2012 Texas 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: 20130131
Title: 2012 Texas Cropland Data Layer | NASS/USDA
Edition: 2012 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/TX>

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2012 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ 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), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites.

Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 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, 2012 Texas Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20110903 PATH/ROW 378
DEIMOS-1 DATE 20110906 PATH/ROW 3A3
DEIMOS-1 DATE 20110906 PATH/ROW 3A4
DEIMOS-1 DATE 20110909 PATH/ROW 3E0
DEIMOS-1 DATE 20110919 PATH/ROW 485
DEIMOS-1 DATE 20110925 PATH/ROW 4F6
DEIMOS-1 DATE 20111001 PATH/ROW 559
DEIMOS-1 DATE 20111002 PATH/ROW 566
DEIMOS-1 DATE 20111014 PATH/ROW 621
DEIMOS-1 DATE 20111017 PATH/ROW 652
DEIMOS-1 DATE 20111020 PATH/ROW 67C
DEIMOS-1 DATE 20111021 PATH/ROW 68F
DEIMOS-1 DATE 20111024 PATH/ROW 6BE
DEIMOS-1 DATE 20120417 PATH/ROW EA6
DEIMOS-1 DATE 20120417 PATH/ROW EA8
DEIMOS-1 DATE 20120423 PATH/ROW EDF
DEIMOS-1 DATE 20120424 PATH/ROW EE6
DEIMOS-1 DATE 20120513 PATH/ROW FAD
DEIMOS-1 DATE 20120516 PATH/ROW FCA
DEIMOS-1 DATE 20120516 PATH/ROW FCB
DEIMOS-1 DATE 20120518 PATH/ROW FDC
DEIMOS-1 DATE 20120522 PATH/ROW 00B
DEIMOS-1 DATE 20120601 PATH/ROW 077
DEIMOS-1 DATE 20120604 PATH/ROW 093
DEIMOS-1 DATE 20120608 PATH/ROW 0C7
DEIMOS-1 DATE 20120610 PATH/ROW 0DA
DEIMOS-1 DATE 20120614 PATH/ROW 10E
DEIMOS-1 DATE 20120617 PATH/ROW 12B
DEIMOS-1 DATE 20120620 PATH/ROW 14C
DEIMOS-1 DATE 20120623 PATH/ROW 170
DEIMOS-1 DATE 20120626 PATH/ROW 195
DEIMOS-1 DATE 20120703 PATH/ROW 1E7
DEIMOS-1 DATE 20120706 PATH/ROW 205
DEIMOS-1 DATE 20120713 PATH/ROW 25D
DEIMOS-1 DATE 20120718 PATH/ROW 28F
DEIMOS-1 DATE 20120719 PATH/ROW 29A
DEIMOS-1 DATE 20120728 PATH/ROW 30E
DEIMOS-1 DATE 20120729 PATH/ROW 31E
DEIMOS-1 DATE 20120801 PATH/ROW 34D
DEIMOS-1 DATE 20120801 PATH/ROW 34F
DEIMOS-1 DATE 20120803 PATH/ROW 361
DEIMOS-1 DATE 20120804 PATH/ROW 36A
DEIMOS-1 DATE 20120807 PATH/ROW 38D
DEIMOS-1 DATE 20120820 PATH/ROW 42D
DEIMOS-1 DATE 20120901 PATH/ROW 4D5
DEIMOS-1 DATE 20120902 PATH/ROW 4E0
DEIMOS-1 DATE 20120905 PATH/ROW 501
DEIMOS-1 DATE 20120909 PATH/ROW 533
DEIMOS-1 DATE 20120911 PATH/ROW 54F
DEIMOS-1 DATE 20120918 PATH/ROW 5A5

LANDSAT 5 TM 20110911 PATH 027 ROW(S) 26-42
LANDSAT 5 TM 20110923 PATH 031 ROW(S) 26-40
LANDSAT 5 TM 20110925 PATH 029 ROW(S) 26-40
LANDSAT 5 TM 20111002 PATH 030 ROW(S) 25-38 40
LANDSAT 5 TM 20111013 PATH 027 ROW(S) 30-37 39-42
LANDSAT 5 TM 20111015 PATH 025 ROW(S) 26 28-40
LANDSAT 5 TM 20111018 PATH 030 ROW(S) 25-40
LANDSAT 5 TM 20111029 PATH 027 ROW(S) 27-42
LANDSAT 5 TM 20111031 PATH 025 ROW(S) 31-40
LANDSAT 5 TM 20111103 PATH 030 ROW(S) 25-40
LANDSAT 5 TM 20111110 PATH 031 ROW(S) 26-40

LANDSAT 7 ETM 20120224 PATH 029 ROW(S) 36-38
LANDSAT 7 ETM 20120423 PATH 026 ROW(S) 37-42
LANDSAT 7 ETM 20120428 PATH 029 ROW(S) 36-39
LANDSAT 7 ETM 20120530 PATH 029 ROW(S) 37-39

MODIS 16 DAY NDVI COMPOSITE DATE 20111101
MODIS 16 DAY NDVI COMPOSITE DATE 20111117
MODIS 16 DAY NDVI COMPOSITE DATE 20111219
MODIS 16 DAY NDVI COMPOSITE DATE 20120305
MODIS 16 DAY NDVI COMPOSITE DATE 20120321

UK-DMC-2 20110917 PATH/ROW 258
UK-DMC-2 20111003 PATH/ROW 2E7
UK-DMC-2 20111019 PATH/ROW 3A5
UK-DMC-2 20120418 PATH/ROW BC6
UK-DMC-2 20120421 PATH/ROW BEC
UK-DMC-2 20120501 PATH/ROW C72
UK-DMC-2 20120517 PATH/ROW D43
UK-DMC-2 20120625 PATH/ROW FAB
UK-DMC-2 20120707 PATH/ROW 085
UK-DMC-2 20120708 PATH/ROW 0A1
UK-DMC-2 20120720 PATH/ROW 193
UK-DMC-2 20120806 PATH/ROW 2F1
UK-DMC-2 20120828 PATH/ROW 4E7
UK-DMC-2 20120903 PATH/ROW 559

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATASET 2006 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATASET 2001 TREE CANOPY

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2012 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2006

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: 20120101
Ending_Date: 20121231

Currentness_Reference: 2012 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -107.0545
East_Bounding_Coordinate: -93.7746
North_Bounding_Coordinate: 36.2961
South_Bounding_Coordinate: 25.7524

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: Global Change Master Directory (GCMD) Instrument Keywords
Theme_Keyword: MODIS > Moderate-Resolution Imaging Spectroradiometer

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: MODIS
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 > Texas

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Texas
Place_Keyword: TX

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2012

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 9.1 and 2011 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.0 <https://www.esri.com/>; Rulequest See5.0 Release 2.09 <http://www.rulequest.com/>; NLCD Mapping Tool <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 this internet site <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original metadata file.

USDA, National Agricultural Statistics Service, 2012 Texas Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**        7,955,463     81.2%   18.8%   0.760


Cover                 ***Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1    736633     88.10%    11.90%    0.87     91.43%      8.57%     0.91
Cotton                           2   2631251     92.23%     7.77%    0.89     91.06%      8.94%     0.88
Rice                             3     90890     88.06%    11.94%    0.88     91.18%      8.82%     0.91
Sorghum                          4   1059692     80.94%    19.06%    0.78     82.28%     17.72%     0.80
Soybeans                         5     32656     54.46%    45.54%    0.54     81.35%     18.65%     0.81
Sunflower                        6     29518     65.55%    34.45%    0.65     86.71%     13.29%     0.87
Peanuts                         10      7963     34.05%    65.95%    0.34     79.61%     20.39%     0.80
Sweet Corn                      12        68     22.22%    77.78%    0.22     80.95%     19.05%     0.81
Mint                            14         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Barley                          21     10768     27.22%    72.78%    0.27     72.39%     27.61%     0.72
Spring Wheat                    23      9288     50.37%    49.63%    0.50     75.54%     24.46%     0.75
Winter Wheat                    24   2823553     90.86%     9.14%    0.87     88.43%     11.57%     0.84
Dbl Crop WinWht/Soybeans        26     10739     70.59%    29.41%    0.71     87.68%     12.32%     0.88
Rye                             27      6395     28.28%    71.72%    0.28     63.52%     36.48%     0.63
Oats                            28     76061     43.74%    56.26%    0.43     69.33%     30.67%     0.69
Millet                          29       157      7.13%    92.87%    0.07     46.59%     53.41%     0.47
Canola                          31      8923     80.66%    19.34%    0.81     96.33%      3.67%     0.96
Safflower                       33        39      9.51%    90.49%    0.10     37.14%     62.86%     0.37
Alfalfa                         36     17146     61.16%    38.84%    0.61     90.87%      9.13%     0.91
Dry Beans                       42         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Potatoes                        43      1959     84.99%    15.01%    0.85     83.11%     16.89%     0.83
Other Crops                     44        95      2.52%    97.48%    0.03     30.74%     69.26%     0.31
Sugarcane                       45      7778     91.71%     8.29%    0.92     88.47%     11.53%     0.88
Sweet Potatoes                  46         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Misc Vegs & Fruits              47         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Watermelons                     48       238      8.22%    91.78%    0.08     60.10%     39.90%     0.60
Onions                          49       598     25.19%    74.81%    0.25     51.20%     48.80%     0.51
Cucumbers                       50         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Peas                            53      1360     30.85%    69.15%    0.31     81.44%     18.56%     0.81
Herbs                           57      2647     34.23%    65.77%    0.34     64.72%     35.28%     0.65
Clover/Wildflowers              58        49      8.51%    91.49%    0.09     90.74%      9.26%     0.91
Switchgrass                     60       458     14.69%    85.31%    0.15     95.02%      4.98%     0.95
Fallow/Idle Cropland            61    322940     38.30%    61.70%    0.36     75.86%     24.14%     0.74
Peaches                         67        21      7.14%    92.86%    0.07     46.67%     53.33%     0.47
Grapes                          69         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Citrus                          72      1266     70.57%    29.43%    0.71     89.03%     10.97%     0.89
Pecans                          74      7308     40.01%    59.99%    0.40     91.14%      8.86%     0.91
Aquaculture                     92      2638     66.30%    33.70%    0.66     90.59%      9.41%     0.91
Triticale                      205      5843     30.49%    69.51%    0.30     81.55%     18.45%     0.82
Carrots                        206        29      8.45%    91.55%    0.08     14.36%     85.64%     0.14
Cantaloupes                    209         3      2.50%    97.50%    0.02    100.00%      0.00%     1.00
Olives                         211         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Oranges                        212         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Honeydew Melons                213         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Broccoli                       214         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Peppers                        216         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Greens                         219         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Squash                         222         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Vetch                          224         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Dbl Crop WinWht/Corn           225      7828     40.18%    59.82%    0.40     70.42%     29.58%     0.70
Dbl Crop Oats/Corn             226      1224     83.66%    16.34%    0.84     79.27%     20.73%     0.79
Pumpkins                       229         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Dbl Crop Barley/Sorghum        235        75     18.07%    81.93%    0.18     47.17%     52.83%     0.47
Dbl Crop WinWht/Sorghum        236     12712     18.32%    81.68%    0.18     53.43%     46.57%     0.53
Dbl Crop Barley/Corn           237         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Dbl Crop WinWht/Cotton         238     28604     29.43%    70.57%    0.29     68.78%     31.22%     0.68
Dbl Crop Soybeans/Oats         240       131     33.16%    66.84%    0.33    100.00%      0.00%     1.00
Dbl Crop Corn/Soybeans         241       555     33.21%    66.79%    0.33     99.11%      0.89%     0.99
Blueberries                    242         1      1.27%    98.73%    0.01    100.00%      0.00%     1.00
Cabbage                        243         1      0.37%    99.63%    0.00      0.89%     99.11%     0.01
Radishes                       246         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Turnips                        247         0      0.00%   100.00%    0.00       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 and 200-255).
FSA-sampled grass and pasture, aquaculture, and all NLCD-sampled categories (codes 62-65 and 81-199) 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 2006). 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/>. ***NOTE: The attribute codes above may not necessarily match the most current coding scheme. Please check the Entity_and_Attribute_Detail_Citation Section of this metadata file to verify the current attibute codes and category names.

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 2006). 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 2006 (NLCD 2006). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD 2006 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 5 TM and Landsat 7 ETM 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: 2012
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.

For the 2012 CDL Program, 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: 20111001
Ending_Date: 20121231

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: 2012
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.

For the 2012 CDL Program, 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: 20111001
Ending_Date: 20121231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: UK-DMC2
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) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2012
Other_Citation_Details:

The Landsat 5 TM and Landsat 7 ETM+ 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: 20111001
Ending_Date: 20121230

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) and National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC)

Title:

Moderate Resolution Imaging Spectroradiometer (MODIS), 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) from the Terra satellite (MOD13Q1v4)

Geospatial_Data_Presentation_Form: vegetation indices based on remote-sensing imagery
Publication_Information:

Publisher: USGS Center for Earth Resources Observation and Sciences (EROS)
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: 2012
Other_Citation_Details:

The Moderate Resolution Imaging Spectroradiometer (MODIS), 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) data products from the Terra satellite (MOD13Q1v4) are downloaded from <https://lpdaac.usgs.gov/>. Often late-season MODIS NDVI data are used from the previous growing season in an effort to improve winter wheat detection. Refer to the 'Supplemental Information' Section of this metadata file for specific dates used as classification inputs.

Source_Scale_Denominator: 250 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20111001
Ending_Date: 20120930

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: MODIS
Source_Contribution: NDVI 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. Slope and Aspect derived from the DEM are also used as additional classification inputs. 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 2001 (NLCD 2006)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2012
Other_Citation_Details:

The NLCD 2006 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2006 Imperviousness and NLCD 2001 Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2006 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., 2011. 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:

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

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: 2012
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

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 reproduced 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. Check the 'Process Description' section of the specific state and year metadata file to verify the methodology used. 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 NLCD 2006 is used as non-agricultural training and validation data.

INPUTS: The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ 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), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. 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 2006 (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: 2012
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: Texas
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 39613

Column_Count: 41615

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: WGS84
Ellipsoid_Name: WGS84
Semi-major_Axis: 6378137.00

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 United States Geological Survey (USGS) National Land Cover Database 2006 (NLCD 2006). 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:

 ***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, 2012 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"       Grass/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

2012 Texas Cropland Data Layer | NASS/USDA

Metadata: