20251118 13591700 1.0 FALSE MS_CroplandFC_2024_County 002 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_UTM_Zone_16N <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'><WKT>PROJCS[&quot;WGS_1984_UTM_Zone_16N&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,500000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-87.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,32616]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>1</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>2</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>32616</WKID><LatestWKID>32616</LatestWKID></ProjectedCoordinateSystem> 20251121 08101600 20251121 10552400 150000000 5000 ISO19115_3 Esri ArcGIS 13.5.2.57366 MS Croplands by County 2024 Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079. PDF available at <https://www.nass.usda.gov/Research_and_Science/Cropland/docs/IGARSS2024_Proceedings_10mCDL_Li_etal.pdf>. Data available free for download at <https://croplandcros.scinet.usda.gov/>. Frequently Asked Questions at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. 2024-12-30T00:00:00 2025-02-27T00:00:00 Staff United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) USDA NASS Marketing and Information Services Office, Washington, D.C. <https://croplandcros.scinet.usda.gov/> Steve Walker MARIS GIS Operations Manager 3825 Ridgewood Rd Suite 717 Jackson MS 39211 swalker@mississippi.edu US The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide supplemental acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products. *** November 2025 - MARIS staff converted the .tif from USDA to a polygon feature class. They then created their own shade set to not have non-Mississippi Categories in the Legend. For full USDA metadata, see : https://maris.mississippi.edu//HTML/DATA/data_Geoscience/Cropland.html#gsc.tab=0 or the .htm included in this packet! MARIS staff copied some of the major metadata into this document. *** <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.</SPAN></P><P><SPAN>New for the 2024 10-meter CDL, the crop classification utilized remote sensing data from harmonized Sentinel-2 MSI Level-2A, Landsat 8, and Landsat 9 Level-2 Collection 2 Tier-1 products, providing surface reflectance (SR) data across multiple spectral bands, including GREEN, RED, NIR, SWIR1, SWIR2, and RedEdge bands 1-4. To mitigate cloud cover, 10-day median composites of surface reflectance and NDVI were created from the cloud-masked Landsat-Sentinel multi-sensor data for the growing season of 2024. An impervious layer from USGS NLCD 2021 and a digital elevation model from USGS 3DEP were also included ancillary input variables. In addition, mixed sampling strategies and localized training and were applied to the 2024 10m CDL production. Additional information: Z. Li, R. Mueller, Z. Yang, D. Johnson and P. Willis, "Cloud-Powered Agricultural Mapping: A Revolution Toward 10m Resolution Cropland Data Layers," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 4081-4084, doi: 10.1109/IGARSS53475.2024.10641079. PDF available at &lt;https://www.nass.usda.gov/Research_and_Science/Cropland/docs/IGARSS2024_Proceedings_10mCDL_Li_etal.pdf&gt;.</SPAN></P><P><SPAN>The 2024 CDL has a spatial resolution of 10 meters and was produced using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B collected throughout the growing season. Additional ancillary inputs were used to supplement and improve the land cover classification including the United States Geological Survey (USGS) 3D Elevation Program (3DEP) Elevation Dataset (NED), and the USGS National Land Cover Database imperviousness data layer. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. Some CDL states incorporate additional crop-specific ground reference obtained from the following non-FSA sources which are detailed in the 'Lineage' Section of this metadata: US Bureau of Reclamation, NASS Citrus Data Layer (internal use only), California Department of Water Resources, Florida Department of Agriculture and Consumer Services Office of Agricultural Water Policy, Cornell University grape/vineyard data, Utah Department of Water Resources, and Washington State Department of Agriculture. The 2021 NLCD was used as non-agricultural training and validation data for the 2024 CDL. Please visit the CDL FAQs and metadata webpages at &lt;https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php&gt; to view a complete list of imagery, ancillary inputs, and ground reference used for a specific state and year.</SPAN></P><P><SPAN>** NOTE - In November 2025, ARIS staff converted the .tif from USDA to a polygon feature class. They then created their own shade set to not have non-Mississippi Categories in the Legend. For full metadata from USDA, see their .htm included in this packet! MARIS staff copied some of the major metadata into this document.</SPAN></P></DIV></DIV></DIV> United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), MARIS USDA crops land use Mississippi USDA NASS, Spatial Analysis Research Section staff USDA NASS, Spatial Analysis Research Section 1400 Independence Avenue, SW, Room 5029 South Building Washington District of Columbia 20250-2001 SM.NASS.RDD.GIB@usda.gov US 855-493-0447 File Geodatabase Feature Class File geodatabase 11.5 dataset EPSG 9.8.8(3.0.1) 0 Simple FALSE 0 TRUE FALSE MS_CroplandFC_2024_County Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. FID_MS_CroplandFC_2024 FID_MS_CroplandFC_2024 Integer 4 0 0 Id Id Integer 4 0 0 gridcode gridcode Integer 4 0 0 Class_Name Class_Name String 27 0 0 FID_MS_CountyBoundaries_2015 FID_MS_CountyBoundaries_2015 Integer 4 0 0 AREA AREA Double 8 0 0 PERIMETER PERIMETER Double 8 0 0 NEWSTCO_ NEWSTCO_ Double 8 0 0 NEWSTCO_ID NEWSTCO_ID Double 8 0 0 COUNTY COUNTY SmallInteger 2 0 0 CONAME CONAME String 15 0 0 CO_SEAT CO_SEAT String 40 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. he 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. f the following table does not display properly, then please visit the following website to view the original metadata at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>. Data Dictionary: USDA National Agricultural Statistics Service, 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 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 "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 "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 20251121 Staff United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) USDA NASS Marketing and Information Services Office, Washington, D.C. <https://croplandcros.scinet.usda.gov/> Staff <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The USDA NASS Cropland Data Layer and the data offered on the CroplandCROS website 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.</SPAN></P></DIV></DIV></DIV> If the following table does not display properly, then please visit the CDL Metadata webpage at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original file. Accuracy at the individual state-level can be viewed at the CDL Metadata webpage. USDA National Agricultural Statistics Service, 2024 Cropland Data Layer STATEWIDE AGRICULTURAL ACCURACY REPORT Crop-specific covers only *Correct Accuracy Error Kappa ------------------------- ------- -------- ------ ----- OVERALL ACCURACY** 2,763,127 77.5% 22.5% 0.733 Cover Attribute *Correct Producer's Omission User's Commission Cond'l Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa ---- ---- ------ -------- ----- ----- -------- ----- ----- Corn 1 926,684 92.6% 7.4% 0.923 92.3% 7.7% 0.919 Cotton 2 118,322 85.2% 14.8% 0.852 86.2% 13.8% 0.861 Rice 3 35,276 96.1% 3.9% 0.961 91.3% 8.7% 0.912 Sorghum 4 60,299 79.9% 20.1% 0.799 69.8% 30.2% 0.697 Soybeans 5 830,135 91.2% 8.8% 0.908 91.6% 8.4% 0.912 Sunflower 6 7,181 82.8% 17.2% 0.828 82.8% 17.2% 0.828 Peanuts 10 18,182 90.7% 9.3% 0.906 74.8% 25.2% 0.747 Tobacco 11 329 37.9% 62.1% 0.379 70.3% 29.7% 0.703 Sweet Corn 12 1,389 56.2% 43.8% 0.562 56.6% 43.4% 0.566 Pop or Orn Corn 13 1,298 82.7% 17.3% 0.827 52.3% 47.7% 0.523 Mint 14 402 82.5% 17.5% 0.825 76.9% 23.1% 0.769 Barley 21 16,542 74.3% 25.7% 0.743 65.1% 34.9% 0.650 Durum Wheat 22 16,332 72.0% 28.0% 0.720 62.6% 37.4% 0.625 Spring Wheat 23 110,760 82.9% 17.1% 0.828 86.7% 13.3% 0.866 Winter Wheat 24 204,900 87.1% 12.9% 0.869 81.1% 18.9% 0.809 Other Small Grains 25 90 33.2% 66.8% 0.332 54.5% 45.5% 0.545 Dbl Crop WinWht/Soybeans 26 36,376 78.4% 21.6% 0.783 81.6% 18.4% 0.815 Rye 27 2,895 54.7% 45.3% 0.546 36.1% 63.9% 0.360 Oats 28 6,701 57.3% 42.7% 0.573 36.2% 63.8% 0.362 Millet 29 5,076 64.7% 35.3% 0.647 52.3% 47.7% 0.523 Speltz 30 29 15.4% 84.6% 0.154 30.9% 69.1% 0.309 Canola 31 28,077 93.8% 6.2% 0.938 89.1% 10.9% 0.891 Flaxseed 32 1,003 61.2% 38.8% 0.612 51.4% 48.6% 0.514 Safflower 33 1,363 75.4% 24.6% 0.754 77.2% 22.8% 0.772 Rape Seed 34 25 25.5% 74.5% 0.255 50.0% 50.0% 0.500 Mustard 35 1,657 82.9% 17.1% 0.829 73.3% 26.7% 0.733 Alfalfa 36 88,352 79.6% 20.4% 0.795 60.3% 39.7% 0.601 Other Hay/Non Alfalfa 37 13,256 46.8% 53.2% 0.464 8.3% 91.7% 0.082 Camelina 38 165 44.6% 55.4% 0.446 39.0% 61.0% 0.390 Buckwheat 39 311 57.9% 42.1% 0.579 71.0% 29.0% 0.710 Sugarbeets 41 11,233 95.6% 4.4% 0.956 91.1% 8.9% 0.911 Dry Beans 42 14,668 80.6% 19.4% 0.806 77.0% 23.0% 0.770 Potatoes 43 8,489 89.7% 10.3% 0.897 87.1% 12.9% 0.871 Other Crops 44 731 46.0% 54.0% 0.460 54.3% 45.7% 0.543 Sugarcane 45 13,498 88.5% 11.5% 0.885 90.0% 10.0% 0.900 Sweet Potatoes 46 935 83.8% 16.2% 0.838 77.6% 22.4% 0.776 Misc Vegs & Fruits 47 62 9.8% 90.2% 0.098 24.5% 75.5% 0.245 Watermelons 48 268 45.9% 54.1% 0.459 55.4% 44.6% 0.554 Onions 49 923 70.1% 29.9% 0.701 76.8% 23.2% 0.768 Cucumbers 50 306 56.5% 43.5% 0.565 68.0% 32.0% 0.680 Chick Peas 51 5,280 77.7% 22.3% 0.777 81.7% 18.3% 0.817 Lentils 52 10,012 82.2% 17.8% 0.822 77.6% 22.4% 0.776 Peas 53 10,544 81.8% 18.2% 0.818 73.0% 27.0% 0.730 Tomatoes 54 1,893 78.0% 22.0% 0.780 86.1% 13.9% 0.861 Caneberries 55 62 51.2% 48.8% 0.512 49.2% 50.8% 0.492 Hops 56 594 88.9% 11.1% 0.889 81.5% 18.5% 0.815 Herbs 57 615 53.2% 46.8% 0.532 38.7% 61.3% 0.387 Clover/Wildflowers 58 490 41.7% 58.3% 0.417 34.8% 65.2% 0.348 Sod/Grass Seed 59 4,068 60.1% 39.9% 0.600 49.6% 50.4% 0.496 Switchgrass 60 33 18.8% 81.3% 0.187 30.0% 70.0% 0.300 Fallow/Idle Cropland 61 68,594 84.1% 15.9% 0.840 67.7% 32.3% 0.676 Shrubland 64 18,064 72.3% 27.7% 0.723 49.2% 50.8% 0.492 Cherries 66 805 52.9% 47.1% 0.529 47.0% 53.0% 0.470 Peaches 67 597 45.5% 54.5% 0.455 48.4% 51.6% 0.484 Apples 68 2,715 63.5% 36.5% 0.635 74.8% 25.2% 0.747 Grapes 69 10,441 71.9% 28.1% 0.719 76.3% 23.7% 0.763 Christmas Trees 70 68 9.7% 90.3% 0.097 14.3% 85.7% 0.143 Other Tree Crops 71 275 30.8% 69.2% 0.308 44.0% 56.0% 0.440 Citrus 72 3,974 58.5% 41.5% 0.585 75.1% 24.9% 0.751 Pecans 74 2,706 76.6% 23.4% 0.766 49.9% 50.1% 0.499 Almonds 75 23,975 86.3% 13.7% 0.863 86.5% 13.5% 0.865 Walnuts 76 5,826 88.7% 11.3% 0.887 72.3% 27.7% 0.723 Pears 77 293 56.0% 44.0% 0.560 52.6% 47.4% 0.526 Aquaculture 92 4,555 85.3% 14.7% 0.853 80.7% 19.3% 0.807 Pistachios 204 8,607 88.9% 11.1% 0.889 85.7% 14.3% 0.857 Triticale 205 2,559 46.5% 53.5% 0.465 25.7% 74.3% 0.257 Carrots 206 147 54.6% 45.4% 0.546 56.5% 43.5% 0.565 Asparagus 207 17 34.0% 66.0% 0.340 54.8% 45.2% 0.548 Garlic 208 144 66.4% 33.6% 0.664 76.6% 23.4% 0.766 Cantaloupes 209 57 34.8% 65.2% 0.348 60.0% 40.0% 0.600 Prunes 210 245 54.1% 45.9% 0.541 36.5% 63.5% 0.365 Olives 211 479 73.6% 26.4% 0.736 45.7% 54.3% 0.457 Oranges 212 3,617 61.0% 39.0% 0.610 58.5% 41.5% 0.585 Honeydew Melons 213 2 10.5% 89.5% 0.105 11.1% 88.9% 0.111 Broccoli 214 61 36.7% 63.3% 0.367 47.7% 52.3% 0.477 Avocados 215 353 69.9% 30.1% 0.699 48.9% 51.1% 0.489 Peppers 216 108 38.3% 61.7% 0.383 54.5% 45.5% 0.545 Pomegranates 217 206 83.7% 16.3% 0.837 51.2% 48.8% 0.512 Nectarines 218 1 5.3% 94.7% 0.053 14.3% 85.7% 0.143 Greens 219 63 39.1% 60.9% 0.391 40.9% 59.1% 0.409 Plums 220 20 14.6% 85.4% 0.146 5.7% 94.3% 0.057 Strawberries 221 26 15.7% 84.3% 0.157 45.6% 54.4% 0.456 Squash 222 51 24.4% 75.6% 0.244 45.1% 54.9% 0.451 Apricots 223 5 7.5% 92.5% 0.075 6.2% 93.8% 0.062 Vetch 224 64 57.7% 42.3% 0.577 59.8% 40.2% 0.598 Dbl Crop WinWht/Corn 225 1,796 46.1% 53.9% 0.460 48.5% 51.5% 0.485 Dbl Crop Oats/Corn 226 315 47.9% 52.1% 0.479 48.8% 51.2% 0.488 Lettuce 227 97 41.6% 58.4% 0.416 27.1% 72.9% 0.271 Dbl Crop Triticale/Corn 228 1,841 47.0% 53.0% 0.469 61.8% 38.2% 0.618 Pumpkins 229 226 36.3% 63.7% 0.363 57.1% 42.9% 0.571 Dbl Crop Lettuce/Durum Wht 230 0 0.0% 100.0% 0.000 n/a n/a n/a Dbl Crop Lettuce/Cantaloupe 231 56 48.7% 51.3% 0.487 90.3% 9.7% 0.903 Dbl Crop Lettuce/Cotton 232 97 68.3% 31.7% 0.683 84.3% 15.7% 0.843 Dbl Crop Lettuce/Barley 233 1 20.0% 80.0% 0.200 25.0% 75.0% 0.250 Dbl Crop WinWht/Sorghum 236 1,725 56.4% 43.6% 0.564 33.3% 66.7% 0.333 Dbl Crop Barley/Corn 237 187 39.0% 61.0% 0.390 57.9% 42.1% 0.579 Dbl Crop WinWht/Cotton 238 407 32.9% 67.1% 0.329 18.2% 81.8% 0.182 Dbl Crop Soybeans/Cotton 239 0 0.0% 100.0% 0.000 n/a n/a n/a Dbl Crop Soybeans/Oats 240 160 29.5% 70.5% 0.295 37.0% 63.0% 0.370 Dbl Crop Corn/Soybeans 241 31 32.6% 67.4% 0.326 48.4% 51.6% 0.484 Blueberries 242 368 45.4% 54.6% 0.454 39.1% 60.9% 0.391 Cabbage 243 90 49.2% 50.8% 0.492 46.9% 53.1% 0.469 Cauliflower 244 10 28.6% 71.4% 0.286 20.4% 79.6% 0.204 Celery 245 6 14.3% 85.7% 0.143 25.0% 75.0% 0.250 Radishes 246 28 46.7% 53.3% 0.467 50.9% 49.1% 0.509 Turnips 247 19 38.8% 61.2% 0.388 48.7% 51.3% 0.487 Eggplants 248 1 10.0% 90.0% 0.100 50.0% 50.0% 0.500 Gourds 249 3 17.6% 82.4% 0.176 60.0% 40.0% 0.600 Cranberries 250 18 17.6% 82.4% 0.176 69.2% 30.8% 0.692 Dbl Crop Barley/Soybeans 254 433 47.5% 52.5% 0.475 63.0% 37.0% 0.630 *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/>.