2016 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: 20170130
Title: 2016 California Cropland Data Layer | NASS/USDA
Edition: 2016 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/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

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

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2016 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 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 2011 (NLCD 2011).

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, 2016 California Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20160416 PATH/ROW 7DE
DEIMOS-1 DATE 20160510 PATH/ROW 8C3
DEIMOS-1 DATE 20160516 PATH/ROW 90D
DEIMOS-1 DATE 20160517 PATH/ROW 919
DEIMOS-1 DATE 20160530 PATH/ROW 9A2
DEIMOS-1 DATE 20160602 PATH/ROW 9C2
DEIMOS-1 DATE 20160603 PATH/ROW 9CD
DEIMOS-1 DATE 20160606 PATH/ROW 9ED
DEIMOS-1 DATE 20160623 PATH/ROW AA0
DEIMOS-1 DATE 20160704 PATH/ROW B19
DEIMOS-1 DATE 20160707 PATH/ROW B3D
DEIMOS-1 DATE 20160717 PATH/ROW BAD
DEIMOS-1 DATE 20160721 PATH/ROW BD8
DEIMOS-1 DATE 20160723 PATH/ROW BF0
DEIMOS-1 DATE 20160724 PATH/ROW BFF
DEIMOS-1 DATE 20160806 PATH/ROW C9D
DEIMOS-1 DATE 20160816 PATH/ROW D1F
DEIMOS-1 DATE 20160817 PATH/ROW D2C
DEIMOS-1 DATE 20160902 PATH/ROW DF7
DEIMOS-1 DATE 20160910 PATH/ROW E55

LANDSAT 8 OLI/TIRS DATE 20151026 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151031 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20151113 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20151116 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20151118 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151120 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151123 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20151129 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20160415 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160419 PATH 041 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20160421 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20160424 PATH 044 ROW(S) 27-35
LANDSAT 8 OLI/TIRS DATE 20160510 PATH 044 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160512 PATH 042 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160517 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160523 PATH 039 ROW(S) 27 29-38
LANDSAT 8 OLI/TIRS DATE 20160624 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20160627 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20160629 PATH 042 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160708 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160710 PATH 039 ROW(S) 28-38
LANDSAT 8 OLI/TIRS DATE 20160713 PATH 044 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160715 PATH 042 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160717 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160720 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160729 PATH 044 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160802 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160804 PATH 038 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20160814 PATH 044 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20160821 PATH 045 ROW(S) 26-33
LANDSAT 8 OLI/TIRS DATE 20160825 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160830 PATH 044 ROW(S) 26 30-35
LANDSAT 8 OLI/TIRS DATE 20160901 PATH 042 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160903 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160906 PATH 045 ROW(S) 26-28 30-34
LANDSAT 8 OLI/TIRS DATE 20160912 PATH 039 ROW(S) 26 30-38
LANDSAT 8 OLI/TIRS DATE 20160915 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20160917 PATH 042 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160924 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20160926 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20160928 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20161001 PATH 044 ROW(S) 26-35

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATASET 2011 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATASET 2011 TREE CANOPY
USDA, NASS AG MASK BASED ON 2011-2015 CDLS (INTERNAL USE DATA LAYER)

UK-DMC-2 DATE 20160416 PATH/ROW F11
UK-DMC-2 DATE 20160624 PATH/ROW 14A
UK-DMC-2 DATE 20160625 PATH/ROW 154
UK-DMC-2 DATE 20160701 PATH/ROW 190
UK-DMC-2 DATE 20160708 PATH/ROW 1AE
UK-DMC-2 DATE 20160721 PATH/ROW 1F9
UK-DMC-2 DATE 20160725 PATH/ROW 21F
UK-DMC-2 DATE 20160807 PATH/ROW 290
UK-DMC-2 DATE 20160817 PATH/ROW 2EC
UK-DMC-2 DATE 20160820 PATH/ROW 30C
UK-DMC-2 DATE 20160902 PATH/ROW 393
UK-DMC-2 DATE 20160906 PATH/ROW 3BD

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2016 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2011
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2016 CROP CLASSIFICATIONS
VINEYARD LOCATIONS AS IDENTIFIED BY E.&J. GALLO WINERY (2013 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: 20151001
Ending_Date: 20161231

Currentness_Reference: 2016 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: UK-DMC 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: 2016

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) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <https://www.esri.com/>.

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 Versions 2011 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.10 <http://www.rulequest.com/>; NLCD Mapping Tool v2.08 <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, 2016 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**          891,603     89.5%   10.5%   0.887


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1     21854     85.85%    14.15%   0.857     92.68%      7.32%    0.926
Cotton                           2     77454     98.04%     1.96%   0.980     94.88%      5.12%    0.947
Rice                             3     81801     99.77%     0.23%   0.998     99.89%      0.11%    0.999
Sorghum                          4      2062     65.79%    34.21%   0.658     91.40%      8.60%    0.914
Sunflower                        6      5789     98.67%     1.33%   0.987     98.54%      1.46%    0.985
Sweet Corn                      12       301     43.06%    56.94%   0.430     41.40%     58.60%    0.414
Pop or Orn Corn                 13        97     92.38%     7.62%   0.924     97.00%      3.00%    0.970
Mint                            14        89     97.80%     2.20%   0.978    100.00%      0.00%    1.000
Barley                          21     15129     80.11%    19.89%   0.799     87.93%     12.07%    0.878
Durum Wheat                     22      7415     78.92%    21.08%   0.788     81.61%     18.39%    0.815
Spring Wheat                    23      1334     96.60%     3.40%   0.966     95.70%      4.30%    0.957
Winter Wheat                    24     57803     83.03%    16.97%   0.824     86.43%     13.57%    0.859
Rye                             27       676     53.52%    46.48%   0.535     88.83%     11.17%    0.888
Oats                            28      7274     74.20%    25.80%   0.741     88.89%     11.11%    0.888
Canola                          31       176     81.11%    18.89%   0.811    100.00%      0.00%    1.000
Safflower                       33     18674     95.68%     4.32%   0.956     97.31%      2.69%    0.973
Alfalfa                         36    133361     96.25%     3.75%   0.959     93.34%      6.66%    0.928
Other Hay/Non Alfalfa           37     22403     82.45%    17.55%   0.822     92.34%      7.66%    0.922
Sugarbeets                      41      5713     84.26%    15.74%   0.842     84.88%     15.12%    0.848
Dry Beans                       42      4458     89.82%    10.18%   0.898     89.77%     10.23%    0.897
Potatoes                        43      2165     80.63%    19.37%   0.806     83.75%     16.25%    0.837
Other Crops                     44       880     75.99%    24.01%   0.760     88.62%     11.38%    0.886
Sugarcane                       45         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Sweet Potatoes                  46       103     92.79%     7.21%   0.928     99.04%      0.96%    0.990
Misc Vegs & Fruits              47      1722     73.87%    26.13%   0.738     84.62%     15.38%    0.846
Watermelons                     48       526     57.80%    42.20%   0.578     67.09%     32.91%    0.671
Onions                          49      6987     77.92%    22.08%   0.778     85.94%     14.06%    0.859
Cucumbers                       50       640     94.40%     5.60%   0.944     98.92%      1.08%    0.989
Peas                            53       988     56.62%    43.38%   0.566     84.95%     15.05%    0.849
Tomatoes                        54     54420     94.32%     5.68%   0.941     94.89%      5.11%    0.947
Herbs                           57       623     61.68%    38.32%   0.617     85.93%     14.07%    0.859
Clover/Wildflowers              58      3391     96.01%     3.99%   0.960     97.41%      2.59%    0.974
Sod/Grass Seed                  59       334     57.99%    42.01%   0.580     97.09%      2.91%    0.971
Fallow/Idle Cropland            61    102249     90.93%     9.07%   0.903     79.08%     20.92%    0.778
Cherries                        66       436     43.08%    56.92%   0.431     89.34%     10.66%    0.893
Peaches                         67       155     52.36%    47.64%   0.524     76.73%     23.27%    0.767
Apples                          68        20     36.36%    63.64%   0.364     38.46%     61.54%    0.385
Grapes                          69     32350     92.24%     7.76%   0.921     92.27%      7.73%    0.921
Other Tree Crops                71       496     61.01%    38.99%   0.610     92.19%      7.81%    0.922
Citrus                          72      2930     84.71%    15.29%   0.847     84.46%     15.54%    0.844
Pecans                          74       176     59.86%    40.14%   0.599    100.00%      0.00%    1.000
Almonds                         75    104166     92.94%     7.06%   0.925     92.57%      7.43%    0.921
Walnuts                         76     15464     82.17%    17.83%   0.820     90.82%      9.18%    0.907
Pears                           77       280     83.58%    16.42%   0.836     90.91%      9.09%    0.909
Aquaculture                     92       167     88.36%    11.64%   0.884     81.86%     18.14%    0.819
Pistachios                     204     42206     83.79%    16.21%   0.834     89.90%     10.10%    0.896
Triticale                      205      7889     72.30%    27.70%   0.722     86.20%     13.80%    0.861
Carrots                        206      3216     65.06%    34.94%   0.650     76.39%     23.61%    0.763
Asparagus                      207        61     58.10%    41.90%   0.581     96.83%      3.17%    0.968
Garlic                         208      3736     83.11%    16.89%   0.831     92.96%      7.04%    0.929
Cantaloupes                    209      2184     64.56%    35.44%   0.645     76.90%     23.10%    0.769
Olives                         211      1097     91.34%     8.66%   0.913     93.44%      6.56%    0.934
Oranges                        212      2321     75.65%    24.35%   0.756     83.52%     16.48%    0.835
Honeydew Melons                213      1060     69.65%    30.35%   0.696     87.75%     12.25%    0.877
Broccoli                       214       180     16.70%    83.30%   0.167     22.67%     77.33%    0.226
Peppers                        216       407     68.87%    31.13%   0.689     77.82%     22.18%    0.778
Pomegranates                   217      5551     85.07%    14.93%   0.850     89.47%     10.53%    0.894
Nectarines                     218        51     45.54%    54.46%   0.455     98.08%      1.92%    0.981
Greens                         219      1112     46.06%    53.94%   0.460     47.87%     52.13%    0.478
Plums                          220       871     68.15%    31.85%   0.681     91.30%      8.70%    0.913
Strawberries                   221        90     68.18%    31.82%   0.682     85.71%     14.29%    0.857
Squash                         222       194     77.91%    22.09%   0.779     97.49%      2.51%    0.975
Vetch                          224       259     90.24%     9.76%   0.902    100.00%      0.00%    1.000
Dbl Crop WinWht/Corn           225     16171     87.31%    12.69%   0.872     83.83%     16.17%    0.837
Dbl Crop Oats/Corn             226      3688     82.38%    17.62%   0.823     88.31%     11.69%    0.883
Lettuce                        227      1088     39.24%    60.76%   0.392     50.89%     49.11%    0.508
Pumpkins                       229        97     97.98%     2.02%   0.980     97.98%      2.02%    0.980
Dbl Crop WinWht/Sorghum        236      2235     64.67%    35.33%   0.646     82.56%     17.44%    0.825
Dbl Crop Barley/Corn           237       271     94.43%     5.57%   0.944     88.85%     11.15%    0.889
Dbl Crop WinWht/Cotton         238        47     63.51%    36.49%   0.635     97.92%      2.08%    0.979
Blueberries                    242        20     32.79%    67.21%   0.328     90.91%      9.09%    0.909
Cabbage                        243        99     28.53%    71.47%   0.285     32.25%     67.75%    0.322
Cauliflower                    244        20      5.52%    94.48%   0.055     14.71%     85.29%    0.147
Celery                         245         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Radishes                       246         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Turnips                        247        18     81.82%    18.18%   0.818     94.74%      5.26%    0.947

*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 2011). 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 2011). 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 2011 (NLCD 2011). 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 and DMC-UK 2 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: Elecnor Deimos Imaging
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2016
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: 20151001
Ending_Date: 20161231

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: DMC International Imaging
Title: UK-DMC 2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Astrium GEO Information Services
Publication_Place: DMC International Imaging, Guildford, Surrey UK

Publication_Date: 2016
Other_Citation_Details:

The UK-DMC 2 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about UK-DMC 2 data can be obtained at <http://www.dmcii.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

The UK-DMC 2 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: 20151001
Ending_Date: 20161231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: UK-DMC 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)

Title:

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

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2016
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: 20151001
Ending_Date: 20161231

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

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

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

Publication_Date: 2009
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

Title: National Land Cover Database 2011 (NLCD 2011)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2014
Other_Citation_Details:

The NLCD 2011 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2011 Imperviousness and Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the 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. Preferred NLCD2006 citation: "Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2012. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."

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)

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

Publisher: USDA, FSA Aerial Photography Field Office

Publication_Place: Salt Lake City, Utah 84119-2020 USA

Publication_Date: 2016
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: unknown

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: E.&J. Gallo Winery

Title: Vineyard Data

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: E.&J. Gallo Winery

Publication_Place: Modesto, CA 95354 USA

Publication_Date: 2013
Other_Citation_Details:

The E.&J. Gallo Winery company collects vineyard locations by driving and noting field locations on GPS. More information about E.&J. Gallo Winery can be found online at <http://gallo.com/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2013

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: Gallo
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

Title:

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

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher:

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

Publication_Place: Boulder City, NV 89006-1470, USA

Publication_Date: 2016
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: 2016

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: BLM LCRAS GIS Data
Source_Contribution:

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

Process_Step:

Process_Description:

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

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

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

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

INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 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 2011 (NLCD 2011). 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 2011 (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/>. See the 'Ordering Instructions' section of this metadata file for detailed download instructions. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2016
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

Spatial_Data_Organization_Information:

Indirect_Spatial_Reference: California
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 35841

Column_Count: 29767

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name:

Albers Conical Equal Area as used by mrlc.gov (NLCD). The official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system. FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM), Spheriod 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. The UTM Zones are as follows: Zone 11 - California, Idaho, Nevada, Oregon, Washington; Zone 12 - Arizona, Montana, Utah; Zone 13 - Colorado, New Mexico, Wyoming; Zone 14 - Kansas, North Dakota, Nebraska, Oklahoma, South Dakota, Texas; Zone 15 - Arkansas, Iowa, Louisiana, Minnesota, Missouri; Zone 16 - Alabama, Illinois, Indiana, Kentucky, Michigan, Mississippi, Tennessee; Zone 17 - Florida, Georgia, North Carolina, Ohio, South Carolina, Virginia, West Virginia; Zone 18 - Connecticut, Delaware, Maryland, New Jersey, New York, Pennsylvania, Vermont; Zone 19 - Maine, Massachusetts, New Hampshire, Rhode Island.

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:

 Data Dictionary: USDA, National Agricultural Statistics Service, 2016 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 1-20

 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

 Raster
 Attribute Domain Values and Definitions: GRAINS,HAY,SEEDS 21-40

 Categorization Code   Land Cover
          "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

 Raster
 Attribute Domain Values and Definitions: CROPS 41-60

 Categorization Code   Land Cover
          "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
         "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 - California 2016
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: 2016
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/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

Fees:

Ordering_Instructions:

The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. If you experience problems downloading all years of CDL data through the Geospatial Data Gateway then you can try to use the 'Direct Data Download' link in the lower right-hand corner of their webpage.

Custom_Order_Process:

For a list of other states and years of available data please visit: <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Technical_Prerequisites:

If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/> or using the freeware browser ESRI ArcGIS Explorer <https://www.esri.com/>.

Metadata_Reference_Information:

Metadata_Date: 20170130
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:

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

2016 California Cropland Data Layer | NASS/USDA

Metadata: