2023 Maryland Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)

Publication_Date: 20240131
Title: 2023 Maryland Cropland Data Layer | USDA NASS
Edition: 2023 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 CroplandCROS <https://croplandcros.scinet.usda.gov/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>.

***Maryland Cropland Data Layer specific information*** The processing for the Maryland CDL differed from the other CDLs in that Delaware, Maryland, New Jersey and Washington, D.C. were grouped and treated as one classification.

Online_Linkage: <https://croplandcros.scinet.usda.gov/>

Description:

Abstract:

The USDA NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2023 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B 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 data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the NLCD 2016.

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, 2023 Delaware, Maryland, New Jersey and Washington, D.C. Cropland Data Layers

CLASSIFICATION INPUTS:
LANDSAT 8 DATE 20230510 PATH/ORBIT 016
LANDSAT 8 DATE 20230526 PATH/ORBIT 016
LANDSAT 8 DATE 20230629 PATH/ORBIT 014
LANDSAT 8 DATE 20230731 PATH/ORBIT 014
LANDSAT 8 DATE 20230816 PATH/ORBIT 014
LANDSAT 8 DATE 20230901 PATH/ORBIT 014
LANDSAT 8 DATE 20230908 PATH/ORBIT 015
LANDSAT 8 DATE 20230915 PATH/ORBIT 016
LANDSAT 8 DATE 20231001 PATH/ORBIT 016
LANDSAT 8 DATE 20231003 PATH/ORBIT 014
LANDSAT 8 DATE 20231010 PATH/ORBIT 015
LANDSAT 8 DATE 20231019 PATH/ORBIT 014

LANDSAT 9 DATE 20230418 PATH/ORBIT 014
LANDSAT 9 DATE 20230605 PATH/ORBIT 014
LANDSAT 9 DATE 20230723 PATH/ORBIT 014
LANDSAT 9 DATE 20230730 PATH/ORBIT 015
LANDSAT 9 DATE 20230808 PATH/ORBIT 014
LANDSAT 9 DATE 20230822 PATH/ORBIT 016
LANDSAT 9 DATE 20230831 PATH/ORBIT 015
LANDSAT 9 DATE 20230902 PATH/ORBIT 013
LANDSAT 9 DATE 20230916 PATH/ORBIT 015
LANDSAT 9 DATE 20231002 PATH/ORBIT 015
LANDSAT 9 DATE 20231011 PATH/ORBIT 014

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATABASE 2016 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2019 IMPERVIOUSNESS
USDA, NASS CROPLAND DATA LAYERS 2017-2022

SENTINEL 2A DATE 20221020 PATH/ORBIT 097
SENTINEL 2A DATE 20221109 PATH/ORBIT 097
SENTINEL 2A DATE 20230515 PATH/ORBIT 054
SENTINEL 2A DATE 20230518 PATH/ORBIT 097
SENTINEL 2A DATE 20230525 PATH/ORBIT 054
SENTINEL 2A DATE 20230601 PATH/ORBIT 011
SENTINEL 2A DATE 20230711 PATH/ORBIT 011
SENTINEL 2A DATE 20230820 PATH/ORBIT 011
SENTINEL 2A DATE 20230902 PATH/ORBIT 054
SENTINEL 2A DATE 20230915 PATH/ORBIT 097
SENTINEL 2A DATE 20230919 PATH/ORBIT 011
SENTINEL 2A DATE 20231002 PATH/ORBIT 054
SENTINEL 2A DATE 20231012 PATH/ORBIT 054

SENTINEL 2B DATE 20221022 PATH/ORBIT 054
SENTINEL 2B DATE 20221108 PATH/ORBIT 011
SENTINEL 2B DATE 20230420 PATH/ORBIT 054
SENTINEL 2B DATE 20230510 PATH/ORBIT 054
SENTINEL 2B DATE 20230517 PATH/ORBIT 011
SENTINEL 2B DATE 20230602 PATH/ORBIT 097
SENTINEL 2B DATE 20230606 PATH/ORBIT 011
SENTINEL 2B DATE 20230619 PATH/ORBIT 054
SENTINEL 2B DATE 20230629 PATH/ORBIT 054
SENTINEL 2B DATE 20230706 PATH/ORBIT 011
SENTINEL 2B DATE 20230726 PATH/ORBIT 011
SENTINEL 2B DATE 20230729 PATH/ORBIT 054
SENTINEL 2B DATE 20230831 PATH/ORBIT 097
SENTINEL 2B DATE 20230907 PATH/ORBIT 054
SENTINEL 2B DATE 20231004 PATH/ORBIT 011

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2023 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATABASE 2019

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: 20221001
Ending_Date: 20231231

Currentness_Reference: 2023 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -79.5198
East_Bounding_Coordinate: -75.0490
North_Bounding_Coordinate: 39.7192
South_Bounding_Coordinate: 37.9270

Keywords:

Theme:

Theme_Keyword_Thesaurus: NGDA Portfolio Themes
Theme_Keyword: National Geospatial Data Asset
Theme_Keyword: Land Use Land Cover Theme
Theme_Keyword: NGDA
Theme_Keyword: NGDA109

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: ESA SENTINEL-2
Theme_Keyword: Landsat
Theme_Keyword: CroplandCROS

Place:

Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword: Continent > North America > United States of America > Maryland

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: Maryland
Place_Keyword: MD

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2023

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) or ERDAS Imagine (.img) file formats then we suggest using CroplandCROS <https://croplandcros.scinet.usda.gov/>.

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 10 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.11a <http://www.rulequest.com/>; NLCD Mapping Tool version 'NLCD_for_IMAGINE_ver_16_0_0_build_199_2018-09-12' <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 (pre-2007) of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

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

USDA National Agricultural Statistics Service, 2023 Delaware, Maryland, New Jersey and Washington, D.C. Cropland Data Layers
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only             *Correct  Accuracy       Error      Kappa
-------------------------              -------   --------     ------      -----
FSA Crops                              446,810      83.7%      16.3%      0.773

Cover                     Attribute   *Correct Producer's   Omission                User's Commission     Cond'l
Type                           Code     Pixels   Accuracy      Error      Kappa   Accuracy      Error      Kappa
----                           ----     ------   --------      -----      -----   --------      -----      -----
Corn                              1    210,104      91.9%       8.1%      0.891      93.6%       6.4%      0.914
Cotton                            2          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Sorghum                           4      2,682      49.0%      51.0%      0.488      83.7%      16.3%      0.836
Soybeans                          5    140,043      87.6%      12.4%      0.849      90.2%       9.8%      0.880
Sunflower                         6          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Tobacco                          11          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Sweet Corn                       12      2,039      69.9%      30.1%      0.698      87.1%      12.9%      0.870
Pop or Orn Corn                  13         11      50.0%      50.0%      0.500      91.7%       8.3%      0.917
Mint                             14          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Barley                           21         59       8.8%      91.2%      0.088      44.0%      56.0%      0.440
Winter Wheat                     24      2,259      36.8%      63.2%      0.366      73.9%      26.1%      0.738
Dbl Crop WinWht/Soybeans         26     55,947      90.1%       9.9%      0.894      87.5%      12.5%      0.866
Rye                              27        124      17.6%      82.4%      0.175      59.9%      40.1%      0.599
Oats                             28         49      19.2%      80.8%      0.192      59.8%      40.2%      0.597
Millet                           29          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Rape Seed                        34          2       5.6%      94.4%      0.056     100.0%       0.0%      1.000
Alfalfa                          36      1,868      49.5%      50.5%      0.493      74.7%      25.3%      0.746
Other Hay/Non Alfalfa            37     17,756      59.7%      40.3%      0.585      70.8%      29.2%      0.698
Dry Beans                        42      1,083      46.0%      54.0%      0.459      94.3%       5.7%      0.943
Potatoes                         43        685      67.3%      32.7%      0.673      86.2%      13.8%      0.861
Other Crops                      44         52      28.4%      71.6%      0.284      58.4%      41.6%      0.584
Sweet Potatoes                   46        199      71.1%      28.9%      0.711      80.2%      19.8%      0.802
Misc Vegs & Fruits               47          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Watermelons                      48        765      71.1%      28.9%      0.711      82.6%      17.4%      0.826
Cucumbers                        50         10      20.4%      79.6%      0.204      33.3%      66.7%      0.333
Peas                             53        103      53.4%      46.6%      0.534     100.0%       0.0%      1.000
Tomatoes                         54        145      29.5%      70.5%      0.295      89.0%      11.0%      0.890
Hops                             56          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Herbs                            57          2       2.6%      97.4%      0.026      66.7%      33.3%      0.667
Clover/Wildflowers               58          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Sod/Grass Seed                   59      1,966      71.7%      28.3%      0.716      84.3%      15.7%      0.843
Switchgrass                      60          9      47.4%      52.6%      0.474      75.0%      25.0%      0.750
Fallow/Idle Cropland             61        143      16.0%      84.0%      0.159      42.1%      57.9%      0.420
Cherries                         66          1       3.6%      96.4%      0.036      14.3%      85.7%      0.143
Peaches                          67        141      61.8%      38.2%      0.618      70.5%      29.5%      0.705
Apples                           68        133      45.5%      54.5%      0.455      69.3%      30.7%      0.693
Grapes                           69          9      11.5%      88.5%      0.115      69.2%      30.8%      0.692
Christmas Trees                  70         41      25.2%      74.8%      0.251      56.9%      43.1%      0.569
Other Tree Crops                 71          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Walnuts                          76          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Pears                            77          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Open Water                      111     12,970      93.0%       7.0%      0.929      92.1%       7.9%      0.920
Developed/Open Space            121     42,261      99.1%       0.9%      0.990      82.6%      17.4%      0.817
Developed/Low Intensity         122     32,432      99.6%       0.4%      0.996      90.7%       9.3%      0.903
Developed/Med Intensity         123     20,554      99.8%       0.2%      0.998      96.2%       3.8%      0.961
Developed/High Intensity        124      9,703     100.0%       0.0%      1.000      99.2%       0.8%      0.992
Barren                          131        769      58.0%      42.0%      0.580      63.3%      36.7%      0.633
Deciduous Forest                141     79,183      85.9%      14.1%      0.840      76.1%      23.9%      0.733
Evergreen Forest                142      7,189      59.6%      40.4%      0.590      65.2%      34.8%      0.647
Mixed Forest                    143     11,415      41.1%      58.9%      0.396      51.8%      48.2%      0.502
Shrubland                       152         90       3.8%      96.2%      0.038      13.6%      86.4%      0.134
Grassland/Pasture               176     19,247      64.8%      35.2%      0.633      51.6%      48.4%      0.499
Woody Wetlands                  190     41,410      75.5%      24.5%      0.737      70.0%      30.0%      0.680
Herbaceous Wetlands             195     12,087      82.2%      17.8%      0.819      84.1%      15.9%      0.838
Triticale                       205         18      22.8%      77.2%      0.228      21.2%      78.8%      0.212
Carrots                         206          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Asparagus                       207          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Cantaloupes                     209          8      36.4%      63.6%      0.364      88.9%      11.1%      0.889
Peppers                         216        118      31.7%      68.3%      0.317      49.2%      50.8%      0.491
Nectarines                      218          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Greens                          219          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Plums                           220          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Strawberries                    221          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Squash                          222         78      26.1%      73.9%      0.261      45.3%      54.7%      0.453
Dbl Crop WinWht/Corn            225        203      19.7%      80.3%      0.197      74.1%      25.9%      0.741
Dbl Crop Oats/Corn              226         14      30.4%      69.6%      0.304      66.7%      33.3%      0.667
Dbl Crop Triticale/Corn         228        655      40.1%      59.9%      0.401      72.0%      28.0%      0.719
Pumpkins                        229         99      27.6%      72.4%      0.276      74.4%      25.6%      0.744
Dbl Crop WinWht/Sorghum         236        323      46.3%      53.7%      0.463      85.2%      14.8%      0.852
Dbl Crop Barley/Corn            237        146      26.7%      73.3%      0.267      56.2%      43.8%      0.561
Dbl Crop Soybeans/Oats          240         43      41.7%      58.3%      0.417      97.7%       2.3%      0.977
Dbl Crop Corn/Soybeans          241          0       n/a        n/a        n/a        0.0%     100.0%      0.000
Blueberries                     242      1,004      86.4%      13.6%      0.864      82.5%      17.5%      0.825
Cabbage                         243          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Radishes                        246          0       0.0%     100.0%      0.000       n/a        n/a        n/a
Turnips                         247          0       0.0%     100.0%      0.000       0.0%     100.0%      0.000
Eggplants                       248          4      20.0%      80.0%      0.200      40.0%      60.0%      0.400
Cranberries                     250          4       9.8%      90.2%      0.098      40.0%      60.0%      0.400
Dbl Crop Barley/Soybeans        254      5,663      73.0%      27.0%      0.728      83.6%      16.4%      0.835

*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. 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. 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 and United States Geological Survey (USGS) National Land Cover Database (NLCD). 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).

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: European Space Agency (ESA)
Publication_Date: 2023
Title: SENTINEL-2
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

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 2023 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: 20221001
Ending_Date: 20231231

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:

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

Publication_Date: 2023
Title:

Landsat 8 and 9 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The Landsat 8 and 9 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: 20221001
Ending_Date: 20231231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat 8 and Landsat 9
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

Publication_Date: 2009
Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

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

Publication_Date: 2021
Title: National Land Cover Database 2019 (NLCD 2019)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

The NLCD 2019 land cover was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2019 Imperviousness layer was used as ancillary data sources in the Cropland Data Layer classification process. The Tree Canopy data was not available with the NLCD 2019, so the NLCD 2016 Tree Canopy data was used as an ancillary input. More information on the NLCD 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.

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)

Publication_Date: 2023
Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Salt Lake City, Utah 84119-2020 USA
Publisher: USDA, FSA Aerial Photography Field Office

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: 2023

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 (pre-2007) 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 Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B 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 data layer from the USGS National Land Cover Database 2019 (NLCD 2019) and the tree canopy data layer from the NLCD 2016. 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 (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 CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2023
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: Maryland
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 6956

Column_Count: 12773

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name:

Albers Conical Equal Area as used by mrlc.gov (NLCD). FOR GEOSPATIAL DATA GATEWAY USERS: Due to technical restrictions, the downloadable CDL data available on the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered as Universal Transverse Mercator (UTM), Spheroid WGS84, Datum WGS84.

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, 2023 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 1-60

 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
          "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
          "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
         "228"       Dbl Crop Triticale/Corn
         "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

2023 Maryland Cropland Data Layer | USDA NASS

Metadata: