%      0.625
Greens                          219        190      43.9%      56.1%      0.438      34.9%      65.1%      0.348
Plums                           220        113      10.7%      89.3%      0.106      49.3%      50.7%      0.493
Strawberries                    221         21      65.6%      34.4%      0.656      70.0%      30.0%      0.700
Squash                          222          1       1.5%      98.5%      0.015     100.0%       0.0%      1.000
Apricots                        223          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Vetch                           224          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop WinWht/Corn            225     10,219      68.0%      32.0%      0.674      61.7%      38.3%      0.611
Dbl Crop Oats/Corn              226      2,752      64.3%      35.7%      0.642      70.6%      29.4%      0.705
Lettuce                         227        446      29.1%      70.9%      0.290      50.0%      50.0%      0.499
Dbl Crop Triticale/Corn         228      8,583      65.9%      34.1%      0.654      71.7%      28.3%      0.713
Pumpkins                        229         69      71.9%      28.1%      0.719     100.0%       0.0%      1.000
Dbl Crop Lettuce/Cantaloupe     231          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop Lettuce/Cotton         232          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Dbl Crop WinWht/Sorghum         236         16      11.6%      88.4%      0.116      15.7%      84.3%      0.157
Dbl Crop Barley/Corn            237         43      39.8%      60.2%      0.398     100.0%       0.0%      1.000
Dbl Crop WinWht/Cotton          238          1       0.8%      99.2%      0.008      14.3%      85.7%      0.143
Blueberries                     242          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Cabbage                         243        236      73.8%      26.3%      0.737      64.7%      35.3%      0.646
Cauliflower                     244          2       1.6%      98.4%      0.016       5.9%      94.1%      0.059
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          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Eggplants                       248          0       0.0%     100.0%      0.000       n/a        n/a        n/a

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

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

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

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

Attribute_Accuracy_Explanation:

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

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

Logical_Consistency_Report:

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

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

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected).

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: European Space Agency (ESA)
Publication_Date: 2023
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 2023 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: 20221001
Ending_Date: 20231231

Source_Currentness_Reference: ground condition

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

Publication_Date: 2023
Title:

Landsat 8 and 9 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The Landsat 8 and 9 OLI/TIRS data are free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20221001
Ending_Date: 20231231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat 8 and Landsat 9
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

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

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

Other_Citation_Details:

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

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NED
Source_Contribution:

spatial and attribute information used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

Publication_Date: 2021
Title: National Land Cover Database 2019 (NLCD 2019)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The NLCD 2019 land cover was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2019 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The Tree Canopy data was not available with the NLCD 2019, so the NLCD 2016 Tree Canopy data was used as an ancillary input. More information on the NLCD can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

Publication_Date: 2023
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: 2023

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: 2023
Title: Statewide Land Use 2021 (Provisional Data)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Sacramento, California 95811 USA
Publisher: LandIQ

Other_Citation_Details:

More information can be found online at <https://data.cnra.ca.gov/dataset/statewide-crop-mapping> and <https://www.landiq.com/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2021

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: 2023
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: 2023

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: LCRAS GIS Data
Source_Contribution:

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

Process_Step:

Process_Description:

OVERVIEW: FOR MORE TECHNICAL DETAILS AND PROGRAM HISTORY: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php> The United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates, most closely aligned with planted acres, produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.'

SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine.

DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions (pre-2007) of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships.

GROUND TRUTH: As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground truth data from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground truth data provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <https://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data.

INPUTS: The CDL is produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected during the current growing season. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the NLCD 2016. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL.

ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report.

PUBLIC RELEASE: The USDA NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2023
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

2023 California Cropland Data Layer | USDA NASS

Metadata: