2017 Oregon 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: 20180126
Title: 2017 Oregon Cropland Data Layer | NASS/USDA
Edition: 2017 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/OR>

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2017 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 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, 2017 Oregon Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20170523 PATH/ROW(S) 827
DEIMOS-1 DATE 20170703 PATH/ROW(S) 9F9
DEIMOS-1 DATE 20170717 PATH/ROW(S) A8F
DEIMOS-1 DATE 20170927 PATH/ROW(S) D48

RESOURCESAT-2 LISS-3 20170521 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170623 PATH/ROW(S) 242
RESOURCESAT-2 LISS-3 20170624 PATH/ROW(S) 247
RESOURCESAT-2 LISS-3 20170703 PATH/ROW(S) 244
RESOURCESAT-2 LISS-3 20170708 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170713 PATH/ROW(S) 246
RESOURCESAT-2 LISS-3 20170727 PATH/ROW(S) 244
RESOURCESAT-2 LISS-3 20170801 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170816 PATH/ROW(S) 248
RESOURCESAT-2 LISS-3 20170825 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170928 PATH/ROW(S) 247

LANDSAT 8 OLI/TIRS DATE 20161104 PATH 042 ROW(S) 26-37
LANDSAT 8 OLI/TIRS DATE 20161109 PATH 045 ROW(S) 26-34
LANDSAT 8 OLI/TIRS DATE 20161111 PATH 043 ROW(S) 26-36
LANDSAT 8 OLI/TIRS DATE 20170429 PATH 042 ROW(S) 24-38
LANDSAT 8 OLI/TIRS DATE 20170522 PATH 043 ROW(S) 25-37
LANDSAT 8 OLI/TIRS DATE 20170527 PATH 046 ROW(S) 25-32
LANDSAT 8 OLI/TIRS DATE 20170529 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170707 PATH 045 ROW(S) 25-34
LANDSAT 8 OLI/TIRS DATE 20170716 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170730 PATH 046 ROW(S) 25-33
LANDSAT 8 OLI/TIRS DATE 20170803 PATH 042 ROW(S) 25-36
LANDSAT 8 OLI/TIRS DATE 20170808 PATH 045 ROW(S) 25-34
LANDSAT 8 OLI/TIRS DATE 20170817 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170819 PATH 042 ROW(S) 25-36
LANDSAT 8 OLI/TIRS DATE 20170824 PATH 045 ROW(S) 25-34
LANDSAT 8 OLI/TIRS DATE 20170826 PATH 043 ROW(S) 25-36
LANDSAT 8 OLI/TIRS DATE 20170831 PATH 046 ROW(S) 25-33
LANDSAT 8 OLI/TIRS DATE 20170902 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170911 PATH 043 ROW(S) 25-36
LANDSAT 8 OLI/TIRS DATE 20170927 PATH 043 ROW(S) 25-36

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 2012-2016 CDLS (INTERNAL USE DATA LAYER)

SENTINEL-2 DATE 20170503 PATH/ROW(S) 013
SENTINEL-2 DATE 20170523 PATH/ROW(S) 013
SENTINEL-2 DATE 20170527 PATH/ROW(S) 070
SENTINEL-2 DATE 20170605 PATH/ROW(S) 056
SENTINEL-2 DATE 20170619 PATH/ROW(S) 113
SENTINEL-2 DATE 20170629 PATH/ROW(S) 113
SENTINEL-2 DATE 20170702 PATH/ROW(S) 013
SENTINEL-2 DATE 20170706 PATH/ROW(S) 070
SENTINEL-2 DATE 20170709 PATH/ROW(S) 113
SENTINEL-2 DATE 20170716 PATH/ROW(S) 070
SENTINEL-2 DATE 20170719 PATH/ROW(S) 113
SENTINEL-2 DATE 20170722 PATH/ROW(S) 013
SENTINEL-2 DATE 20170729 PATH/ROW(S) 113
SENTINEL-2 DATE 20170808 PATH/ROW(S) 113
SENTINEL-2 DATE 20170818 PATH/ROW(S) 113
SENTINEL-2 DATE 20170825 PATH/ROW(S) 070
SENTINEL-2 DATE 20170831 PATH/ROW(S) 013
SENTINEL-2 DATE 20170904 PATH/ROW(S) 070
SENTINEL-2 DATE 20170927 PATH/ROW(S) 113

UK-DMC-2 DATE 20170619 PATH/ROW(S) 1C1
UK-DMC-2 DATE 20170718 PATH/ROW(S) 2E3
UK-DMC-2 DATE 20170801 PATH/ROW(S) 396
UK-DMC-2 DATE 20170804 PATH/ROW(S) 3BF
UK-DMC-2 DATE 20170818 PATH/ROW(S) 465
UK-DMC-2 DATE 20170927 PATH/ROW(S) 66C

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2017 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: 20161001
Ending_Date: 20171231

Currentness_Reference: 2017 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -125.0138
East_Bounding_Coordinate: -116.4950
North_Bounding_Coordinate: 46.1729
South_Bounding_Coordinate: 41.9980

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: ISRO ResourceSat-2 LISS-3
Theme_Keyword: ESA SENTINEL-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 > Oregon

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Oregon
Place_Keyword: OR

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2017

Access_Constraints: None
Use_Constraints:

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

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

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

Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 7 Enterprise; ERDAS Imagine Versions 2011 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.11 <http://www.rulequest.com/>; NLCD Mapping Tool v2.08 <https://www.mrlc.gov/>.

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

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

***NOTE ABOUT THE UNBUFFERED VALIDATION ACCURACY TABLES BEGINNING IN 2016: The training and validation data used to create and accuracy assess the CDL has traditionally been based on ground truth data that is buffered inward 30 meters. This was done 1) because satellite imagery (as well as the polygon reference data) in the past was not georeferenced to the same precision as now (i.e. everything "stacked" less perfectly), 2) to eliminate from training spectrally-mixed pixels at land cover boundaries, and 3) to be spatially conservative during the era when coarser 56 meter AWiFS satellite imagery was incorporated. Ultimately, all of these scenarios created "blurry" edge pixels through the seasonal time series which it was found if ignored from training in the classification helped improve the quality of CDL. However, the accuracy assessment portion of the analysis also used buffered data meaning those same edge pixels were not assessed fully with the rest of the classification. This would be inconsequential if those edge pixels were similar in nature to the rest of the scene but they are not- they tend to be more difficult to classify correctly. Thus, the accuracy assessments as have been presented are inflated somewhat. Beginning with the 2016 CDL season we are creating CDL accuracy assessments using unbuffered validation data. These "unbuffered" accuracy metrics will now reflect the accuracy of field edges which have not been represented previously. Beginning with the 2016 CDLs we published both the traditional "buffered" accuracy metrics and the new "unbuffered" accuracy assessments. The purpose of publishing both versions is to provide a benchmark for users interested in comparing the different validation methods. For the 2017 CDL season we are now only publishing the unbuffered accuracy only publishing the unbuffered accuracy assessments within the official metadata files and offer the full "unbuffered" error matrices for download on the FAQs webpage. Both metadata and FAQs are accessible at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. We plan to continue producing these unbuffered accuracy assessments for future CDLs. However, there are no plans to create these unbuffered accuracy assessments for past years. It should be noted that accuracy assessment is challenging and the CDL group has always strived to provide robust metrics of usability to the land cover community. This admission of modestly inflated accuracy measures does not render past assessments useless. They were all done consistently so comparison across years and/or states is still valid. Yet, by providing both scenarios for 2016 gives guidance on the bias. 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, 2017 Oregon Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy     Error     Kappa
-------------------------   -------  --------    ------     -----
OVERALL ACCURACY**           392187     87.0%     13.0%     0.839


Cover                       Attribute  *Correct Producer's  Omission             User's Commission   Cond'l
Type                             Code    Pixels  Accuracy     Error     Kappa  Accuracy     Error     Kappa
----                             ----    ------  --------     -----     -----  --------     -----     -----
Corn                                1      9034     82.5%     17.5%     0.823     88.8%     11.2%     0.887
Sorghum                             4         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Soybeans                            5        21     12.8%     87.2%     0.128     58.3%     41.7%     0.583
Sunflower                           6        68     18.2%     81.8%     0.182     97.1%      2.9%     0.971
Mint                               14        51     41.1%     58.9%     0.411     78.5%     21.5%     0.785
Barley                             21      3657     61.6%     38.4%     0.614     59.4%     40.6%     0.592
Spring Wheat                       23      7466     60.5%     39.5%     0.601     74.4%     25.6%     0.741
Winter Wheat                       24    135876     96.7%      3.3%     0.961     96.2%      3.8%     0.955
Rye                                27       126     24.2%     75.8%     0.242     64.0%     36.0%     0.639
Oats                               28       557     33.3%     66.7%     0.332     58.3%     41.7%     0.583
Canola                             31       652     49.8%     50.2%     0.498     94.2%      5.8%     0.942
Flaxseed                           32         2     10.5%     89.5%     0.105     16.7%     83.3%     0.167
Safflower                          33         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Rape Seed                          34        20     76.9%     23.1%     0.769     87.0%     13.0%     0.870
Mustard                            35         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Alfalfa                            36     41377     85.7%     14.3%     0.849     82.2%     17.8%     0.813
Other Hay/Non Alfalfa              37     13597     59.4%     40.6%     0.587     79.6%     20.4%     0.791
Buckwheat                          39         5     16.7%     83.3%     0.167     19.2%     80.8%     0.192
Sugarbeets                         41      1817     75.1%     24.9%     0.751     87.6%     12.4%     0.875
Dry Beans                          42      1385     65.0%     35.0%     0.649     80.4%     19.6%     0.803
Potatoes                           43      4842     86.3%     13.7%     0.863     90.1%      9.9%     0.900
Other Crops                        44       129     21.1%     78.9%     0.211     36.8%     63.2%     0.367
Misc Vegs & Fruits                 47         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Watermelons                        48        53     63.1%     36.9%     0.631     84.1%     15.9%     0.841
Onions                             49      3129     87.7%     12.3%     0.877     83.9%     16.1%     0.838
Cucumbers                          50         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Peas                               53      2345     72.0%     28.0%     0.719     80.3%     19.7%     0.803
Caneberries                        55         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Hops                               56       707     81.7%     18.3%     0.817     82.6%     17.4%     0.826
Herbs                              57      2014     69.2%     30.8%     0.691     84.3%     15.7%     0.843
Clover/Wildflowers                 58      3686     73.4%     26.6%     0.732     81.3%     18.7%     0.812
Sod/Grass Seed                     59     36643     86.8%     13.2%     0.863     90.9%      9.1%     0.905
Fallow/Idle Cropland               61    115474     94.7%      5.3%     0.940     96.8%      3.2%     0.963
Cherries                           66      1856     78.8%     21.2%     0.788     86.6%     13.4%     0.865
Peaches                            67         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Apples                             68       368     54.7%     45.3%     0.547     86.0%     14.0%     0.860
Grapes                             69       157     27.2%     72.8%     0.271     64.1%     35.9%     0.641
Christmas Trees                    70       293     56.0%     44.0%     0.560     74.6%     25.4%     0.745
Other Tree Crops                   71      2036     62.8%     37.2%     0.627     74.1%     25.9%     0.740
Walnuts                            76         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Pears                              77       803     71.0%     29.0%     0.710     80.1%     19.9%     0.801
Triticale                         205       511     25.1%     74.9%     0.250     62.8%     37.2%     0.627
Carrots                           206       606     60.6%     39.4%     0.606     85.5%     14.5%     0.855
Garlic                            208       140     58.8%     41.2%     0.588     74.9%     25.1%     0.749
Broccoli                          214         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Nectarines                        218         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Greens                            219        13     22.4%     77.6%     0.224     26.5%     73.5%     0.265
Plums                             220         4      4.4%     95.6%     0.044    100.0%      0.0%     1.000
Strawberries                      221         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Squash                            222       240     69.0%     31.0%     0.690     82.5%     17.5%     0.825
Vetch                             224         5      3.9%     96.1%     0.039     71.4%     28.6%     0.714
Lettuce                           227         5     11.6%     88.4%     0.116     19.2%     80.8%     0.192
Pumpkins                          229         5      9.6%     90.4%     0.096      8.1%     91.9%     0.081
Blueberries                       242       209     57.1%     42.9%     0.571     59.7%     40.3%     0.597
Cabbage                           243         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Cauliflower                       244         5     26.3%     73.7%     0.263     62.5%     37.5%     0.625
Radishes                          246       172     54.3%     45.7%     0.542     52.8%     47.2%     0.527
Turnips                           247        26     24.3%     75.7%     0.243     92.9%      7.1%     0.929
Gourds                            249         0      0.0%    100.0%     0.000      n/a       n/a       n/a

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

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

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

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

Attribute_Accuracy_Explanation:

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

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

Logical_Consistency_Report:

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

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

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

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

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Indian Space Research Organization (ISRO)
Title: ResourceSat-2 LISS-3
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Indian Space Research Organization (ISRO)
Publication_Place:

Indian Space Research Organisation HQ, Department of Space, Government of India Antariksh Bhavan, New BEL Road, Bangalore 560 231

Publication_Date: 2017
Other_Citation_Details:

The ISRO ResourceSat-2 LISS-3 satellite sensor operates in four spectral bands at a spatial resolution of 24 meters. Additional information about the data can be obtained at <https://www.isro.gov.in/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2017 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 24 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20161001
Ending_Date: 20171231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: LISS-3
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator: European Space Agency (ESA)
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: Copernicus - European Commission
Publication_Place: European Commission, Brussels (Belgium)

Publication_Date: 2017
Other_Citation_Details:

The ESA SENTINEL-2 satellite sensor operates in twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional information about the data can be obtained at <http://www.esa.int/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2017 CDL Program, the imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 10 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20161001
Ending_Date: 20171231

Source_Currentness_Reference: ground condition

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

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: 2017
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: 20161001
Ending_Date: 20171231

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: 2017
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: 20161001
Ending_Date: 20171231

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: 2017
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: 20161001
Ending_Date: 20171231

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: 2017
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: 2017

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, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 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: 2017
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

Cloud_Cover: 0

Spatial_Data_Organization_Information:

Indirect_Spatial_Reference: Oregon
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 16497

Column_Count: 22013

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, 2017 Cropland Data Layer

 Source: USDA, National Agricultural Statistics Service

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

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

 Categorization Code   Land Cover
         "0"       Background

 Raster
 Attribute Domain Values and Definitions: CROPS 1-20

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

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

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

 Raster
 Attribute Domain Values and Definitions: CROPS 41-60

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

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

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

 Raster
 Attribute Domain Values and Definitions: CROPS 66-80

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

 Raster
 Attribute Domain Values and Definitions: OTHER 81-109

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

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

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

 Raster
 Attribute Domain Values and Definitions: CROPS 195-255

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

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA, NASS Customer Service
Contact_Person: USDA, NASS Customer Service Staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5038-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA

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

Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at <https://nassgeodata.gmu.edu/CropScape/> and <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Resource_Description: Cropland Data Layer - Oregon 2017
Distribution_Liability:

Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Format_Name: GEOTIFF
Format_Version_Date: 2017
Format_Information_Content: GEOTIFF

Digital_Transfer_Option:

Online_Option:

Computer_Contact_Information:

Network_Address:

Network_Resource_Name: <https://nassgeodata.gmu.edu/CropScape/>

Access_Instructions:

The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. It is also available free for download from the Geospatial Data Gateway website <https://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

Fees:

Ordering_Instructions:

The CDL is available online and free for download from the CropScape website <https://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. If you experience problems downloading all years of CDL data through the Geospatial Data Gateway then you can try to use the 'Direct Data Download' link in the lower right-hand corner of their webpage.

Custom_Order_Process:

For a list of other states and years of available CDL data please visit <https://nassgeodata.gmu.edu/CropScape/> or <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Technical_Prerequisites:

If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/> or using the freeware browser ESRI ArcGIS Explorer <https://www.esri.com/>.

Metadata_Reference_Information:

Metadata_Date: 20180126
Metadata_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Access_Constraints: No restrictions on the distribution or use of the metadata file
Metadata_Use_Constraints: No restrictions on the distribution or use of the metadata file

2017 Oregon Cropland Data Layer | NASS/USDA

Metadata: