2015 California Cropland Data Layer | NASS/USDA

Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)
Publication_Date: 20160212
Title: 2015 California Cropland Data Layer | NASS/USDA
Edition: 2015 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 2015 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 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, 2015 California Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20150418 PATH/ROW 7A1
DEIMOS-1 DATE 20150502 PATH/ROW 85B
DEIMOS-1 DATE 20150512 PATH/ROW 8E2
DEIMOS-1 DATE 20150516 PATH/ROW 913
DEIMOS-1 DATE 20150519 PATH/ROW 93B
DEIMOS-1 DATE 20150602 PATH/ROW 9F6
DEIMOS-1 DATE 20150716 PATH/ROW C75
DEIMOS-1 DATE 20150726 PATH/ROW D06
DEIMOS-1 DATE 20150729 PATH/ROW D2F
DEIMOS-1 DATE 20150805 PATH/ROW D92
DEIMOS-1 DATE 20150812 PATH/ROW DFF
DEIMOS-1 DATE 20150818 PATH/ROW E52
DEIMOS-1 DATE 20150904 PATH/ROW F44
DEIMOS-1 DATE 20150918 PATH/ROW 013
DEIMOS-1 DATE 20150924 PATH/ROW 065
DEIMOS-1 DATE 20150925 PATH/ROW 070

LANDSAT 8 OLI/TIRS DATE 20141021 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20141028 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20141104 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20141106 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20141108 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20141115 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20141117 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20141119 PATH 038 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20141124 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20141126 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150422 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150426 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150428 PATH 038 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150503 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150505 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150508 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150510 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150524 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150625 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150702 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20150708 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150711 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150713 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150715 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150729 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150809 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150812 PATH 044 ROW(S) 26-35
LANDSAT 8 OLI/TIRS DATE 20150814 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150816 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150830 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150906 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20150908 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150917 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20150920 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20150922 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20150926 PATH 039 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20151003 PATH 040 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151006 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20151010 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151022 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20151026 PATH 041 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20151031 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 2010-2014 CDLS (INTERNAL USE DATA LAYER)

UK-DMC-2 DATE 20150417 PATH/ROW 2BE
UK-DMC-2 DATE 20150503 PATH/ROW 37A
UK-DMC-2 DATE 20150524 PATH/ROW 43F
UK-DMC-2 DATE 20150527 PATH/ROW 456
UK-DMC-2 DATE 20150530 PATH/ROW 474
UK-DMC-2 DATE 20150602 PATH/ROW 48D
UK-DMC-2 DATE 20150616 PATH/ROW 520
UK-DMC-2 DATE 20150619 PATH/ROW 544
UK-DMC-2 DATE 20150705 PATH/ROW 5E0
UK-DMC-2 DATE 20150706 PATH/ROW 5EA
UK-DMC-2 DATE 20150715 PATH/ROW 644
UK-DMC-2 DATE 20150804 PATH/ROW 6F1
UK-DMC-2 DATE 20150818 PATH/ROW 779
UK-DMC-2 DATE 20150903 PATH/ROW 7ED

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2015 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2011
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2015 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: 20141001
Ending_Date: 20151231
Currentness_Reference: 2015 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: 2015
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 XP; 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:
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, 2015 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**          735,959     82.3%   17.7%   0.807


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1     18936     83.05%    16.95%   0.829     86.49%     13.51%    0.863
Cotton                           2     49010     97.22%     2.78%   0.971     92.74%      7.26%    0.925
Rice                             3     62790     99.31%     0.69%   0.993     99.31%      0.69%    0.993
Sorghum                          4       884     38.22%    61.78%   0.382     49.55%     50.45%    0.495
Sunflower                        6      2867     84.52%    15.48%   0.845     92.66%      7.34%    0.927
Sweet Corn                      12       444     57.66%    42.34%   0.576     69.70%     30.30%    0.697
Mint                            14        64     84.21%    15.79%   0.842     38.79%     61.21%    0.388
Barley                          21     10838     66.66%    33.34%   0.664     77.07%     22.93%    0.769
Durum Wheat                     22     14901     82.46%    17.54%   0.823     80.59%     19.41%    0.804
Spring Wheat                    23       369     60.10%    39.90%   0.601     73.07%     26.93%    0.731
Winter Wheat                    24     38478     69.60%    30.40%   0.688     73.87%     26.13%    0.731
Rye                             27       383     27.28%    72.72%   0.272     41.09%     58.91%    0.411
Oats                            28      4800     42.15%    57.85%   0.419     63.76%     36.24%    0.635
Millet                          29         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Canola                          31         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Safflower                       33     15958     88.90%    11.10%   0.888     94.74%      5.26%    0.947
Alfalfa                         36    135925     94.56%     5.44%   0.941     90.81%      9.19%    0.901
Other Hay/Non Alfalfa           37     17196     65.87%    34.13%   0.655     75.31%     24.69%    0.750
Sugarbeets                      41      4333     85.06%    14.94%   0.850     87.46%     12.54%    0.874
Dry Beans                       42      1574     55.56%    44.44%   0.555     75.49%     24.51%    0.755
Potatoes                        43      2681     72.79%    27.21%   0.727     72.81%     27.19%    0.728
Other Crops                     44       505     50.30%    49.70%   0.503     83.75%     16.25%    0.837
Sugarcane                       45         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Sweet Potatoes                  46         7      6.48%    93.52%   0.065     77.78%     22.22%    0.778
Misc Vegs & Fruits              47      1696     65.97%    34.03%   0.659     78.12%     21.88%    0.781
Watermelons                     48       263     36.28%    63.72%   0.362     33.46%     66.54%    0.334
Onions                          49      5279     73.72%    26.28%   0.736     80.47%     19.53%    0.804
Cucumbers                       50       116     29.67%    70.33%   0.297     60.73%     39.27%    0.607
Peas                            53       495     50.87%    49.13%   0.509     82.91%     17.09%    0.829
Tomatoes                        54     48367     91.42%     8.58%   0.912     91.16%      8.84%    0.909
Herbs                           57       219     25.26%    74.74%   0.252     70.87%     29.13%    0.709
Clover/Wildflowers              58      2893     93.69%     6.31%   0.937     92.31%      7.69%    0.923
Sod/Grass Seed                  59       199     41.20%    58.80%   0.412     56.53%     43.47%    0.565
Fallow/Idle Cropland            61    100845     84.88%    15.12%   0.837     70.47%     29.53%    0.685
Cherries                        66       207     27.97%    72.03%   0.280     71.63%     28.37%    0.716
Peaches                         67        11      5.34%    94.66%   0.053     15.71%     84.29%    0.157
Apples                          68       240     78.18%    21.82%   0.782     79.47%     20.53%    0.795
Grapes                          69     28586     88.86%    11.14%   0.887     90.33%      9.67%    0.902
Other Tree Crops                71       927     73.16%    26.84%   0.732     93.92%      6.08%    0.939
Citrus                          72      3348     89.04%    10.96%   0.890     89.54%     10.46%    0.895
Pecans                          74        68     33.83%    66.17%   0.338     73.91%     26.09%    0.739
Almonds                         75     79005     90.43%     9.57%   0.900     89.66%     10.34%    0.892
Walnuts                         76     12160     72.91%    27.09%   0.727     78.97%     21.03%    0.788
Pears                           77       272     83.69%    16.31%   0.837     74.32%     25.68%    0.743
Aquaculture                     92        40     45.98%    54.02%   0.460     37.38%     62.62%    0.374
Pistachios                     204     31075     77.78%    22.22%   0.774     89.42%     10.58%    0.892
Triticale                      205      4715     62.59%    37.41%   0.625     69.76%     30.24%    0.696
Carrots                        206      2666     60.70%    39.30%   0.606     80.84%     19.16%    0.808
Asparagus                      207         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Garlic                         208      2825     74.21%    25.79%   0.742     86.42%     13.58%    0.864
Cantaloupes                    209       748     52.82%    47.18%   0.528     62.86%     37.14%    0.628
Olives                         211       951     71.99%    28.01%   0.720     82.12%     17.88%    0.821
Oranges                        212      2384     79.81%    20.19%   0.798     76.09%     23.91%    0.761
Honeydew Melons                213       492     48.86%    51.14%   0.488     76.40%     23.60%    0.764
Broccoli                       214         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Peppers                        216       264     57.77%    42.23%   0.578     71.16%     28.84%    0.712
Pomegranates                   217      1317     57.74%    42.26%   0.577     68.24%     31.76%    0.682
Nectarines                     218         1      1.49%    98.51%   0.015      4.00%     96.00%    0.040
Greens                         219      1278     77.60%    22.40%   0.776     69.46%     30.54%    0.694
Plums                          220       400     40.49%    59.51%   0.405     76.05%     23.95%    0.760
Strawberries                   221        86     39.45%    60.55%   0.394     86.87%     13.13%    0.869
Squash                         222         8      8.99%    91.01%   0.090     23.53%     76.47%    0.235
Vetch                          224         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Dbl Crop WinWht/Corn           225     14693     78.84%    21.16%   0.786     73.84%     26.16%    0.736
Dbl Crop Oats/Corn             226      2190     58.42%    41.58%   0.583     65.10%     34.90%    0.650
Lettuce                        227       190     15.26%    84.74%   0.152     46.57%     53.43%    0.465
Pumpkins                       229        66     85.71%    14.29%   0.857     66.00%     34.00%    0.660
Dbl Crop WinWht/Sorghum        236      1408     34.75%    65.25%   0.347     56.50%     43.50%    0.564
Dbl Crop Barley/Corn           237        49     17.31%    82.69%   0.173     57.65%     42.35%    0.576
Dbl Crop WinWht/Cotton         238         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Cabbage                        243         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Cauliflower                    244         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Celery                         245         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Radishes                       246        14     32.56%    67.44%   0.326     48.28%     51.72%    0.483
Eggplants                      248         0      0.00%   100.00%   0.000       n/a        n/a      n/a

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.
The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 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: 2015
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: 20141001
Ending_Date: 20151231
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: 2015
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: 20141001
Ending_Date: 20151231
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: 2015
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: 20141001
Ending_Date: 20151231
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: 2015
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: 2015
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: unknown
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 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: 2015
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Cloud_Cover: 0
Spatial_Data_Organization_Information:
Indirect_Spatial_Reference: 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)
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. However, the official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system.
Albers_Conical_Equal_Area:
Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 30
Ordinate_Resolution: 30
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257223563
Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
Entity_and_Attribute_Detail_Citation:
If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. 
 ***NOTE: The 1997-2013 CDLs were recoded and re-released in January 2014 to better represent pasture and grass-related categories. A new
 category named Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous
 (code 171), and Pasture/Hay (code 181). This was done to eliminate confusion among these similar land cover types which were not always
 classified definitionally consistent from state to state or year to year and frequently had poor classification accuracies. This follows
 the recoding of the entire CDL archive in January 2012 to better align the historical CDLs with the current product. For a detailed list
 of the category name and code changes, please visit the Frequently Asked Questions (FAQ's) section at <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>.


 Data Dictionary: USDA, National Agricultural Statistics Service, 2015 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 2015
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: 2015
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:
Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Ordering_Instructions:
The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. If you experience problems downloading all years of CDL data through the Geospatial Data Gateway then you can try to use the 'Direct Data Download' link in the lower right-hand corner of their webpage.
Custom_Order_Process:
For a list of other states and years of available 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: 20160212
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section Staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Access_Constraints: No restrictions on the distribution or use of the metadata file
Metadata_Use_Constraints: No restrictions on the distribution or use of the metadata file
Generated by mp version 2.9.49 on Fri Feb 15 14:35:38 2019