2017 California 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 California 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/CA>

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 California Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20170503 PATH/ROW(S) 742
DEIMOS-1 DATE 20170520 PATH/ROW(S) 805
DEIMOS-1 DATE 20170616 PATH/ROW(S) 93C
DEIMOS-1 DATE 20170617 PATH/ROW(S) 945
DEIMOS-1 DATE 20170620 PATH/ROW(S) 963
DEIMOS-1 DATE 20170623 PATH/ROW(S) 987
DEIMOS-1 DATE 20170624 PATH/ROW(S) 993
DEIMOS-1 DATE 20170703 PATH/ROW(S) 9F8
DEIMOS-1 DATE 20170704 PATH/ROW(S) A03
DEIMOS-1 DATE 20170707 PATH/ROW(S) A24
DEIMOS-1 DATE 20170717 PATH/ROW(S) A8E
DEIMOS-1 DATE 20170718 PATH/ROW(S) A9C
DEIMOS-1 DATE 20170721 PATH/ROW(S) AB9
DEIMOS-1 DATE 20170728 PATH/ROW(S) B0C
DEIMOS-1 DATE 20170731 PATH/ROW(S) B2C
DEIMOS-1 DATE 20170807 PATH/ROW(S) B73
DEIMOS-1 DATE 20170817 PATH/ROW(S) BD9
DEIMOS-1 DATE 20170824 PATH/ROW(S) C23
DEIMOS-1 DATE 20170917 PATH/ROW(S) CF4
DEIMOS-1 DATE 20170920 PATH/ROW(S) D10
DEIMOS-1 DATE 20170924 PATH/ROW(S) D2F

RESOURCESAT-2 LISS-3 20170502 PATH/ROW(S) 246
RESOURCESAT-2 LISS-3 20170521 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170606 PATH/ROW(S) 253
RESOURCESAT-2 LISS-3 20170629 PATH/ROW(S) 248
RESOURCESAT-2 LISS-3 20170704 PATH/ROW(S) 249
RESOURCESAT-2 LISS-3 20170708 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170713 PATH/ROW(S) 246
RESOURCESAT-2 LISS-3 20170801 PATH/ROW(S) 245
RESOURCESAT-2 LISS-3 20170812 PATH/ROW(S) 252
RESOURCESAT-2 LISS-3 20170825 PATH/ROW(S) 245

LANDSAT 8 OLI/TIRS DATE 20170415 PATH 040 ROW(S) 25-38
LANDSAT 8 OLI/TIRS DATE 20170501 PATH 040 ROW(S) 24-38
LANDSAT 8 OLI/TIRS DATE 20170513 PATH 044 ROW(S) 25-26 28-35
LANDSAT 8 OLI/TIRS DATE 20170526 PATH 039 ROW(S) 24-28 31-38
LANDSAT 8 OLI/TIRS DATE 20170529 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170611 PATH 039 ROW(S) 24-38
LANDSAT 8 OLI/TIRS DATE 20170614 PATH 044 ROW(S) 25-26 28-35
LANDSAT 8 OLI/TIRS DATE 20170627 PATH 039 ROW(S) 24-38
LANDSAT 8 OLI/TIRS DATE 20170630 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170704 PATH 040 ROW(S) 24-38
LANDSAT 8 OLI/TIRS DATE 20170713 PATH 039 ROW(S) 25-38
LANDSAT 8 OLI/TIRS DATE 20170716 PATH 044 ROW(S) 25-35
LANDSAT 8 OLI/TIRS DATE 20170902 PATH 044 ROW(S) 25-35

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATASET 2011 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATASET 2011 TREE CANOPY
USDA, NASS AG MASK BASED ON 2012-2016 CDLS (INTERNAL USE DATA LAYER)
VINEYARD MASK BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)
ALMOND MASK BASED ON PREVIOUS CALIFORNIA CDLS (INTERNAL USE DATA LAYER)

SENTINEL-2 DATE 20170421 PATH/ROW(S) 127
SENTINEL-2 DATE 20170504 PATH/ROW(S) 027
SENTINEL-2 DATE 20170510 PATH/ROW(S) 113
SENTINEL-2 DATE 20170511 PATH/ROW(S) 127
SENTINEL-2 DATE 20170606 PATH/ROW(S) 070
SENTINEL-2 DATE 20170610 PATH/ROW(S) 127
SENTINEL-2 DATE 20170613 PATH/ROW(S) 027
SENTINEL-2 DATE 20170619 PATH/ROW(S) 113
SENTINEL-2 DATE 20170620 PATH/ROW(S) 127
SENTINEL-2 DATE 20170626 PATH/ROW(S) 070
SENTINEL-2 DATE 20170703 PATH/ROW(S) 027
SENTINEL-2 DATE 20170709 PATH/ROW(S) 113
SENTINEL-2 DATE 20170720 PATH/ROW(S) 127
SENTINEL-2 DATE 20170729 PATH/ROW(S) 113
SENTINEL-2 DATE 20170812 PATH/ROW(S) 027
SENTINEL-2 DATE 20170818 PATH/ROW(S) 113
SENTINEL-2 DATE 20170819 PATH/ROW(S) 127
SENTINEL-2 DATE 20170825 PATH/ROW(S) 070
SENTINEL-2 DATE 20170918 PATH/ROW(S) 127
SENTINEL-2 DATE 20170927 PATH/ROW(S) 113
SENTINEL-2 DATE 20170928 PATH/ROW(S) 127
SENTINEL-2 DATE 20171004 PATH/ROW(S) 070
SENTINEL-2 DATE 20171008 PATH/ROW(S) 127

UK-DMC-2 DATE 20170504 PATH/ROW(S) FA0
UK-DMC-2 DATE 20170602 PATH/ROW(S) 0FC
UK-DMC-2 DATE 20170802 PATH/ROW(S) 3A4
UK-DMC-2 DATE 20170808 PATH/ROW(S) 3F5

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2017 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2011
US BUREAU OF RECLAMATION, LOWER COLORADO RIVER ACCOUNTING SYSTEM 2017 CROP CLASSIFICATIONS
VINEYARD LOCATIONS AS IDENTIFIED BY E.&J. GALLO WINERY (2013 DATA)
ALMOND ORCHARDS AS IDENTIFIED BY LAND IQ (2014 DATA)

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

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 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: -124.5876
East_Bounding_Coordinate: -114.1885
North_Bounding_Coordinate: 41.9743
South_Bounding_Coordinate: 32.5028

Keywords:

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

Theme:

Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: DEIMOS-1
Theme_Keyword: UK-DMC 2
Theme_Keyword: 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 > California

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: California
Place_Keyword: CA

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 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 California Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy     Error     Kappa
-------------------------   -------  --------    ------     -----
OVERALL ACCURACY**          1574917     92.5%      7.5%     0.865


Cover                       Attribute  *Correct Producer's  Omission             User's Commission   Cond'l
Type                             Code    Pixels  Accuracy     Error     Kappa  Accuracy     Error     Kappa
----                             ----    ------  --------     -----     -----  --------     -----     -----
Corn                                1      3399     53.5%     46.5%     0.534     76.9%     23.1%     0.768
Cotton                              2      6014     83.1%     16.9%     0.830     81.7%     18.3%     0.816
Rice                                3     76758     97.3%      2.7%     0.972     99.7%      0.3%     0.997
Sorghum                             4       332     40.5%     59.5%     0.405     73.3%     26.7%     0.733
Sunflower                           6      1038     77.6%     22.4%     0.776     70.3%     29.7%     0.703
Pop or Orn Corn                    13         6     11.1%     88.9%     0.111     66.7%     33.3%     0.667
Mint                               14         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Barley                             21      4247     59.3%     40.7%     0.592     72.8%     27.2%     0.727
Durum Wheat                        22        26     14.4%     85.6%     0.144     19.0%     81.0%     0.190
Spring Wheat                       23       366     45.8%     54.3%     0.457     60.3%     39.7%     0.603
Winter Wheat                       24     25495     68.1%     31.9%     0.676     72.4%     27.6%     0.719
Rye                                27       122     32.4%     67.6%     0.324     47.1%     52.9%     0.471
Oats                               28      2882     43.0%     57.0%     0.429     58.3%     41.7%     0.582
Canola                             31       169     79.3%     20.7%     0.793     96.6%      3.4%     0.966
Safflower                          33      2762     75.1%     24.9%     0.750     80.5%     19.5%     0.804
Alfalfa                            36    101296     91.6%      8.4%     0.911     87.5%     12.5%     0.868
Other Hay/Non Alfalfa              37      9364     53.6%     46.4%     0.533     65.5%     34.5%     0.652
Sugarbeets                         41       919     46.7%     53.3%     0.467     86.0%     14.0%     0.860
Dry Beans                          42       314     50.7%     49.3%     0.507     50.2%     49.8%     0.501
Potatoes                           43        60     71.4%     28.6%     0.714     65.2%     34.8%     0.652
Other Crops                        44       850     69.4%     30.6%     0.694     88.4%     11.6%     0.884
Sweet Potatoes                     46        70     82.4%     17.6%     0.824     78.7%     21.3%     0.787
Misc Vegs & Fruits                 47         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Watermelons                        48       248     44.3%     55.7%     0.443     58.5%     41.5%     0.585
Onions                             49      3167     78.6%     21.4%     0.785     71.4%     28.6%     0.713
Cucumbers                          50       194     45.5%     54.5%     0.455     67.8%     32.2%     0.678
Peas                               53         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Tomatoes                           54      1568     55.9%     44.1%     0.559     72.4%     27.6%     0.724
Herbs                              57       140     23.4%     76.6%     0.234     30.6%     69.4%     0.305
Clover/Wildflowers                 58      3558     75.7%     24.3%     0.757     91.5%      8.5%     0.915
Sod/Grass Seed                     59      1113     47.2%     52.8%     0.472     65.3%     34.7%     0.653
Fallow/Idle Cropland               61     76593     80.8%     19.2%     0.799     80.5%     19.5%     0.796
Cherries                           66       117     16.2%     83.8%     0.161     23.3%     76.7%     0.232
Peaches                            67        40      9.9%     90.1%     0.099     43.0%     57.0%     0.430
Apples                             68         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Grapes                             69     71113     89.7%     10.3%     0.893     95.3%      4.7%     0.951
Other Tree Crops                   71       901     51.6%     48.4%     0.515     65.6%     34.4%     0.656
Citrus                             72      7228     93.0%      7.0%     0.929     94.9%      5.1%     0.949
Pecans                             74        19      7.6%     92.4%     0.076    100.0%      0.0%     1.000
Almonds                            75   1106470     98.7%      1.3%     0.974     99.2%      0.8%     0.984
Walnuts                            76     12143     72.3%     27.7%     0.721     67.0%     33.0%     0.668
Pears                              77       313     56.1%     43.9%     0.561     84.1%     15.9%     0.841
Pistachios                        204     18282     82.4%     17.6%     0.822     75.3%     24.7%     0.751
Triticale                         205      1893     46.3%     53.7%     0.462     61.0%     39.0%     0.609
Carrots                           206      1147     51.0%     49.0%     0.510     57.8%     42.2%     0.577
Garlic                            208      1355     65.0%     35.0%     0.650     90.5%      9.5%     0.905
Cantaloupes                       209       935     53.2%     46.8%     0.531     49.0%     51.0%     0.490
Olives                            211      1220     55.5%     44.5%     0.555     65.2%     34.8%     0.652
Oranges                           212      1197     70.6%     29.4%     0.706     63.7%     36.3%     0.637
Honeydew Melons                   213       324     45.3%     54.7%     0.453     54.8%     45.2%     0.548
Broccoli                          214        82     14.4%     85.6%     0.144     11.5%     88.5%     0.114
Peppers                           216       132     72.5%     27.5%     0.725     68.0%     32.0%     0.680
Pomegranates                      217      1696     71.2%     28.8%     0.712     79.5%     20.5%     0.795
Nectarines                        218        18     22.5%     77.5%     0.225     48.6%     51.4%     0.486
Greens                            219       521     29.7%     70.3%     0.296     13.1%     86.9%     0.131
Plums                             220       505     37.9%     62.1%     0.378     52.5%     47.5%     0.525
Strawberries                      221        18     21.4%     78.6%     0.214     60.0%     40.0%     0.600
Squash                            222         1      1.4%     98.6%     0.014      5.0%     95.0%     0.050
Vetch                             224       205     45.7%     54.3%     0.456     56.3%     43.7%     0.563
Dbl Crop WinWht/Corn              225     19059     79.1%     20.9%     0.788     77.2%     22.8%     0.770
Dbl Crop Oats/Corn                226      3086     61.6%     38.4%     0.616     60.8%     39.2%     0.607
Lettuce                           227       483     20.1%     79.9%     0.201     45.1%     54.9%     0.450
Pumpkins                          229         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Dbl Crop Lettuce/Cantaloupe       231         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Dbl Crop Lettuce/Cotton           232        22      3.5%     96.5%     0.035     73.3%     26.7%     0.733
Dbl Crop WinWht/Sorghum           236      1015     49.6%     50.4%     0.496     52.0%     48.0%     0.520
Dbl Crop Barley/Corn              237       288     53.1%     46.9%     0.531     79.6%     20.4%     0.796
Dbl Crop WinWht/Cotton            238        19      5.6%     94.4%     0.056     45.2%     54.8%     0.452
Blueberries                       242         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Cabbage                           243         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Cauliflower                       244         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Radishes                          246         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

Source_Information:

Source_Citation:

Citation_Information:

Originator: E.&J. Gallo Winery

Title: Vineyard Data

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: E.&J. Gallo Winery

Publication_Place: Modesto, CA 95354 USA

Publication_Date: 2013
Other_Citation_Details:

The E.&J. Gallo Winery company collects vineyard locations by driving and noting field locations on GPS. More information about E.&J. Gallo Winery can be found online at <http://gallo.com/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2013

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: Gallo
Source_Contribution:

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

Source_Information:

Source_Citation:

Citation_Information:

Originator: LandIQ

Title: Almond Data

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: LandIQ

Publication_Place: Sacramento, California 95811 USA

Publication_Date: unknown
Other_Citation_Details:

More information about LandIQ can be found online at <https://www.landiq.com/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2014

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: LandIQ
Source_Contribution:

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

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Title:

Lower Colorado River Water Accounting System (LCRAS) GIS data layer

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher:

United States Department of Interior, Bureau of Reclamation, Lower Colorado Region

Publication_Place: Boulder City, NV 89006-1470, USA

Publication_Date: 2017
Other_Citation_Details:

The Lower Colorado River Water Accounting System (LCRAS) GIS data layer contains an annually updated record of crop types that was used to supplement the training and validation of the Cropland Data Layer. The area covered is Southern California and Southwest Arizona. For more details please reference the Bureau of Reclamation website <https://www.usbr.gov/lc/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2017

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: BLM LCRAS GIS Data
Source_Contribution:

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

Process_Step:

Process_Description:

OVERVIEW: The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates, 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

2017 California Cropland Data Layer | NASS/USDA

Metadata: