No Raster Data Has Been Found

[Cora Auch]

Im at a bit of a loss here. I am attempting to generate a multi-directional hillshade, and the output raster cells seem to have a 'border' around them. I cannot for the life of me work out why and what to do about it.

What happens if you don't resample the DEM. Just reproject it to an appropriate Projected Coordinate System, if necessary. Keep the default cell size. And run the Hillshade function on it. This will ensure no/less loss of the raster values, thus the Hillshade values would be more accurate.

With the 'hillshade' option de-selected above, the issue goes away. With it selected, it appears. With some tweaking of stretching, min/max values and transparency, I've ended up with a pretty good looking result (at least in my opinion!)

I seem to be having a similar issue. I am creating rasters (LAS Point Statistics As Raster, LAS Dataset To Raster, and Hillshade) from a LAS dataset in Pro 2.1. They are also creating these types of "borders" but it seems to related to the sampling value that I choose when creating the rasters. The smaller the value, the bigger the "border" lines are? What is weird is that this issue has only started when I upgraded to Pro 2.1 a few days ago (I have created rasters with the exact same data before and didn't have these "borders").

I need to know what a single point is (i.e. what is x, y and z). If I can understand what is going on, I can find a way to change the data so it is a list of coordinates and that way, I can import it into CAD data.

Another thing I would like to know is how to actually use the data to pick a location. The area I have shown in my example was downloaded from a large area (around 100 square kilometers, if I've understood map scales correctly). I'd like to be able to know what areas, specifically, that the numbers are defining.

Instead, you have a raster, which is roughly a regular grid having one variable/information represented inside each cell (pixel) of the grid. DSMs and DTMs can be derived from LiDAR data, and they represent surfaces, where the information inside each pixel is elevation data (the z values), and the grid is georeferenced, in which every cell of this grid has a x and y coordinate. Here is a GIS post to understand the differences between DSM and DTM.

The first pixel (origin) from your raster, which is the most top (or bottom) left of the grid, have x and y coordinates corresponding to the xllcorner and yllcorner coordinates of the .asc file, hence 350000 and 300000, respectively. These are the coordinates from the lower left corner of the first pixel (if it was the center coordinates, then, the .asc file would carry xllcenter and yllcenter instead). Then, the z value of the first pixel is 118.700.

You can directly import .asc files in almost all GIS software such as ArcGIS, QGIS, R, etc. To know its location open the file together with other vector data as background reference (such as a map from UK, for example). Note, you need to know in which coordinate reference system (CRS) your raster is, in order to get the exact location. If you don't know what it is, this post might help.

For users that don't use Acad verticals or who need pointset inserted into plain vanilla ACAD as point objects to create contours or what ever they need, there are two functions ASC_INN and FILTER_OUT. First is used to read in .asc file and create point objects. To remove unnecessary points user can create a closed polyline and use function FILTER_OUT to remove all points outside bounding polyline. Since in .asc file we have a point set of 250 000 (500 500) points using point objects hugely slows Acad..

I have created this code for my personal use and I'm sharing it to anyone who may need it. I use point sets to create natural terrain for simulation of open pit opening and development regarding terrain configuration and don't use ACAD verticals.

Section 207(f)(2) of the E-Government Act of 2002 requires federal agencies to develop an inventory of information to be published on their Web sites, establish a schedule for publishing information, make those schedules available for public comment, and post the schedules and priorities on the Web site.

Online Links: Other_Citation_Details:NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at . The data is available free for download through CroplandCROS and the Geospatial Data Gateway .What geographic area does the data set cover?West_Bounding_Coordinate: -127.8459

East_Bounding_Coordinate: -67.0096

North_Bounding_Coordinate: 49.3253

South_Bounding_Coordinate: 24.3321

What does it look like?There are over 100 potential land cover categories. The legend above lists only a subset of available categories.Does the data set describe conditions during a particular time period? Beginning_Date: 1997

Ending_Date: annual, ongoing

Currentness_Reference: annual growing season

What is the general form of this data set?Geospatial_Data_Presentation_Form: raster digital data, typically Geotiff (.tif)

How does the data set represent geographic features?How are geographic features stored in the data set?

Indirect_Spatial_Reference: Continental United States

This is a Raster data set.It contains the following raster data types: Dimensions 96523 x 153811, type PixelWhat coordinate system is used to represent geographic features?

The map projection used is (EPSG:5070) Albers Conical Equal Area as used by mrlc.gov (NLCD).

Projection parameters: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 coordinates are encoded using row and column

Abscissae (x-coordinates) are specified to the nearest 30

Ordinates (y-coordinates) are specified to the nearest 30

Planar coordinates are specified in meters

The horizontal datum used is North American Datum of 1983.

The ellipsoid used is Geodetic Reference System 80.

The semi-major axis of the ellipsoid used is 6378137.000000.

The flattening of the ellipsoid used is 1/298.257223563.

How does the data set describe geographic features?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, 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 "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/SoybeansWho produced the data set?Who are the originators of the data set?United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)Who also contributed to the data set?USDA National Agricultural Statistics ServiceTo whom should users address questions about the data?USDA NASS, Spatial Analysis Research SectionAttn: USDA NASS, Spatial Analysis Research Section staff1400 Independence Avenue, SW, Room 5029 South BuildingWashington, District of Columbia 20250-2001

USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS...@usda.govWhy was the data set created?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.How was the data set created?From what previous works were the data drawn?SENTINEL-2 (source 1 of 14)European Space Agency (ESA), 2023, SENTINEL-2: Copernicus - European Commission, European Commission, Brussels (Belgium).Other_Citation_Details:The CDL used Sentinel-2 satellite imagery as one of the inputs from 2017-2023. 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 . The imagery was resampled to 30 meters to match Landsat spatial resolution. The resample used cubic convolution, rigorous transformation. Refer to for specific scene date, path, row and quadrants used as classification inputs for each state and year.Type_of_Source_Media: online download

Source_Scale_Denominator: 10 meter

Source_Contribution: Raw data used in land cover spectral signature analysis

Landsat (source 2 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS), 2023, Landsat TM/ETM/OLI/TIRS: USGS, EROS, Sioux Falls, South Dakota 57198-001.Other_Citation_Details:The CDL has used Landsat satellite imagery as a primary input throughout the entire history of the program from 1997 to current. The Landsat data are free for download through the following website . Additional information about Landsat data can be obtained at . Refer to for specific sensor, scene date, path and rows used as classification inputs for each state and year.Type_of_Source_Media: online download

Source_Scale_Denominator: 30 meter

Source_Contribution: Raw data used in land cover spectral signature analysis

NED (source 3 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2009, The National Elevation Dataset (NED): USGS, EROS Data Center, Sioux Falls, South Dakota 57198 USA.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 . Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.Type_of_Source_Media: online

Source_Scale_Denominator: 30 meter

Source_Contribution:spatial and attribute information used in land cover spectral signature analysisNLCD (source 4 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2021, National Land Cover Database 2019 (NLCD 2019): USGS, EROS Data Center, Sioux Falls, South Dakota 57198 USA.Other_Citation_Details:The most current publicly available NLCD is used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD Imperviousness and Tree Canopy Layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD can be found at . Refer to for the complete list of ancillary data sources used as classification inputs for each state and year.Type_of_Source_Media: online

Source_Scale_Denominator: 30 meter

Source_Contribution: Raw data used in land cover spectral signature analysis

FSA CLU (source 5 of 14)United States Department of Agriculture (USDA) Farm Service Agency (FSA), 2023, USDA, FSA Common Land Unit (CLU): USDA, FSA Aerial Photography Field Office, Salt Lake City, Utah 84119-2020 USA.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 .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverNCCPI (source 6 of 14)United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2012, National Commodity Crop Productivity Index (NCCPI) Version 2.0: United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center, Lincoln, Nebraska USA.Other_Citation_Details:(Michigan only dataset) The NCCPI was used as an ancillary input for the Michigan CDL. The data was resampled to 30 meters for use in CDL production. For more information about the NCCPI: .Type_of_Source_Media: online

Source_Scale_Denominator: 30 meter

Source_Contribution: Ancillary input used in land cover spectral signature analysis

LandIQ (source 7 of 14)California Department of Water Resources (DWR), 2023, Statewide Land Use 2021 (Provisional): California Department of Water Resources (DWR), Sacramento, California 94236-0001 USA.Other_Citation_Details:(California only dataset) The California Department of Water Resources Land Use Program data is used as additional crop-specific ground reference training and validation for tree crops and vineyards in California. More information about California Department of Water Resources Land Use Program can be found online at and .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverLCRAS GIS Data (source 8 of 14)United States Department of Interior, Bureau of Reclamation, Lower Colorado Region, 2023, Lower Colorado River Water Accounting System (LCRAS) GIS data layer: United States Department of Interior, Bureau of Reclamation, Lower Colorado Region, Boulder City, NV 89006-1470, USA.Other_Citation_Details:(Arizona and California only dataset) 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 .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverNASS Citrus Grove Data Layer (source 9 of 14)United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), 2023, USDA NASS Citrus Grove Data Layer: USDA NASS Florida Field Office, Maitland, Florida 32751-7057 USA.Other_Citation_Details:(Florida only dataset) The Citrus Grove Data Layer is used as additional citrus training and validation ground reference data. Access to the USDA National Agricultural Statistics Service (NASS) Citrus Grove Data Layer is unpublished, for internal NASS use only.Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverFSAID (source 10 of 14)Florida Department of Agriculture and Consumer Services, 2023, Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase: Florida Department of Agriculture and Consumer Services, Tallahassee, Florida 32399-0800 USA.Other_Citation_Details:(Florida only dataset) The Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase provides additional training and validation ground reference for Florida specialty tree crops. More information about this data set can be found online at .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverLake Erie Vineyards GIS data (source 11 of 14)Cornell Cooperative Extension, Lake Erie Regional Grape Program, 2023, GIS Mapping of Lake Erie Vineyards: Lake Erie Regional Grape Program at CLEREL - Cornell University, Portland, NY, 14769 USA.Other_Citation_Details:(New York, Ohio and Pennsylvania only dataset) The Lake Erie Vineyards GIS data provides additional training and validation data for vineyards. More information can be found at .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land covernone (source 12 of 14)Gordon B. Jones, PhD, and Rick Hilton of Oregon State University; Karim Naguib of the Jackson County GIS Office, unknown, Pear and Vineyard Data for Jackson County, Oregon: unpublished, Central Point, Oregon 97502 USA.Other_Citation_Details:(Oregon only dataset) The Oregon State University Pear and Vineyard Data for Jackson County, Oregon provides additional tree crop and vineyard training and validation data. Contact Gordon B. Jones at Oregon State University for more information.Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverUtah DWR Agriculture Check Polygons (source 13 of 14)Utah Division of Water Resources, 2023, Agriculture Check Polygons: Utah Division of Water Resources, Salt Lake City, Utah 84116 USA.Other_Citation_Details:(Utah only dataset) The Utah Division of Water Resources Agriculture Check Polygon data provides additional training and validation data for Utah's cropland.Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverWSDA Crop Geodatabase (source 14 of 14)Washington State Department of Agriculture (WSDA), 2023, WSDA Crop Geodatabase: Washington State Department of Agriculture, Olympia, WA 98504-2560 USA.Other_Citation_Details:(Washington only dataset) The WSDA Crop Geodatabase provides additional training and validation data for Washington's orchards, vineyards and small acreage crops. More information about the WSDA Crop Geodatabase can be found at .Type_of_Source_Media: online

Source_Scale_Denominator: 4800

Source_Contribution:spatial and attribute information used in the spectral signature training and validation of agricultural land coverHow were the data generated, processed, and modified?Date: 2023 (process 1 of 1)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 CDL acreage estimates, which most closely aligned with planted acres, are not simple pixel counting but regression estimates using NASS survey data. 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 uses a 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 reference areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground reference from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground reference 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 . The most current version of the NLCD is used as non-agricultural training and validation data.INPUTS: The 2023 CDL has a spatial resolution of 30 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. Some CDL states used additional ancillary inputs to supplement and improve the land cover classification including the United States Geological Survey (USGS) National Elevation Dataset (NED) and the most current versions of the USGS National Land Cover Database imperviousness and the tree canopy data layers. 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 visit the CDL metadata webpages at to view complete lists of imagery, ancillary inputs and training and validation used for a specific state and year.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. Please visit the CDL FAQs and metadata webpages at to view or download full accuracy reports by state and year.PUBLIC RELEASE: The USDA NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is . The data is available free for download through CroplandCROS and the Geospatial Data Gateway . Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.Person who carried out this activity:

USDA NASS, Spatial Analysis Research SectionAttn: USDA NASS, Spatial Analysis Research Section staff1400 Independence Avenue, SW, Room 5029 South BuildingWashington, District of Columbia 20250-2001

USA800-727-9540 (voice)855-493-0447 (FAX)SM.NASS...@usda.govWhat similar or related data should the user be aware of?How reliable are the data; what problems remain in the data set?How well have the observations been checked?

Below are the Overall Accuracy metrics for the crop-specific categories for the Continental United States 2016 to 2023 CDLs. Full accuracy reports for past years, states, and individual crop types are available at the official NASS metadata website noted above.2023 Cropland Data Layer81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.788 KAPPA2022 Cropland Data Layer80.9% OVERALL CROP ACCURACY, 19.1% ERROR, 0.780 KAPPA2021 Cropland Data Layer81.6% OVERALL CROP ACCURACY, 18.4% ERROR, 0.787 KAPPA2020 Cropland Data Layer81.3% OVERALL CROP ACCURACY, 18.7% ERROR, 0.786 KAPPA2019 Cropland Data Layer81.5% OVERALL CROP ACCURACY, 18.5% ERROR, 0.789 KAPPA2018 Cropland Data Layer82.3% OVERALL CROP ACCURACY, 17.7% ERROR, 0.796 KAPPA2017 Cropland Data Layer82.2% OVERALL CROP ACCURACY, 17.8% ERROR, 0.800 KAPPA2016 Cropland Data Layer79.6% OVERALL CROP ACCURACY, 20.4% ERROR, 0.767 KAPPAThe 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 .How accurate are the geographic locations?

The Cropland Data Layer retai