SDE Raster Dataset
Tags
imageryBaseMapsEarthCover, 010, MODIS, MODIS > Moderate-Resolution Imaging Spectroradiometer, Earth Science > Land Surface > Land Use Land Cover > Land Cover, Cropscape, UK-DMC 2, agriculture, cropland, 2011, farming, 001, Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands, Continent > North America > United States of America > North Dakota, AWiFS, crop estimates, North Dakota, environment, 007, Landsat, crop cover, DEIMOS-1, ND, land cover
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2011 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, the Spanish DEIMOS-1 sensor, the British UK-DMC 2 sensor, and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) 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), the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 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.
The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
USDA, National Agricultural Statistics Service
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 <http: nassgeodata.gmu.edu CropScape > or the freeware browser ESRI ArcGIS Explorer <http: www.esri.com >.
NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <http: www.nass.usda.gov research Cropland SARS1a.htm>. The data is available free for download through CropScape at <http: nassgeodata.gmu.edu CropScape >. The data is also available free for download through the Geospatial Data Gateway at <http: datagateway.nrcs.usda.gov >. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.
USDA, National Agricultural Statistics Service
2011 growing season
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 (HQ_RDD_GIB@nass.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 <http: www.nass.usda.gov research Cropland SARS1a.htm>.
None
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 <http: nassgeodata.gmu.edu CropScape > or the freeware browser ESRI ArcGIS Explorer <http: www.esri.com >.
North Dakota
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 2006 (NLCD 2006). More information about the FSA CLU Program can be found at <http: www.fsa.usda.gov >. More information about the NLCD 2006 can be found at <http: www.mrlc.gov >. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.
The entire state is covered by the Cropland Data Layer.
If the following table does not display properly, then please visit this internet site <http: www.nass.usda.gov research Cropland metadata meta.htm> to view the original metadata file. USDA, National Agricultural Statistics Service, 2011 North Dakota Cropland Data Layer STATEWIDE AGRICULTURAL ACCURACY REPORT Crop-specific covers only *Correct Accuracy Error Kappa ------------------------- ------- -------- ------ ----- OVERALL ACCURACY** 5,277,203 73.8% 26.2% 0.690 Cover Attribute *Correct Producer's Omission User's Commission Cond'l Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa ---- ---- ------ -------- ----- ----- -------- ----- ----- Corn 1 623485 90.91% 9.09% 0.905 93.89% 6.11% 0.936 Sorghum 4 1050 22.35% 77.65% 0.223 49.07% 50.93% 0.490 Soybeans 5 1108802 95.74% 4.26% 0.954 93.59% 6.41% 0.930 Sunflower 6 174647 83.79% 16.21% 0.836 88.83% 11.17% 0.887 Barley 21 27625 30.18% 69.82% 0.300 67.57% 32.43% 0.674 Durum Wheat 22 195229 59.37% 40.63% 0.586 73.06% 26.94% 0.724 Spring Wheat 23 2038251 93.36% 6.64% 0.921 87.90% 12.10% 0.857 Winter Wheat 24 152646 80.00% 20.00% 0.798 90.89% 9.11% 0.908 Rye 27 645 20.93% 79.07% 0.209 69.58% 30.42% 0.696 Oats 28 10235 22.44% 77.56% 0.223 57.11% 42.89% 0.570 Millet 29 2678 19.94% 80.06% 0.199 49.90% 50.10% 0.499 Canola 31 196090 89.87% 10.13% 0.897 95.81% 4.19% 0.957 Flaxseed 32 14105 41.46% 58.54% 0.414 79.83% 20.17% 0.798 Safflower 33 459 38.28% 61.72% 0.383 81.53% 18.47% 0.815 Mustard 35 99 35.48% 64.52% 0.355 67.35% 32.65% 0.673 Alfalfa 36 157291 51.51% 48.49% 0.506 61.51% 38.49% 0.607 Non-alfalfa Hay 37 386228 47.35% 52.65% 0.439 43.72% 56.28% 0.404 Camelina 38 0 n a n a n a 0.00% 100.00% 0.000 Sugarbeets 41 20484 78.03% 21.97% 0.780 91.93% 8.07% 0.919 Dry Beans 42 43317 69.75% 30.25% 0.696 78.32% 21.68% 0.782 Potatoes 43 16524 63.26% 36.74% 0.632 86.61% 13.39% 0.866 Other Crops 44 1873 29.68% 70.32% 0.297 74.50% 25.50% 0.745 Onions 49 0 n a n a n a 0.00% 100.00% 0.000 Lentils 52 21522 68.68% 31.32% 0.686 81.69% 18.31% 0.817 Peas 53 19082 58.52% 41.48% 0.585 83.14% 16.86% 0.831 Clover Wildflowers 58 756 24.29% 75.71% 0.243 73.83% 26.17% 0.738 Sod Grass Seed 59 356 39.47% 60.53% 0.395 77.73% 22.27% 0.777 Switchgrass 60 0 0.00% 100.00% 0.000 n a n a n a Fallow Idle Cropland 61 63475 39.01% 60.99% 0.386 65.18% 34.82% 0.648 Triticale 205 88 8.27% 91.73% 0.083 32.59% 67.41% 0.326 Dbl. Crop Corn Soybeans 241 0 0.00% 100.00% 0.000 0.00% 100.00% 0.000 Radishes 246 161 23.50% 76.50% 0.235 90.96% 9.04% 0.910 *NOTE: Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix. **NOTE: The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61,66-80 and 200-255). FSA-sampled grass and pasture, aquaculture, and all NLCD-sampled categories (codes 62-65 and 81-199) 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 2006). 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 <http: www.mrlc.gov >.
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 2006). 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.
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 5 TM and Landsat 7 ETM imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <http: glovis.usgs.gov >. Please reference the metadata on the Glovis website for each Landsat scene for
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 reproduced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.' SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check the 'Process Description' section of the specific state and year metadata file to verify the methodology used. 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 <http: www.fsa.usda.gov >. INPUTS: The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. For the 2011 CDL Program, the AWiFS imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used bilinear interpolation, rigorous transformation. 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), the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. 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 2006 (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 <http: www.nass.usda.gov research Cropland SARS1a.htm>. The data is available free for download through CropScape <http: nassgeodata.gmu.edu CropScape > and the Geospatial Data Gateway <http: 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.
Raw data used in land cover spectral signature analysis
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 <http: 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. For the 2011 CDL Program, the DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
ground condition
Raw data used in land cover spectral signature analysis
The RESOURCESAT-1 (IRS-P6) AWiFS satellite sensor operates in four spectral bands at a spatial resolution of 56 meters. Additional information about AWiFS data can be obtained at <http: www.isro.org >. The AWiFS imagery used in the Cropland Data Layer is obtained through a partnership with the USDA, Foreign Agricultural Service, International Production Assessment (IPA) Program. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs. For the 2011 CDL Program, the AWiFS imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used bilinear interpolation, rigorous transformation.
ground condition
spatial and attribute information used in the spectral signature training and validation of non-agricultural land cover
The NLCD 2006 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2006 Imperviousness and NLCD 2001 Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2006 can be found at <http: 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., 2011. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."
ground condition
spatial and attribute information used in land cover spectral signature analysis
The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. Slope and Aspect derived from the DEM are also used as additional classification inputs. More information on the USGS NED can be found at <http: ned.usgs.gov >. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.
ground condition
NDVI data used in land cover spectral signature analysis
The Moderate Resolution Imaging Spectroradiometer (MODIS), 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) data products from the Terra satellite (MOD13Q1v4) are downloaded from <https: lpdaac.usgs.gov >. Often late-season MODIS NDVI data are used from the previous growing season in an effort to improve winter wheat detection. Refer to the 'Supplemental Information' Section of this metadata file for specific dates used as classification inputs.
ground condition
spatial and attribute information used in the spectral signature training and validation of agricultural land cover
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 <http: www.fsa.usda.gov >.
ground condition, updated annually
Raw data used in land cover spectral signature analysis
The Landsat 5 TM and Landsat 7 ETM+ data is free for download through the following website <http: glovis.usgs.gov >. Additional information about Landsat data can be obtained at <http: eros.usgs.gov >. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.
ground condition
Raw data used in land cover spectral signature analysis
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. For the 2011 CDL Program, the UK-DMC 2 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.
ground condition
Please visit the official website <http: www.nass.usda.gov research Cropland SARS1a.htm> for distribution details. The Cropland Data Layer is available free for download at <http: nassgeodata.gmu.edu CropScape > and <http: datagateway.nrcs.usda.gov >. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
The CDL is available online and free for download from the Cropscape website <http: nassgeodata.gmu.edu CropScape >. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <http: datagateway.nrcs.usda.gov >. IMPORTANT NOTE: When downloading the CDL using the NRCS Geospatial Data Gateway all available years of CDL production for the requested state are included in a single compressed file. Geospatial Data Gateway technical restrictions do not allow us to offer the CDL by individual state year. We are working on offering this option in the future. Instructions for downloading from the NRCS Geospatial Data Gateway: Start by clicking on 'Get Data' Then click on 'Quick State' Scroll down to choose your state and click 'Continue' Choose 'Land_use_land_cover' and select 'Cropland Data Layer by State' and 'Continue to Step3' Choose 'Continue' to Step4 Lastly, you are given the option to download the data for free.
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
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 United States Geological Survey (USGS) National Land Cover Database 2006 (NLCD 2006). 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.
If the following table does not display properly, then please visit the following website to view the original metadata file <http: www.nass.usda.gov research Cropland metadata meta.htm>. Data Dictionary: USDA, National Agricultural Statistics Service, 2011 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 "62" Pasture Grass "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 "171" Grassland Herbaceous "181" Pasture Hay "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 "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
No restrictions on the distribution or use of the metadata file
No restrictions on the distribution or use of the metadata file