2015 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: 20160212
Title: 2015 Texas 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/TX>

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 Texas Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20150502 PATH/ROW 859
DEIMOS-1 DATE 20150510 PATH/ROW 8C8
DEIMOS-1 DATE 20150527 PATH/ROW 9A2
DEIMOS-1 DATE 20150603 PATH/ROW A02
DEIMOS-1 DATE 20150619 PATH/ROW AE7
DEIMOS-1 DATE 20150623 PATH/ROW B1A
DEIMOS-1 DATE 20150626 PATH/ROW B4E
DEIMOS-1 DATE 20150706 PATH/ROW BD4
DEIMOS-1 DATE 20150713 PATH/ROW C41
DEIMOS-1 DATE 20150716 PATH/ROW C73
DEIMOS-1 DATE 20150720 PATH/ROW CAC
DEIMOS-1 DATE 20150724 PATH/ROW CE4
DEIMOS-1 DATE 20150727 PATH/ROW D14
DEIMOS-1 DATE 20150805 PATH/ROW D91
DEIMOS-1 DATE 20150809 PATH/ROW DD4
DEIMOS-1 DATE 20150816 PATH/ROW E39
DEIMOS-1 DATE 20150820 PATH/ROW E6F
DEIMOS-1 DATE 20150826 PATH/ROW EC6
DEIMOS-1 DATE 20150830 PATH/ROW EFF
DEIMOS-1 DATE 20151002 PATH/ROW 0CC
DEIMOS-1 DATE 20151006 PATH/ROW 102
DEIMOS-1 DATE 20151019 PATH/ROW 1B1

LANDSAT 8 OLI/TIRS DATE 20141021 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20141030 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20141111 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20141118 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20141120 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20141124 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20141125 PATH 032 ROW(S) 26-39
LANDSAT 8 OLI/TIRS DATE 20141127 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20141129 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150420 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150429 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150501 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150503 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150529 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150602 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150604 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150611 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150623 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150625 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150630 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150711 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150713 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150716 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150718 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150720 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150725 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150727 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150729 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150805 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150807 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150810 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150812 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150814 PATH 026 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150826 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150828 PATH 028 ROW(S) 26-41
LANDSAT 8 OLI/TIRS DATE 20150902 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150904 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150906 PATH 027 ROW(S) 26-42
LANDSAT 8 OLI/TIRS DATE 20150908 PATH 025 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151006 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151013 PATH 030 ROW(S) 26-40

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 2010-2014 CDLS (INTERNAL USE DATA LAYER)

UK-DMC-2 DATE 20150425 PATH/ROW 31B
UK-DMC-2 DATE 20150501 PATH/ROW 366
UK-DMC-2 DATE 20150518 PATH/ROW 407
UK-DMC-2 DATE 20150610 PATH/ROW 4E0
UK-DMC-2 DATE 20150630 PATH/ROW 5AD
UK-DMC-2 DATE 20150713 PATH/ROW 62F
UK-DMC-2 DATE 20150716 PATH/ROW 650
UK-DMC-2 DATE 20150719 PATH/ROW 668
UK-DMC-2 DATE 20150720 PATH/ROW 671
UK-DMC-2 DATE 20150801 PATH/ROW 6DB
UK-DMC-2 DATE 20150802 PATH/ROW 6E6
UK-DMC-2 DATE 20150805 PATH/ROW 6F8
UK-DMC-2 DATE 20150812 PATH/ROW 73F
UK-DMC-2 DATE 20150822 PATH/ROW 799
UK-DMC-2 DATE 20150901 PATH/ROW 7DF
UK-DMC-2 DATE 20150907 PATH/ROW 808
UK-DMC-2 DATE 20151001 PATH/ROW 89D
UK-DMC-2 DATE 20151014 PATH/ROW 8F5

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2015 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: 20140101
Ending_Date: 20151231

Currentness_Reference: 2015 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: 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 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, 2015 Texas Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**          783,682     80.0%   20.0%   0.751


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1     89492     89.24%    10.76%   0.887     89.38%     10.62%    0.888
Cotton                           2    167710     95.16%     4.84%   0.946     86.52%     13.48%    0.852
Rice                             3     11470     93.56%     6.44%   0.935     94.54%      5.46%    0.945
Sorghum                          4    127313     83.21%    16.79%   0.818     82.73%     17.27%    0.813
Soybeans                         5      4316     56.44%    43.56%   0.563     79.51%     20.49%    0.794
Sunflower                        6      3059     57.22%    42.78%   0.571     84.64%     15.36%    0.846
Peanuts                         10       904     45.15%    54.85%   0.451     74.90%     25.10%    0.749
Sweet Corn                      12         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Barley                          21       113      5.99%    94.01%   0.060     51.83%     48.17%    0.518
Spring Wheat                    23      1778     49.18%    50.82%   0.491     65.97%     34.03%    0.659
Winter Wheat                    24    310615     93.32%     6.68%   0.919     88.37%     11.63%    0.861
Dbl Crop WinWht/Soybeans        26       364     44.12%    55.88%   0.441     77.78%     22.22%    0.778
Rye                             27       792     34.45%    65.55%   0.344     69.66%     30.34%    0.696
Oats                            28     11123     48.77%    51.23%   0.484     70.65%     29.35%    0.703
Millet                          29       175     33.21%    66.79%   0.332     86.63%     13.37%    0.866
Canola                          31       438     52.71%    47.29%   0.527     97.33%      2.67%    0.973
Alfalfa                         36      3069     78.07%    21.93%   0.780     87.91%     12.09%    0.879
Other Hay/Non Alfalfa           37     18550     43.85%    56.15%   0.431     69.63%     30.37%    0.690
Buckwheat                       39         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Sugarbeets                      41         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dry Beans                       42        41     21.13%    78.87%   0.211     69.49%     30.51%    0.695
Potatoes                        43       431     69.40%    30.60%   0.694     81.63%     18.37%    0.816
Other Crops                     44         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Sugarcane                       45      1755     89.00%    11.00%   0.890     85.57%     14.43%    0.856
Sweet Potatoes                  46         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Misc Vegs & Fruits              47         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Watermelons                     48        22     14.86%    85.14%   0.149     38.60%     61.40%    0.386
Onions                          49        22     11.46%    88.54%   0.115     44.00%     56.00%    0.440
Cucumbers                       50         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Peas                            53        95     15.52%    84.48%   0.155     59.01%     40.99%    0.590
Tomatoes                        54         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Herbs                           57       773     28.20%    71.80%   0.282     57.56%     42.44%    0.575
Clover/Wildflowers              58         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Sod/Grass Seed                  59      1746     57.23%    42.77%   0.572     81.32%     18.68%    0.813
Switchgrass                     60         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Fallow/Idle Cropland            61     21982     39.29%    60.71%   0.383     66.80%     33.20%    0.658
Peaches                         67         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Grapes                          69         3     33.33%    66.67%   0.333     15.79%     84.21%    0.158
Citrus                          72        99     61.88%    38.13%   0.619     75.00%     25.00%    0.750
Pecans                          74       951     63.83%    36.17%   0.638     86.85%     13.15%    0.868
Aquaculture                     92       234     58.21%    41.79%   0.582     84.48%     15.52%    0.845
Triticale                      205      1394     33.04%    66.96%   0.330     75.64%     24.36%    0.756
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         3     12.00%    88.00%   0.120     50.00%     50.00%    0.500
Honeydew Melons                213         4     33.33%    66.67%   0.333    100.00%      0.00%    1.000
Peppers                        216        56     69.14%    30.86%   0.691     82.35%     17.65%    0.824
Greens                         219         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Squash                         222         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Vetch                          224         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Dbl Crop WinWht/Corn           225       737     44.64%    55.36%   0.446     66.16%     33.84%    0.661
Pumpkins                       229         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dbl Crop WinWht/Sorghum        236      1078     20.12%    79.88%   0.200     56.68%     43.32%    0.566
Dbl Crop Barley/Corn           237         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dbl Crop WinWht/Cotton         238      1206     23.52%    76.48%   0.235     64.46%     35.54%    0.644
Dbl Crop Soybeans/Oats         240         3      4.41%    95.59%   0.044    100.00%      0.00%    1.000
Dbl Crop Corn/Soybeans         241         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
Cauliflower                    244         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: 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

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:

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: 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:

 ***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

2015 Texas Cropland Data Layer | NASS/USDA

Metadata: