2009 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: 20171211
Title: 2009 California Cropland Data Layer | NASS/USDA
Edition: 2009 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:

***NOTE ON THE NEW 2008 AND 2009 CROPLAND DATA LAYERS (RELEASED 12/11/2017): The 2008 and 2009 Cropland Data Layers (CDL) for the entire Continental United States have been reprocessed and re-released at a 30 meter spatial resolution to best match the products from 2010 forward. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications. More detailed information will be posted on our Frequently Asked Questions at: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2009 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) 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 NLCD 2006 imperviousness layer and NLCD 2001 canopy data layer from the USGS National Land Cover Database.

Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was 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, 2009 California Cropland Data Layer

CLASSIFICATION INPUTS:
AWIFS DATE 20090405 PATH 249 ROW(S)&QUADRANT(S) 45BD
AWIFS DATE 20090508 PATH 246 ROW(S)&QUADRANT(S) 40B 45B
AWIFS DATE 20090522 PATH 244 ROW(S)&QUADRANT(S) 35BD 40BD 45B
AWIFS DATE 20090527 PATH 245 ROW(S)&QUADRANT(S) 40D 45B
AWIFS DATE 20090620 PATH 245 ROW(S)&QUADRANT(S) 40D 45B
AWIFS DATE 20090625 PATH 246 ROW(S)&QUADRANT(S) 35BD 40B 45B
AWIFS DATE 20090714 PATH 245 ROW(S)&QUADRANT(S) 40D 44B
AWIFS DATE 20090715 PATH 250 ROW(S)&QUADRANT(S) 35BD 40BD 45D
AWIFS DATE 20090720 PATH 251 ROW(S)&QUADRANT(S) 44ACD 48B
AWIFS DATE 20090812 PATH 246 ROW(S)&QUADRANT(S) 40B 45B
AWIFS DATE 20090817 PATH 247 ROW(S)&QUADRANT(S) 40D 45B
AWIFS DATE 20090826 PATH 244 ROW(S)&QUADRANT(S) 35BD 40BD 45B
AWIFS DATE 20090827 PATH 249 ROW(S)&QUADRANT(S) 45BD

LANDSAT 5 TM DATE 20090418 PATH 039 ROW(S) 25-38
LANDSAT 5 TM DATE 20090421 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090425 PATH 040 ROW(S) 28-32 34-38
LANDSAT 5 TM DATE 20090504 PATH 039 ROW(S) 27-38
LANDSAT 5 TM DATE 20090507 PATH 044 ROW(S) 27-35
LANDSAT 5 TM DATE 20090523 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090608 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090621 PATH 039 ROW(S) 27-29 31-38
LANDSAT 5 TM DATE 20090624 PATH 044 ROW(S) 27-35
LANDSAT 5 TM DATE 20090701 PATH 045 ROW(S) 25-34
LANDSAT 5 TM DATE 20090705 PATH 041 ROW(S) 25-37
LANDSAT 5 TM DATE 20090707 PATH 039 ROW(S) 25-38
LANDSAT 5 TM DATE 20090710 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090717 PATH 045 ROW(S) 25-33
LANDSAT 5 TM DATE 20090721 PATH 041 ROW(S) 25-37
LANDSAT 5 TM DATE 20090726 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090730 PATH 040 ROW(S) 25-38
LANDSAT 5 TM DATE 20090808 PATH 039 ROW(S) 25-26 28-29 33-38
LANDSAT 5 TM DATE 20090811 PATH 044 ROW(S) 28-35
LANDSAT 5 TM DATE 20090815 PATH 040 ROW(S) 27-38
LANDSAT 5 TM DATE 20090818 PATH 045 ROW(S) 25-34
LANDSAT 5 TM DATE 20090824 PATH 039 ROW(S) 25-38
LANDSAT 5 TM DATE 20090827 PATH 044 ROW(S) 25-35
LANDSAT 5 TM DATE 20090829 PATH 042 ROW(S) 25-26 28-36
LANDSAT 5 TM DATE 20090903 PATH 045 ROW(S) 26-34
LANDSAT 5 TM DATE 20090907 PATH 041 ROW(S) 25-37
LANDSAT 5 TM DATE 20090909 PATH 039 ROW(S) 25-38
LANDSAT 5 TM DATE 20090916 PATH 040 ROW(S) 25-38
LANDSAT 5 TM DATE 20090918 PATH 038 ROW(S) 25-38
LANDSAT 5 TM DATE 20090925 PATH 039 ROW(S) 25-38

USGS, NATIONAL ELEVATION DATASET ELEVATION
USGS, NATIONAL LAND COVER DATABASE 2001 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2006 IMPERVIOUSNESS
VINEYARD MASK BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)
ALMOND MASK BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2009 COMMON LAND UNITS
USGS, NATIONAL LAND COVER DATABASE 2011 (NLCD2006-NLCD2011 PIXELS OF CHANGE REMOVED)
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2009 CROP CLASSIFICATIONS
VINEYARD LOCATIONS AS IDENTIFIED BY E.&J. GALLO WINERY (2012 DATA)
ALMOND ORCHARDS AS IDENTIFIED BY LAND IQ (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: 20081001
Ending_Date: 20091231

Currentness_Reference: 2009 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: AWiFS
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: 2009

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 2.11 <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, 2009 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy     Error     Kappa
-------------------------   -------  --------    ------     -----
OVERALL ACCURACY**          3997077     82.6%     17.4%     0.800


Cover                       Attribute  *Correct Producer's  Omission             User's Commission   Cond'l
Type                             Code    Pixels  Accuracy     Error     Kappa  Accuracy     Error     Kappa
----                             ----    ------  --------     -----     -----  --------     -----     -----
Corn                                1     23323     68.4%     31.6%     0.682     58.4%     41.6%     0.581
Cotton                              2     35259     85.1%     14.9%     0.850     77.5%     22.5%     0.774
Rice                                3    508069     99.4%      0.6%     0.993     99.0%      1.0%     0.989
Sorghum                             4        38      5.4%     94.6%     0.054      7.0%     93.0%     0.070
Sunflower                           6      8438     81.1%     18.9%     0.810     87.5%     12.5%     0.875
Sweet Corn                         12      1234     37.2%     62.8%     0.372     65.7%     34.3%     0.657
Pop or Orn Corn                    13         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Mint                               14      2839     83.1%     16.9%     0.831     98.9%      1.1%     0.989
Barley                             21     34596     56.0%     44.0%     0.556     72.3%     27.7%     0.720
Durum Wheat                        22    106143     82.1%     17.9%     0.816     84.7%     15.3%     0.843
Spring Wheat                       23     10364     92.5%      7.5%     0.925     70.8%     29.2%     0.708
Winter Wheat                       24    213149     67.0%     33.0%     0.650     67.8%     32.2%     0.658
Rye                                27      2475     33.1%     66.9%     0.330     64.2%     35.8%     0.642
Oats                               28     41899     39.9%     60.1%     0.390     52.7%     47.3%     0.518
Millet                             29         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Safflower                          33     23064     85.2%     14.8%     0.851     91.7%      8.3%     0.916
Alfalfa                            36    712033     90.6%      9.4%     0.889     89.2%     10.8%     0.873
Other Hay/Non Alfalfa              37     83088     60.4%     39.6%     0.596     78.0%     22.0%     0.774
Sugarbeets                         41       858     62.0%     38.0%     0.620     89.7%     10.3%     0.897
Dry Beans                          42      2107     33.7%     66.3%     0.337     61.0%     39.0%     0.610
Potatoes                           43       235     38.2%     61.8%     0.382     26.3%     73.7%     0.263
Other Crops                        44      3622     54.7%     45.3%     0.546     81.8%     18.2%     0.818
Sugarcane                          45         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Sweet Potatoes                     46      1858     66.3%     33.7%     0.662     82.0%     18.0%     0.820
Misc Vegs & Fruits                 47        66     14.3%     85.7%     0.143     22.5%     77.5%     0.225
Watermelons                        48      1264     24.4%     75.6%     0.243     36.9%     63.1%     0.369
Onions                             49     22232     81.4%     18.6%     0.813     86.3%     13.7%     0.862
Cucumbers                          50      1038     27.1%     72.9%     0.271     54.7%     45.3%     0.547
Peas                               53       126      8.6%     91.4%     0.086     51.6%     48.4%     0.516
Tomatoes                           54     43812     77.0%     23.0%     0.767     78.9%     21.1%     0.787
Caneberries                        55         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Herbs                              57       795     45.1%     54.9%     0.451     70.2%     29.8%     0.702
Clover/Wildflowers                 58     17665     80.9%     19.1%     0.809     84.8%     15.2%     0.848
Sod/Grass Seed                     59      2106     44.2%     55.8%     0.442     79.8%     20.2%     0.798
Fallow/Idle Cropland               61    375967     75.7%     24.3%     0.733     78.5%     21.5%     0.763
Cherries                           66      1491     25.2%     74.8%     0.252     61.3%     38.7%     0.613
Peaches                            67       708     17.7%     82.3%     0.176     51.9%     48.1%     0.518
Apples                             68      1101     43.1%     56.9%     0.431     76.8%     23.2%     0.768
Grapes                             69    124504     83.3%     16.7%     0.829     89.5%     10.5%     0.892
Other Tree Crops                   71      4378     47.9%     52.1%     0.478     68.7%     31.3%     0.687
Citrus                             72      9035     72.8%     27.2%     0.727     83.3%     16.7%     0.833
Pecans                             74         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Almonds                            75   1202510     94.6%      5.4%     0.929     93.6%      6.4%     0.916
Walnuts                            76     44589     69.0%     31.0%     0.686     74.8%     25.2%     0.744
Pears                              77      1455     77.7%     22.3%     0.777     88.7%     11.3%     0.887
Pistachios                        204     77674     79.3%     20.7%     0.789     82.5%     17.5%     0.822
Triticale                         205      8000     39.9%     60.1%     0.398     69.8%     30.2%     0.697
Carrots                           206      8157     59.2%     40.8%     0.591     70.0%     30.0%     0.699
Asparagus                         207         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Garlic                            208      3766     47.5%     52.5%     0.475     73.5%     26.5%     0.734
Cantaloupes                       209      6285     64.6%     35.4%     0.645     66.7%     33.3%     0.666
Prunes                            210         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Olives                            211     12318     67.4%     32.6%     0.673     77.2%     22.8%     0.771
Oranges                           212     10423     73.0%     27.0%     0.730     68.3%     31.7%     0.682
Honeydew Melons                   213      1090     31.8%     68.2%     0.318     39.9%     60.1%     0.398
Broccoli                          214       536     19.7%     80.3%     0.197     56.1%     43.9%     0.561
Peppers                           216        70      7.4%     92.6%     0.073     20.3%     79.7%     0.203
Pomegranates                      217     12543     64.1%     35.9%     0.640     75.3%     24.7%     0.752
Nectarines                        218       176     14.0%     86.0%     0.139     27.0%     73.0%     0.269
Greens                            219      1234     27.4%     72.6%     0.274     46.7%     53.3%     0.467
Plums                             220      8435     48.0%     52.0%     0.478     71.1%     28.9%     0.710
Strawberries                      221       758     45.0%     55.0%     0.450     46.5%     53.5%     0.465
Squash                            222       230     35.9%     64.1%     0.359     45.8%     54.2%     0.458
Apricots                          223         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Vetch                             224      1276     46.2%     53.8%     0.462     68.5%     31.5%     0.684
Dbl Crop WinWht/Corn              225    117872     80.0%     20.0%     0.794     72.5%     27.5%     0.717
Dbl Crop Oats/Corn                226     45055     63.9%     36.1%     0.634     60.4%     39.6%     0.598
Lettuce                           227      2908     35.3%     64.7%     0.352     65.7%     34.3%     0.656
Pumpkins                          229         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Dbl Crop Lettuce/Cantaloupe       231        89     16.0%     84.0%     0.160     70.6%     29.4%     0.706
Dbl Crop Lettuce/Cotton           232        11    100.0%      0.0%     1.000      9.2%     90.8%     0.092
Dbl Crop Lettuce/Barley           233         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Dbl Crop Durum Wht/Sorghum        234         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Dbl Crop WinWht/Sorghum           236      8294     46.2%     53.8%     0.460     57.6%     42.4%     0.575
Dbl Crop Barley/Corn              237       259     36.2%     63.8%     0.362     73.6%     26.4%     0.736
Dbl Crop WinWht/Cotton            238        13      1.3%     98.7%     0.013     22.8%     77.2%     0.228
Blueberries                       242        15      6.9%     93.1%     0.069     18.5%     81.5%     0.185
Cabbage                           243         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Cauliflower                       244        57     13.6%     86.4%     0.136     16.2%     83.8%     0.162
Radishes                          246         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Turnips                           247         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Eggplants                         248         0      n/a       n/a       n/a       0.0%    100.0%     0.000

*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). 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). 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 imagery is 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 AWiFS imagery used in the production of the Cropland Data Layer is purchased with an orthorectified level of processing. Thus, the CDL will retain the input imagery's positional accuracy of 60 meters at the circular error at the 90 percent confidence level (CE90). CE90 is a standard metric often used for horizontal accuracy in map products and can be interpreted as 90% of well-defined points tested must fall within a certain radial distance.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator:

Indian Remote-Sensing Satellite series of ISRO (Indian Space Research Organization)

Title: RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: EOTec (Earth Observation Technologies, LLC)
Publication_Place: Washington, D.C. 20008

Publication_Date: 2009
Other_Citation_Details:

The RESOURCESAT-1 (IRS-P6) AWiFS satellite sensor operates in four spectral bands at a spatial resolution of 56 meters. Additional information about AWiFS data can be obtained at <https://data.gov.in/>. The AWiFS imagery used in the Cropland Data Layer is obtained through a partnership with the USDA, Foreign Agricultural Service, International Production Assessment (IPA) Program. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

The AWiFS imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used bilinear interpolation, polynomial approximation, polynomial order of 3.

Source_Scale_Denominator: 56 meter
Type_of_Source_Media: CD-ROM and/or DVD
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20081001
Ending_Date: 20091231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: AWiFS
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 5 Thematic Mapper (TM)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2009
Other_Citation_Details:

The Landsat data is 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: 20081001
Ending_Date: 20091231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat
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:

A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was used as non-agricultural training and validation data. Additionally, the USGS NLCD 2006 Imperviousness layer and NLCD 2001 Tree Canopy layer were used as ancillary data sources in the Cropland Data Layer classification process. 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. 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: 2009
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: 2009

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: LandIQ

Title: Almond Data

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: LandIQ

Publication_Place: Sacramento, California 95811 USA

Publication_Date: unknown
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

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: 2009
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: 2009

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:

***NOTE ON THE NEW 2008 AND 2009 CROPLAND DATA LAYERS (RELEASED 12/11/2017)*** The 2008 and 2009 Cropland Data Layers (CDL) for the entire Continental United States have been reprocessed and re-released at a 30 meter spatial resolution to best match the products from 2010 forward. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications. More detailed information will be posted on our Frequently Asked Questions at: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>

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/>. A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was used as non-agricultural training and validation data.

INPUTS: The CDL is produced using satellite imagery from the Landsat 5 TM sensor and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. The AWiFS 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 NLCD 2006 imperviousness layer and NLCD 2001 canopy data layer from the USGS National Land Cover Database. 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/>. 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: 2017
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, 2009 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 2009
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: 2009
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: 20171211
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

2009 California Cropland Data Layer | NASS/USDA

Metadata: