2016 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: 20170130
Title: 2016 Texas Cropland Data Layer | NASS/USDA
Edition: 2016 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

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

Publisher: USDA, NASS

Other_Citation_Details:

NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape at <https://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

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

Description:

Abstract:

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

Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011).

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

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

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

Purpose:

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

Supplemental_Information:

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

USDA, National Agricultural Statistics Service, 2016 Texas Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20151002 PATH/ROW 0CC
DEIMOS-1 DATE 20151006 PATH/ROW 102
DEIMOS-1 DATE 20151016 PATH/ROW 186
DEIMOS-1 DATE 20151019 PATH/ROW 1B1
DEIMOS-1 DATE 20151026 PATH/ROW 21D
DEIMOS-1 DATE 20151027 PATH/ROW 22C
DEIMOS-1 DATE 20160423 PATH/ROW 81A
DEIMOS-1 DATE 20160427 PATH/ROW 843
DEIMOS-1 DATE 20160504 PATH/ROW 884
DEIMOS-1 DATE 20160507 PATH/ROW 8A4
DEIMOS-1 DATE 20160511 PATH/ROW 8CF
DEIMOS-1 DATE 20160528 PATH/ROW 98D
DEIMOS-1 DATE 20160617 PATH/ROW A63
DEIMOS-1 DATE 20160630 PATH/ROW AEE
DEIMOS-1 DATE 20160705 PATH/ROW B21
DEIMOS-1 DATE 20160711 PATH/ROW B65
DEIMOS-1 DATE 20160717 PATH/ROW BAA
DEIMOS-1 DATE 20160721 PATH/ROW BD6
DEIMOS-1 DATE 20160725 PATH/ROW C07
DEIMOS-1 DATE 20160728 PATH/ROW C2D
DEIMOS-1 DATE 20160803 PATH/ROW C76
DEIMOS-1 DATE 20160804 PATH/ROW C81
DEIMOS-1 DATE 20160807 PATH/ROW CAB
DEIMOS-1 DATE 20160903 PATH/ROW DFF

LANDSAT 8 OLI/TIRS DATE 20151010 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151013 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151015 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20151105 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151107 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151118 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20151121 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160422 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160503 PATH 027 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20160505 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160508 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160510 PATH 028 ROW(S) 26-28 30-41
LANDSAT 8 OLI/TIRS DATE 20160521 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160528 PATH 026 ROW(S) 26-27 30-37 39-42
LANDSAT 8 OLI/TIRS DATE 20160606 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160616 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160625 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160706 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20160708 PATH 025 ROW(S) 27-40
LANDSAT 8 OLI/TIRS DATE 20160713 PATH 028 ROW(S) 26 28-41
LANDSAT 8 OLI/TIRS DATE 20160715 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20160718 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160720 PATH 029 ROW(S) 26 28-40
LANDSAT 8 OLI/TIRS DATE 20160722 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20160724 PATH 025 ROW(S) 27-40
LANDSAT 8 OLI/TIRS DATE 20160803 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160805 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160807 PATH 027 ROW(S) 26-32 34-42
LANDSAT 8 OLI/TIRS DATE 20160809 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20160819 PATH 031 ROW(S) 26-30 32-40
LANDSAT 8 OLI/TIRS DATE 20160904 PATH 031 ROW(S) 26 28-40
LANDSAT 8 OLI/TIRS DATE 20160908 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20160913 PATH 030 ROW(S) 31-32 36-40

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

UK-DMC-2 DATE 20151014 PATH/ROW 8F5
UK-DMC-2 DATE 20151027 PATH/ROW 970
UK-DMC-2 DATE 20160423 PATH/ROW F4F
UK-DMC-2 DATE 20160430 PATH/ROW F97
UK-DMC-2 DATE 20160506 PATH/ROW FBC
UK-DMC-2 DATE 20160527 PATH/ROW 050
UK-DMC-2 DATE 20160530 PATH/ROW 068
UK-DMC-2 DATE 20160605 PATH/ROW 09D
UK-DMC-2 DATE 20160609 PATH/ROW 0BB
UK-DMC-2 DATE 20160702 PATH/ROW 199
UK-DMC-2 DATE 20160716 PATH/ROW 1D6
UK-DMC-2 DATE 20160719 PATH/ROW 1E7
UK-DMC-2 DATE 20160722 PATH/ROW 203
UK-DMC-2 DATE 20160801 PATH/ROW 25E
UK-DMC-2 DATE 20160804 PATH/ROW 278
UK-DMC-2 DATE 20160805 PATH/ROW 280
UK-DMC-2 DATE 20160811 PATH/ROW 2BC
UK-DMC-2 DATE 20160824 PATH/ROW 335
UK-DMC-2 DATE 20160910 PATH/ROW 3E4

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2016 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2011

NOTE: The final extent of the CDL is clipped to the state boundary
even though the raw input data may encompass a larger area.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20151001
Ending_Date: 20161231

Currentness_Reference: 2016 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -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: 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 > Texas

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Texas
Place_Keyword: TX

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2016

Access_Constraints: None
Use_Constraints:

The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <https://www.esri.com/>.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

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

Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 7 Enterprise; ERDAS Imagine Versions 2011 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.10 <http://www.rulequest.com/>; NLCD Mapping Tool v2.08 <https://www.mrlc.gov/>.

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

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

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, 2016 Texas Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**          821,187     81.7%   18.3%   0.778


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1    138544     91.43%     8.57%   0.907     90.95%      9.05%    0.902
Cotton                           2    209102     94.01%     5.99%   0.932     89.42%     10.58%    0.881
Rice                             3     15942     92.06%     7.94%   0.920     91.74%      8.26%    0.917
Sorghum                          4    107202     83.10%    16.90%   0.819     85.15%     14.85%    0.841
Soybeans                         5      6919     65.96%    34.04%   0.658     78.72%     21.28%    0.786
Sunflower                        6       666     40.59%    59.41%   0.406     83.77%     16.23%    0.838
Peanuts                         10      1742     30.13%    69.87%   0.300     68.80%     31.20%    0.687
Sweet Corn                      12         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Mint                            14         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Barley                          21        53      3.51%    96.49%   0.035     38.69%     61.31%    0.386
Spring Wheat                    23      1319     48.58%    51.42%   0.485     72.00%     28.00%    0.720
Winter Wheat                    24    240652     91.94%     8.06%   0.906     85.57%     14.43%    0.833
Dbl Crop WinWht/Soybeans        26       126     35.69%    64.31%   0.357     77.78%     22.22%    0.778
Rye                             27       774     30.82%    69.18%   0.308     58.64%     41.36%    0.586
Oats                            28     10360     52.05%    47.95%   0.517     64.88%     35.12%    0.645
Millet                          29        96     26.02%    73.98%   0.260     48.73%     51.27%    0.487
Canola                          31      1612     80.04%    19.96%   0.800     98.90%      1.10%    0.989
Alfalfa                         36      3181     78.21%    21.79%   0.782     89.88%     10.12%    0.899
Other Hay/Non Alfalfa           37     22586     49.99%    50.01%   0.492     74.78%     25.22%    0.742
Sugarbeets                      41         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Dry Beans                       42        38     19.10%    80.90%   0.191     70.37%     29.63%    0.704
Potatoes                        43       204     72.86%    27.14%   0.729     90.27%      9.73%    0.903
Other Crops                     44        61     29.05%    70.95%   0.290     55.96%     44.04%    0.560
Sugarcane                       45      2332     93.99%     6.01%   0.940     95.69%      4.31%    0.957
Sweet Potatoes                  46         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Watermelons                     48        85     37.95%    62.05%   0.379     68.00%     32.00%    0.680
Onions                          49        60     32.09%    67.91%   0.321     64.52%     35.48%    0.645
Cucumbers                       50         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Peas                            53        12      3.37%    96.63%   0.034     20.34%     79.66%    0.203
Tomatoes                        54         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Herbs                           57        73     11.08%    88.92%   0.111     47.71%     52.29%    0.477
Clover/Wildflowers              58         3     15.00%    85.00%   0.150     75.00%     25.00%    0.750
Sod/Grass Seed                  59      1706     61.17%    38.83%   0.611     80.78%     19.22%    0.807
Switchgrass                     60         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Fallow/Idle Cropland            61     51785     54.49%    45.51%   0.528     72.47%     27.53%    0.711
Peaches                         67         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Grapes                          69         6     23.08%    76.92%   0.231     75.00%     25.00%    0.750
Other Tree Crops                71         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Citrus                          72       138     75.82%    24.18%   0.758     78.86%     21.14%    0.789
Pecans                          74      1070     67.13%    32.87%   0.671     91.69%      8.31%    0.917
Pears                           77         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Aquaculture                     92       266     69.82%    30.18%   0.698     86.36%     13.64%    0.864
Pistachios                     204         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Triticale                      205       971     18.95%    81.05%   0.189     61.49%     38.51%    0.614
Carrots                        206         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Cantaloupes                    209         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Olives                         211         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Oranges                        212         2      4.65%    95.35%   0.047     13.33%     86.67%    0.133
Broccoli                       214         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Peppers                        216         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Greens                         219        18     33.33%    66.67%   0.333     41.86%     58.14%    0.419
Squash                         222         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Vetch                          224         2      9.09%    90.91%   0.091     40.00%     60.00%    0.400
Dbl Crop WinWht/Corn           225       612     25.76%    74.24%   0.257     60.83%     39.17%    0.608
Dbl Crop Oats/Corn             226         7     43.75%    56.25%   0.437     43.75%     56.25%    0.437
Pumpkins                       229        80     67.23%    32.77%   0.672     70.80%     29.20%    0.708
Dbl Crop WinWht/Sorghum        236       350     10.93%    89.07%   0.109     40.60%     59.40%    0.405
Dbl Crop Barley/Corn           237         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Dbl Crop WinWht/Cotton         238       696     21.47%    78.53%   0.214     52.89%     47.11%    0.528
Dbl Crop Soybeans/Oats         240         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dbl Crop Corn/Soybeans         241         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Blueberries                    242         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Cabbage                        243         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Radishes                       246         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Turnips                        247         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Dbl Crop Barley/Soybeans       254         0       n/a       n/a     n/a       0.00%    100.00%    0.000

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

The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD 2011). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

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

Attribute_Accuracy_Explanation:

The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD 2011). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

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

Logical_Consistency_Report:

The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database 2011 (NLCD 2011). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.

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

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 OLI/TIRS imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 and DMC-UK 2 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Elecnor Deimos Imaging
Title: DEIMOS-1
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2016
Other_Citation_Details:

The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <https://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

The DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20151001
Ending_Date: 20161231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Deimos-1
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

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

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

Publication_Date: 2016
Other_Citation_Details:

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

The UK-DMC 2 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 22 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20151001
Ending_Date: 20161231

Source_Currentness_Reference: ground condition

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

Title:

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

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2016
Other_Citation_Details:

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

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20151001
Ending_Date: 20161231

Source_Currentness_Reference: ground condition

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

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

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

Publication_Date: 2009
Other_Citation_Details:

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

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NED
Source_Contribution:

spatial and attribute information used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

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

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

Publication_Date: 2014
Other_Citation_Details:

The NLCD 2011 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2011 Imperviousness and Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2011 can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs. Preferred NLCD2006 citation: "Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2012. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

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

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

Publisher: USDA, FSA Aerial Photography Field Office

Publication_Place: Salt Lake City, Utah 84119-2020 USA

Publication_Date: 2016
Other_Citation_Details:

Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: FSA CLU
Source_Contribution:

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

Process_Step:

Process_Description:

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

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

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

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

INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. The DEIMOS-1 and UK-DMC 2 imagery was resampled to 30 meters using cubic convolution, rigorous transformation to match the traditional Landsat spatial resolution. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL.

ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD 2011 (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report.

PUBLIC RELEASE: The USDA, NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape <https://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed download instructions. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2016
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

Cloud_Cover: 0

Spatial_Data_Organization_Information:

Indirect_Spatial_Reference: 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). The official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system. FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM), Spheriod WGS84, Datum WGS84. Due to technical restrictions, the online data available free for download through the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered in UTM. The UTM Zones are as follows: Zone 11 - California, Idaho, Nevada, Oregon, Washington; Zone 12 - Arizona, Montana, Utah; Zone 13 - Colorado, New Mexico, Wyoming; Zone 14 - Kansas, North Dakota, Nebraska, Oklahoma, South Dakota, Texas; Zone 15 - Arkansas, Iowa, Louisiana, Minnesota, Missouri; Zone 16 - Alabama, Illinois, Indiana, Kentucky, Michigan, Mississippi, Tennessee; Zone 17 - Florida, Georgia, North Carolina, Ohio, South Carolina, Virginia, West Virginia; Zone 18 - Connecticut, Delaware, Maryland, New Jersey, New York, Pennsylvania, Vermont; Zone 19 - Maine, Massachusetts, New Hampshire, Rhode Island.

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:

Abscissa_Resolution: 30
Ordinate_Resolution: 30

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000

Denominator_of_Flattening_Ratio: 298.257223563

Entity_and_Attribute_Information:

Overview_Description:

Entity_and_Attribute_Overview:

The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

Entity_and_Attribute_Detail_Citation:

 Data Dictionary: USDA, National Agricultural Statistics Service, 2016 Cropland Data Layer

 Source: USDA, National Agricultural Statistics Service

 The following is a cross reference list of the categorization codes and land covers.
 Note that not all land cover categories listed below will appear in an individual state.

 Raster
 Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0

 Categorization Code   Land Cover
         "0"       Background

 Raster
 Attribute Domain Values and Definitions: CROPS 1-20

 Categorization Code   Land Cover
           "1"       Corn
           "2"       Cotton
           "3"       Rice
           "4"       Sorghum
           "5"       Soybeans
           "6"       Sunflower
          "10"       Peanuts
          "11"       Tobacco
          "12"       Sweet Corn
          "13"       Pop or Orn Corn
          "14"       Mint

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

 Categorization Code   Land Cover
          "21"       Barley
          "22"       Durum Wheat
          "23"       Spring Wheat
          "24"       Winter Wheat
          "25"       Other Small Grains
          "26"       Dbl Crop WinWht/Soybeans
          "27"       Rye
          "28"       Oats
          "29"       Millet
          "30"       Speltz
          "31"       Canola
          "32"       Flaxseed
          "33"       Safflower
          "34"       Rape Seed
          "35"       Mustard
          "36"       Alfalfa
          "37"       Other Hay/Non Alfalfa
          "38"       Camelina
          "39"       Buckwheat

 Raster
 Attribute Domain Values and Definitions: CROPS 41-60

 Categorization Code   Land Cover
          "41"       Sugarbeets
          "42"       Dry Beans
          "43"       Potatoes
          "44"       Other Crops
          "45"       Sugarcane
          "46"       Sweet Potatoes
          "47"       Misc Vegs & Fruits
          "48"       Watermelons
          "49"       Onions
          "50"       Cucumbers
          "51"       Chick Peas
          "52"       Lentils
          "53"       Peas
          "54"       Tomatoes
          "55"       Caneberries
          "56"       Hops
          "57"       Herbs
          "58"       Clover/Wildflowers
          "59"       Sod/Grass Seed
          "60"       Switchgrass

 Raster
 Attribute Domain Values and Definitions: NON-CROP 61-65

 Categorization Code   Land Cover
          "61"       Fallow/Idle Cropland
          "63"       Forest
          "64"       Shrubland
          "65"       Barren

 Raster
 Attribute Domain Values and Definitions: CROPS 66-80

 Categorization Code   Land Cover
          "66"       Cherries
          "67"       Peaches
          "68"       Apples
          "69"       Grapes
          "70"       Christmas Trees
          "71"       Other Tree Crops
          "72"       Citrus
          "74"       Pecans
          "75"       Almonds
          "76"       Walnuts
          "77"       Pears

 Raster
 Attribute Domain Values and Definitions: OTHER 81-109

 Categorization Code   Land Cover
          "81"       Clouds/No Data
          "82"       Developed
          "83"       Water
          "87"       Wetlands
          "88"       Nonag/Undefined
          "92"       Aquaculture

 Raster
 Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195

 Categorization Code   Land Cover
         "111"       Open Water
         "112"       Perennial Ice/Snow
         "121"       Developed/Open Space
         "122"       Developed/Low Intensity
         "123"       Developed/Med Intensity
         "124"       Developed/High Intensity
         "131"       Barren
         "141"       Deciduous Forest
         "142"       Evergreen Forest
         "143"       Mixed Forest
         "152"       Shrubland
         "176"       Grassland/Pasture
         "190"       Woody Wetlands
         "195"       Herbaceous Wetlands

 Raster
 Attribute Domain Values and Definitions: CROPS 195-255

 Categorization Code   Land Cover
         "204"       Pistachios
         "205"       Triticale
         "206"       Carrots
         "207"       Asparagus
         "208"       Garlic
         "209"       Cantaloupes
         "210"       Prunes
         "211"       Olives
         "212"       Oranges
         "213"       Honeydew Melons
         "214"       Broccoli
         "215"       Avocados
         "216"       Peppers
         "217"       Pomegranates
         "218"       Nectarines
         "219"       Greens
         "220"       Plums
         "221"       Strawberries
         "222"       Squash
         "223"       Apricots
         "224"       Vetch
         "225"       Dbl Crop WinWht/Corn
         "226"       Dbl Crop Oats/Corn
         "227"       Lettuce
         "229"       Pumpkins
         "230"       Dbl Crop Lettuce/Durum Wht
         "231"       Dbl Crop Lettuce/Cantaloupe
         "232"       Dbl Crop Lettuce/Cotton
         "233"       Dbl Crop Lettuce/Barley
         "234"       Dbl Crop Durum Wht/Sorghum
         "235"       Dbl Crop Barley/Sorghum
         "236"       Dbl Crop WinWht/Sorghum
         "237"       Dbl Crop Barley/Corn
         "238"       Dbl Crop WinWht/Cotton
         "239"       Dbl Crop Soybeans/Cotton
         "240"       Dbl Crop Soybeans/Oats
         "241"       Dbl Crop Corn/Soybeans
         "242"       Blueberries
         "243"       Cabbage
         "244"       Cauliflower
         "245"       Celery
         "246"       Radishes
         "247"       Turnips
         "248"       Eggplants
         "249"       Gourds
         "250"       Cranberries
         "254"       Dbl Crop Barley/Soybeans

2016 Texas Cropland Data Layer | NASS/USDA

Metadata: