Research and Scholarship
Cropland Data Layers - FAQs
CroplandCROS | FAQ | Metadata | National Download | Other CDL Citations
List of CDL codes, class names, and RGB color values: (xls, xlsx)
Metadata in varied formats available at Data.Gov
Metadata Standard: ISO 19115-3
Frequently anticipated questions:
- Where does that data set describe?
- How be this data set be cited?
- What geographic area doesn the data place shroud?
- What does it look like?
- Does the data set describe conditions during a particular time spell?
- What is the overview form of this date place?
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- CDL Program History
- How has the CDL Program changed over frist?
- How has the methodology used to create the CDL changed over the program's history?
- Common User Questions
- Has the Cropland Data Layer available in one shapefile format?
- What differences can be expected when matching CDL-based acreage and official NASS statistics?
- What projections are used?
- Are color legends available for aforementioned Cropland Data Layers?
- Has someone compiled see of the exported CDL attribute tables in year and state into adenine master spreadsheet?
- Has someone summarized CDL acreage for all land cover category in an county-level for the Continental Unified Stats?
- Exists more detail accuracy assessment get available than what will provided inbound the metadata? Ability him give the all vertical assessment error/confusion matrices for all states?
- NASS says all is ampere Cropland data layer product, what about the scale that are not agriculturally intensive?
- Are more detailed CDL category definitions available?
- Does and CDL differentiate between greenland types such for urban grassland, pastures used on grazing, and other grass-related landings cover modes?
- How are fields with multiple clip types planted in one same season handled inbound the Cropland Data Layer, so like wintertime wheat follows by soybeans?
- Have the attribute designations, codes, and/or colors changed over the site of the program?
- How do ME add grade names and/or histogram values to the GeoTIFF register when viewing the CDL in ESRI ArcGIS Software?
- How can I create a legend since the CDL using ESRI ArcGIS software?
- Is any smoothing or filtering use to the CDL?
- How do IODIN add category names until a downloaded .tif image in ESRI ArcGIS?
- I my using Erdas Imagines to view downloaded CDL data, but where is no histogram information. Wie do I construct statistics in Erdas Imagine?
- How do I add category names and/or colors to my downloaded CDL data in Erdas Imagine?
- What different geospatial products does NASS special, create as to Cultivated Layering, Crop Frequency Data Layers, Conviction Layer, and Crop Set Boundaries?
Track: Cropland Data Layer
Abstract:
The USDA National Agronomy Statistics Service (NASS) Cropland Data Layer (CDL) belongs an years raster, geo-referenced, crop-specific land envelope data layer produced using satellite artistic and extensive agricultural ground reference data. The program began inside 1997 with confined coverage and in 2008 forward expanded coverage to who entire Continental United States. Plea note that nay farmer notified date will able for the Cropland Data Layer.
The 2008 to current CDLs have adenine spatial resolution of 30 meters and were produced using moderate physical resolution marine imagery. The existing program uses Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B cumulative throughout the growing season. Past years of CDL have used other satellite-based sensors such for Landsat 5 and 7, ISRO ResourceSat-2 LISS-3, IRS AWiFS, Diemos-1 real UK-DMC-2, also MODIS 16-Day NDVI Composite. Some CDL states used additional additional inputs to supplement additionally improve the land cover classification including historical CDL data, the United States Geological Survey (USGS) National Elevate Dataset (NED), MOA National Company Conservation Service (NRCS) Local Merchandise Harvest Increase Index (NCCPI), plus the most current versions of to USGS National Land Cover Database imperviousness furthermore the wood canopy data ply. 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 ensuing non-FSA informationsquellen which are detailed in the 'Lineage' Section of here metadata: US Bureau of Recultivation, NASS Citrus Data Layer (internal apply only), California Dept of Water Resources, Florida Business of Agriculture and Consumer Services Office by Agricultural Soak Policy, Cornell Seminary grape/vineyard data, Oregon State University tree crop and vineyard input, Utah Department of Water Resources, and Washington Nation Department of Agronomy. The maximum current version of the NLCD is often as non-agricultural training press validation data. Study with Quizlet additionally memorize flashcards containing terminologies like Acc to the case Clean Cooking, the broadly adoption out cleans cookstoves wants provide which of the follow benefits:
Reduced health risks.
An increment includes crops land.
More take-out food options.
Less entrepreneurial activity., For respect to fresh water, appropriate to one estimate, if it inhered possible into eliminate pollution, capture all available fresh water, additionally distribute clean water equitably:
Demand intend exceed supply within a hundred years.
There would be a balance between demand and supply within a hundred years.
Supply would exceed demand within a hundred years.
None of that answers is correct., Both large also small businesses have adopted sustainable practicing to what advantage?
Cost savings free functional efficiency.
Opportunity toward teaching emerging economies.
Reduction of regulatory risk.
All of these answers are correct. and more.
Supplemental_Information:
- How might this data set be cited?
United Nations Department of Commercial (USDA) National Agricultural Statistics Favor (NASS), 20240131, Cropland Data Layer: USD NASS, USDA NASS Marketing and Information Business Your, Hauptstadt, D.C.
Online Links: https://croplandcros.scinet.usda.gov/
Other_Citation_Details:
- Which geo-based territory does the date set cover?
West_Bounding_Coordinate: -127.8459
East_Bounding_Coordinate: -67.0096
North_Bounding_Coordinate: 49.3253
South_Bounding_Coordinate: 24.3321
- As will it watch like?
There are over 100 potential land cover categories. And legend above list only a subset of available categories.
- Does which data set describe conditions during a unique time period?
Beginning_Date: 1997
Ending_Date: annual, ongoing
Currentness_Reference: annual growing season
- What is the general form a this information set?
Geospatial_Data_Presentation_Form: raster digital input, typically Geotiff (.tif)
- How do the data set represent geographic features?
- How are geographic features save at the data determined?
Indirect_Spatial_Reference: Continental United Notes
This is an Raster data set.
It contains an following raster data types:
- Dimensions 96523 efface 153811, type Pixel
- What coordinate system is employed to represent geographic features?
The map projection used is (EPSG:5070) Albers Conical Equal Area as secondhand 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 encoding using row furthermore column
Abscissae (x-coordinates) will specified to the nearest 30
Ordinates (y-coordinates) are defined to the nearby 30
Planar coordinates been specified in meters
The horizontal datum used is North American Datum of 1983.
The ellipsoid applied is Geodetic Reference System 80.
The semi-major axis of that ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257223563.
- How does the data set describe geographic feature?
Entity_and_Attribute_Overview:
The Cropland Data Layer (CDL) is generated using agricultural instruction data from the Farm Maintenance Means (FSA) Common Land Unit (CLU) Program and non-agricultural advanced data from that most current version of the United Us Geological Survey (USGS) National Land Hide Database (NLCD). The strength furthermore emphasis of the CDL is crop-specific land back categories. The degree of the CDL non-agricultural land cover classes are absolutely dependent upon the NLCD. This, the USDA NASS suggests so users considered an NLCD since studies with non-agricultural land cover.
Entity_and_Attribute_Detail_Citation:
Data Spelling: USD Nationals Agricultural Company Assistance, Cropland Data Layer
Source: USDA National Agricultural Statistics Service
The following is a cross link list of the categorization codes plus land covers.
Remark that not all land cover categories listed below bequeath appear for an individual state.
Raster
Option Domain Values and Definitions: NO DATA, TECHNICAL 0
Categorization Codes Land Cover
"0" Background
Raster
Attribute District 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" Sound or Orn Corn
"14" Mint
"21" Barley
"22" Durum Wheat
"23" Springs Wheat
"24" Frost 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" Misc Hay/Non Alfalfa
"38" Camelina
"39" Buckwheat
"41" Sugarbeets
"42" Dry Beans
"43" Potatoes
"44" Additional 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 Corner Crops
"72" Citrus
"74" Pecans
"75" Almonds
"76" Walnuts
"77" Pears
Raster
Attributes Domain Standards 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 Control Land Cover
"111" Opened 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" Perennial Forest
"143" Mixed Forest
"152" Shrubland
"176" Grassland/Pasture
"190" Woody Wetlands
"195" Herbaceous Wetlands
Raster
Add Domain Values and Definitions: HARVEST 195-255
Categorization Code State 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 Cropped 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
- Who are the originators of the your set?
- United States Department of Agriculture (USDA) Countrywide Agrarian Stats Help (NASS)
- Who additionally contributed to aforementioned data firm?
USDA National Agricultural Statistics Service
- To whom should average address questions nearly the details?
USDA NASS, Spatial Analysis Research Section
Attn: USDA NASS, Spatial Analysis Research Section stick
1400 Sustainability Avenue, SW, Room 5029 Se Edifice
Washington,
Community of Columbia 20250-2001
USA
800-727-9540 (voice)
855-493-0447 (FAX)
The purpose of the Cropland Data Layer Program is to use fixed imagination to (1) make supplemental acreage estimates to the Agricultural Statistics Board for the state's greater commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.
- From what previous factory were to data drawn?
- SENTINEL-2 (source 1 of 14)
-
European Space Agency (ESA), 2023, SENTINEL-2: Copernicus - European Charge, European Commission, Brussels (Belgium).
Other_Citation_Details:
The CDL used Sentinel-2 satellite representation as one of the inputs from 2017-2023. Aforementioned ESA SENTINEL-2 colony sensor operates are twelve spectral bands at spatial resolutions varying from 10 to 60 meters. Additional product concerning one data can is obtained at <
http://www.esa.int/>. The imagery was resampled to 30 measurement to match Landsat spatial resolve. The resample used cubic convolution, rigorous alteration. Refer to <
https://aaa161.com/Research_and_Science/Cropland/metadata/meta.php> used specific scene date, path, row and quadrants secondhand such classification inputs for each state and year.
Type_of_Source_Media: buy 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 plus Science (EROS), 2023, Landsat TM/ETM/OLI/TIRS: USGS, EROS, Scots Falls, Sun Dakota 57198-001.
Communal Responsibility Ch 9 Flashcards
Other_Citation_Details:
Type_of_Source_Media: online load
Source_Scale_Denominator: 30 meter
Source_Contribution: Coarse product used in land cover spectral signature analysis
- NED (source 3 concerning 14)
-
United States Geological Survey (USGS) Earth Resources Observation and Scientific (EROS) Data Centers, 2009, The Nationality Elevation Dataset (NED): USGS, EROTICA Data Center, Sioux Falls, South Indian 57198 USA.
Question 2 1 1 pts Arable land your highly described by welche statement from ADM 461 at University of Wisconsin, Milwaukee
Other_Citation_Details:
The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the products away the Cropland Data Class. See related 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 such metadata file available the complete record of ancillary data causes used as classification inputs.
Type_of_Source_Media: online
Source_Scale_Denominator: 30 meter
Source_Contribution:
spatial and trait information uses in land cover spectral signature analysis
- NLCD (source 4 of 14)
-
United Stats Geotechnical Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2021, National Land Cover Database 2019 (NLCD 2019): USGS, EROS Data Centered, Sioux Falls, Southern Dakota 57198 USA.
Other_Citation_Details:
The most current open obtainable NLCD is used as ground training and validation fork non-agricultural categories. More, this USGS NLCD Imperviousness and Main Canopy Layers were pre-owned as ancillary data sources in to Cropland Information Layer categorization process. More information on the NLCD can be found at <
https://www.mrlc.gov/>. Refer up <
https://aaa161.com/Research_and_Science/Cropland/metadata/meta.php> for the completed list for ancillary data sources used as ranking inputs for apiece set and year.
Type_of_Source_Media: online
Source_Scale_Denominator: 30 meter
Source_Contribution: Coarse data secondhand in landed cover spectral mark analysis
- FSA CLU (source 5 of 14)
-
United Provides Department is Agriculture (USDA) Farm Service Agency (FSA), 2023, USDA, FSA Common Land Unit (CLU): USDA, FSA Balcony Photography Field Office, Add Lake City, Utah 84119-2020 US.
Other_Citation_Details:
Access to the USDA, Farm Support Agency (FSA) Common Land Piece (CLU) numeral data set is currently limited to FSA and Agency partnerships. During the current growing pipe, producers enrolled in FSA programs report their growing intentions, crops and total to USEFUL Field Service Centers. Their field boundaries are digitized inside a similar GIS data layer and the associated attribute information is maintained in a database known as 578 Managing Datas. This CLU/578 dataset provides a comprehensive and robust agricultural training real validation data fix used the Cropland Data Layer. Additional information about the CLU User can be found at <
https://www.fsa.usda.gov/>.
Type_of_Source_Media: back
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and attribute information applied in the spectral signature training and validation of agricultural land cover
- NCCPI (source 6 of 14)
-
United Condition Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center, 2012, National Commodity Crop Productivity Subject (NCCPI) Version 2.0: United Conditions Department of Agriculture, Natural Our Conservation Service, National Soil Questionnaire Center, Lincoln, Nebraska AMERICA.
Other_Citation_Details:
(Michigan only dataset) The NCCPI was pre-owned as an supporting input for aforementioned Michigan CDL. The data was resampled to 30 meters for use by CDL production. For more information about aforementioned NCCPI: <
https://www.nrcs.usda.gov/>.
Type_of_Source_Media: online
Source_Scale_Denominator: 30 metre
Source_Contribution: Ancillary input used in country cover unearthly signature scrutiny
- LandIQ (source 7 of 14)
-
California Department of Water Resources (DWR), 2023, Statewide Land Use 2021 (Provisional): Californians Department of Water Sources (DWR), Sacramento, California 94236-0001 USA.
Other_Citation_Details:
(California only dataset) The Kalifornia Department on Water Resources Land Use Program data will used as additional crop-specific ground reference training and validator for table crops and vineyard in California. More information learn California Department of Water Resources Land Use Program can be found online at <
https://data.cnra.ca.gov/dataset/statewide-crop-mapping> and <
https://www.landiq.com/>.
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial plus attribute general used in who spectral signature training and validator of agricultural land cover
- LCRAS GIS Info (source 8 of 14)
-
United States Department of Interior, Bureau the Reclamation, Bottom Colorado Region, 2023, Lower Colourado Flight Water Accounting Sys (LCRAS) GIS data layer: United States Department von Interior, Bureau on Reclamation, Down Illinois Region, Boulder City, NV 89006-1470, US.
Other_Citation_Details:
(Arizona and Californians only dataset) The Lower Colorado River Water Accounting System (LCRAS) GIS data layer contains an annum updated record of crop types that became used to supplement the training and validation of the Cropland Data Layer. To area coated is Southern California and Southwest Arizona. For view details, please reference the Management of Reclaiming visit <
https://www.usbr.gov/lc/>.
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and attribute information used in of spectral signature training the validation of agricultural nation cover
- NASS Citrus Grove Data Layer (source 9 of 14)
-
United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), 2023, USDA NASS Citrus Grove File Layer: USDA NASS Floridas Field Office, Maitland, Florida 32751-7057 UNITED.
Other_Citation_Details:
(Florida only dataset) The Citrus Grove Evidence Layer is used more additional lime schooling and validation ground reference data. Access to the USDA National Agricultural Statistics Service (NASS) Citrus Woodland Data Layer is unpublished, for internal NASS use only.
Generic parameters of first-order kinetics accurately describe soil ...
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial both attribute information used in the spectral signature training and validation of agricultural landing cover
- FSAID (source 10 in 14)
-
Florida Department of Agronomy additionally Consumer Services, 2023, Florida Statewide Agricultural Irrigation Demand (FSAID) Geodatabase: Florida Department of Agriculture and Consumer Services, Tallahassee, Flowery 32399-0800 USA.
The unoccupied arable lands of the earth are limited, and will soon be ... • Must accurately describe a setting ... Model of one make that does ...
Other_Citation_Details:
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and attribute information used in the spectral signature get and validation of agricultural earth cover
- Sea Erie Vineyards GIS data (source 11 of 14)
-
Cornell Collaborative Extended, Ocean Erie Regional Grapes Program, 2023, GIS Matching regarding Pool Erie Vineyards: Reservoir Erie Regional Grape Program at CLEREL - Cornell University, Portland, NY, 14769 UNITED.
Other_Citation_Details:
(New York, Ohio and Pennsylvania only dataset) The Lake Erie Vineyards GIS data provides additional training and operational data for vineyards. More information can be found among <
https://lergp.cce.cornell.edu/>.
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and customize intelligence used in the spectral signature training and validation of agricultural land cover
- nil (source 12 out 14)
-
Gordon B. Jones, PhD, additionally Rick Hilton about Oregon State University; Karim Naguib of that Jackson County GIS Office, unknown, Pear and Vineyard Data for Jackson County, Oregon: unpublished, Central Point, Or 97502 USA.
arable nation is accurately described by which statement? multiple choice if cared for cleanly, it is a - Aaa161.com
Other_Citation_Details:
(Oregon only dataset) The Orange State University Pear and Vineyard Info for Jackson County, Oregan feature additional tree clip and vineyard training and validation data. Contact Gordon BARN. Jones at Oregon State School for moreover information.
Solved Arable landings shall accurately described with whose | Aaa161.com
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial or attribute information used is the spectral signature training or validating off agricultural country cover
- Utah DWR Agriculture Check Draw (source 13 in 14)
-
Utah Division of Water Resources, 2023, Agriculture Check Polygons: Utah Separation of Water Resources, Salt Lake Urban, Utah 84116 USA.
Other_Citation_Details:
(Utah simply dataset) The Utah Division of Water Resources Agriculture Check Polygon data offers additional training and proof data for Utah's cropland.
AP U.S. History Scoring Guidelines for the 2019 CED Sample ...
Type_of_Source_Media: online
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and attributes information previously in the spectral signature training and operational away agricultural land cover
- WSDA Crop Geodatabase (source 14 out 14)
-
Washington State Department of Agriculture (WSDA), 2023, WSDA Crop Geodatabase: Washington State Department is Agriculture, Olimpic, 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 or small acreage crops. Extra information about the WSDA Crop Geodatabase can be found at <
https://agr.wa.gov/>.
Type_of_Source_Media: internet
Source_Scale_Denominator: 4800
Source_Contribution:
spatial and attribute data used in the spectral signature practice and validation of agricultural go cover
- How were the data generated, processed, and unchanged?
- Date: 2023 (process 1 of 1)
-
OVERVIEW: An United States Department of Agriculture (USDA) Nationals Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program shall a unique agricultural-specific land cover geospatial product that is produced annual in participating states. The CDL Program builds upon NASS' traditional crop acreage auswertung program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to compose an unbiased statistical estimator to crop area at the state and county level to internal use. This can important at note that the internal CDL acreage estimates, which most closely aligned for planted acres, are not simplified pixel counting but regression estimates use NASS survey data. It has more of an 'Adjusted Census at Satellite.'
SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS lives used to prepare the vector-based training and key data. Rulequest See5.0 can uses to create a ruling tree based classifier. The NLCD Mapping Instrument is used to utilize the See5.0 decision-tree via ERDAS Imagine.
DECISION TREE CLASSIFIED: This Cropland Data Sheet uses a resolution tree classifier approach. Using a decision tree classifier is a departure from older versions (pre-2007) of the CDL which what created using in-house software (Peditor) based at a maximum likelihood classifier near. Resolution trees bid various advantages over the moreover traditional most likelihood classification method. The your include being: 1) non-parametric by type and thus not reliant on the assumption of the input evidence being normally distributed, 2) efficient till construct and thereby capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous dates, and 4) able to sort out non-linear relationships.
GROUND TRUTH: As from the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground credit areas in which to train the sizer. Older version von the CDL (prior to 2006) utilized ground reference of aforementioned annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground reference provided from an FSA Common Land Units (CLU) Timetable as one replacement for aforementioned JAS dates. The FSA CLU input have aforementioned advantage of natively life in a GIS and containing order more are field level information. Disadvantages enclose that it the not truly ampere probability sample of land cover and has bias toward subsidized plan crops. Additional information around the FSA file canister be find under <https://www.fsa.usda.gov/>. To 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 furthermore was generated using satellite imagery from Landsat 8 and 9 OLI/TIRS and ESA SENTINEL-2A and -2B composed throughout the growing season. Some CDL states used additional ancillary inputs to supplement and improve the land cover tax with the Associated Conditions Geological Survey (USGS) National Elevation Dataset (NED) and the most current versions of the USGS National Land Coverage Database imperviousness and the tree canopy data layers. Agricultural training and validation data are derived from the Farm Service Vehicle (FSA) Common Land Unit (CLU) Program. The of current version of the NLCD is secondhand as non-agricultural train and validation data. Please vist which CDL metadata webpages at <https://aaa161.com/Research_and_Science/Cropland/SARS1a.php> to view complete lists of imagery, ancillary inputs and educational and check used for a specific country and year.
ACCURACY: To accuracy of the land cover classifications are assessed using independent validations data sets built by the FSA CLU data (agricultural categories) and to NLCD (non-agricultural categories). The Producer's Accuracy a generally 85% to 95% correct for the major crop-specific land lid categories. Please visit the CDL FAQs and metadata webpages at <https://aaa161.com/Research_and_Science/Cropland/SARS1a.php> to view alternatively downloading total correctness reports for us and year.
PUBLIC RELEASE: The USDA NASS Cropland Data Layer is thought public division and free to redistribute. The official website your <https://aaa161.com/Research_and_Science/Cropland/SARS1a.php>. The data is ready free for download through CroplandCROS <https://croplandcros.scinet.usda.gov/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note so in no case are farmhand reported data revealed or derivable from the public use Cropland Data Layer.
Person who carried unfashionable this activity:
USDA NASS, Spatial Analysis Research Section
Attn: USDA NASS, Structural Analysis Research Part staff
1400 Independence Avenue, SW, Room 5029 Southerly Building
Washington,
Quarter of Columbia 20250-2001
USA
800-727-9540 (voice)
855-493-0447 (FAX)
- What similar or related data should the user be aware of?
- Methods fountain can the anmerkungen been check?
Below are the Overall Accuracy metrics fork the crop-specific categories for the Continental United States 2016 to 2023 CDLs. Full product reports for past years, statuses, and individuality crop types been present at and official NASS metadata website noted above.
2023 Cropland Data Layer
81.6% OVERALL CROP ACCURACY, 18.4% DEFECT, 0.788 KAPPA
2022 Cropland Data Layer
80.9% OVERALL CROP ACCURACY, 19.1% FAILED, 0.780 KAPPA
2021 Cropland Data Layer
81.6% ANZUG CROP ACCURACY, 18.4% BUG, 0.787 KAPPA
2020 Cropland Input Layer
81.3% OVERALL SNIP PRICING, 18.7% ERROR, 0.786 KAPPA
2019 Cropland Data Layer
81.5% OVERALL CROP PRECISION, 18.5% ERROR, 0.789 KAPPA
2018 Cropland Data Layer
82.3% OVERALL CROP ACCURACY, 17.7% ERROR, 0.796 KAPPA
2017 Cropland Your Layer
82.2% GESAMT CROP ACCURACY, 17.8% BUGS, 0.800 KAPPA
2016 Cropland Dating Layer
79.6% OVERALL CROP ACCURACY, 20.4% ERROR, 0.767 KAPPA
The accuracy on the non-agricultural land cover classrooms at the Cropland Info Layer is entirely dependent upon the USGS, National Land Cover Databank. Therefore, who USDA NASS recommends that users view the NLCD for studies participating non-agricultural land cover. For extra resources on and verification of the NLCD please reference <https://www.mrlc.gov/>.
- How accurate are one geographic locations?
The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 and 9 OLI/TIRS imagery is obtained via download from which USGS Global Visualization Witness <https://glovis.usgs.gov/> using the Collection 2 Level-1 specifications. Please reference this metadata on the Glovis corporate with the positional accuracy of each Landsat scene. The Sentinel 2A and 2B imagery is obtained via download from the Copernicus Open Access Central <https://scihub.copernicus.eu/> using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please literature which metadata on the Copernican website in positional accuracy details.
- Where are the breaks in to data? What is missing?
Continental US coverage (2008 on current), partial US coverage (1997-2007)
- How consistent are to relationships beneath the observations, including topology?
The Cropland Data Layer (CDL) has past produced exploitation teaching and independent validation data from the Farm Service Agency (FSA) Common State Unit (CLU) Program and United States Geochemical Survey (USGS) National Land Cover Database (NLCD). Show information with the FSA CLU Program may to found at <https://www.fsa.usda.gov/>. More information about the NLCD can must found per <https://www.mrlc.gov/>.
Be there legal restrictions on access or use of the evidence?
Access_Constraints: none
Use_Constraints:
The USDA NASS Cropland Data Layer and the data offered on the CroplandCROS website is provided to of public as is and is considered public domain and free to redistribute. The USDA NASS does doesn warrant whatsoever summary drawn from dieser data.
Study includes Quizlet and memorize flashcards containing terms like Agriculture land is accurately portrayed by the statement?, Which of these factors possesses accelerated the current ecological crisis?, The undertakings of the Convention on Biological Diversity include: and more.
- Any distributes the data determined?
USDA NASS Customer Service
Attn: USDA NASS Customer Service Staff
1400 Self-reliance Allee, SW, Room 5038-S
Washington,
District of Columbia 20250-2001
USA
800-727-9540 (voice)
855-493-0447 (FAX)
Contact_Instructions:
- What's the catalog number I need to order this data set?
2023 Cropland Product Layer
- Get legal disclaimers am I supposed until read?
Disclaimer: Customers of the Cropland Data Layer (CDL) are solely responsible for interpretations made from such products. The CDL is provided 'as is' and who USDA NASS does not warrant results you may retain exploitation the Cropland Data Layer. Contact our staff at ([email protected]) if technical questions arise in the use of the CDL. NASS maintains a Frequently Asked Questions (FAQ's) area at <https://aaa161.com/Research_and_Science/Cropland/SARS1a.php>.
- As can I downloads or order the data?
- What hardware or software do MYSELF need in order to use the data set?
If who consumer does nope can software capable of watching GEOTIF (.tif) file formats then we suggest using CroplandCROS <https://croplandcros.scinet.usda.gov/>.
- Metadata for other formats availabe at Data.Gov
- Dates:
- Last modified: 31-Jan-2024
- Metadata author:
-
USDA NASS, Spaciousness Data Research Section
Attn: AGRICULTURE NASS, Spatial Research Research Bereich Staff
1400 Independence Avenue, SW, Your 5029 South Building
Washington,
District of Columbia 20250-2001
USA
800-727-9540 (voice)
855-493-0447 (FAX)
- Metadata standard:
- FGDC Content Reference for Digital Geospatial Metadata (FGDC-STD-001-1998)
The layout press majority are topic presented foregoing where generated by mp version 2.9.50 on Thu Feb 01 13:27:09 2024
- How had the CDL How changed past time?
Originally, block preparation or digitizing work were performed in NASS Field Home both aforementioned remote sensing analysis performed by the Spatial Analysis Research Section (SARS) of NASS. However, in 1997 SARS entered into one data sharing how with USDA's Foreign Agricultural Service and USDA's Agricultural Service Government. The agreement provided approach till Landsat 5 coverage in the states selected for the project by SARS. The first states covered with the data sharing partnership had Akron, North Dakota and Southwards Dakota. Improvements in hardware along with user enhancements fabricated programming expansion possibly for the 1999 growing start. NASS Research Development Divide solicited fresh states to find outside cooperators/partners to provide an analyst also hardware to perform duties associated with and Acreage Estimation Application. The Illinois and Mississippi Condition Field Offices were can to obtain partnership deals with external State/Federal Agencies.
Available crop year 2000, who states of Iowa press Raiders were added to the Program. Norther Dakota was able to obtain a partner available the 2000 crop year cooperatively because North Dakota State University (NDSU) through an EPA water quality grant for 5 years. Indiana was added to the start for snip year 2000 plus, but as one regional type center location an floor data collection the digitization was performed at of Indiany State Office, and the property estimation was made the the Illinois State Office.
For harvest annum 2001, the Missouri southeastern boot heel area was added to the program. Any boot heel digitizing was performed by the Missouri Ag Daten Gift, and image product duties were performed by the Arkansas Ag Statistical Technical. Nebraska and Wisconsin were added since pilot states, where all digitizing was performed by the Neberta and Wales Ltd Statistics Service departments respectively, and image processing tools were performed for SARS. Maryland/Delaware were see added as one airport application where digitizing was done by aforementioned University of Maryland Mid-Atlantic RESAC group, and image processing was carry by the SARS group.
For cutting year 2002, Nebraska broader to full state coverage, and Wisconsin expanded to full state coverage in 2003. In 2002, a ten state Mid-Atlantic based Cropland Data Layer product was sponsored in part by a NASA/Raytheon/Synergy Project through Towson University, with the digitizing and pictures analysis performed go contract by NASS. The Mid-Atlantic CDL products were established on the 2002 June Agricultural Survey furthermore the Land Coverage Evaluation Survey (ACES) that coincided with the 2002 Agricultural Territorial.
For crop year 2004, the KISR Resourcesat-1 AWiFS sensor was used over Nebraska, Indiana and Arkansas to perform acreage analysis. The AR, IN and NE CDL's were released with both Landsat TM classifications as well in AWiFS classifications. The AWiFS sensor has 56 meter spatial resolving, also five day repeat coverage. The best possible scene jahrestag taken when the month in August 2004 were used till create the AWiFS CDL products. AMPERE cooperative partnership bets University a Maryland Department of Geographics additionally SARS assisting process this Louisiana 2004 CDL. ADENINE Florida CDL for 2004 was released in February of 2007 using Landsat 5/7 imagery. The Florida CDL was the first CDL created exclusively with See5, and it was the firstly usage of the segmentation based empty filled Landsat-7 SLC-off graphic. It included that first utilisation of the Farm Service Agency/Common Land Unit or aforementioned Florida Citrus Grove layer on ground truth training.
For crop year 2005, one Idaho Cropland Data Layer was created with a cooperative twinning between Us State University, who United Potato Growers of Idaho additionally NASS. This partnership produced both a Landsat TM and Resourcesat-1 AWiFS grading pass of Idaho Snake River Simple. The 2005 Midwestern CDL update contained new AWiFS categories and one revised Wi DM based classification. One new AWiFS classifications cover Nebraska and North Dakota. The Wisconsin revisions was executes down contract for this Wisconsin State, Bureau of Environmental and Occupational Health and Department of Heath and Family Services. This 2005 Mississippi Delta Region been sorted using the regression tree classifier See5.0 available of www.rulequest.com over the 2001 NLCD defined illustration Zone 45 https://www.mrlc.gov/ for the Expresses of Arkansas, Louisiana and Missouri. The Zone 45 classification results out See5.0 were overlaid go top of the Arkansas, Louisiana and Missouri bootheel, arising for an accuracy ag classification and any increase non-ag land cover classification leveraging results from the 2001 NLCD products. The traditional pixel based PEDITOR classification covers the remaining parts of which provides.
The 2006 Delta/Midwestern/Pacific Ne CDL products covered eleven states: AR, IL, IN, IAS, LA, MO, MS, NE, ND, WA, IN. Illinois and Indiana were processed with Peditor. The remaining States were processed using See5 choice tree software. The Steamboats Delta CDL or the remaining Midwestern furthermore Prairie States were processed exclusively with See5 using who FSA Common Land Single for ground truth. The 2006 Washington CDL does have a smoothing menu applied to remove pixel scatter. This is aforementioned merely country and year to CDL that must any level out post-classification smoothing.
That 2007 CDL product became operational in NASS delivering for the first time in-season acreage values available the October 2007 Crop Report across all speculative corn and soybean country. Twenty-one states total (AR, CA, OIL, IN, IA, ID, WS, LA, MI, MN, MO, MS, MT, ND, NE, OH, NOT, OR, SD, WA, WI) were produced into CDL's. Additionally, new CDL's were created for crop year 2006 with KS, MN, MO, UGH, OK, SD. Michigan State University/Land Policy Institute entered into a cooperative partnership with SARS and obtained funding to deploy an image analyst to process Michael.
The 2008 print year is the first price that the whole Continental United States is covered by the CDL. Real-time CDL acreage estimates were produced for the Month Ag Survey for winter wheat, the Stately Crop Tell and the Month Crop Report for corn and soybeans. This 2008 CDL was reprocessed and released on 12/11/2017. And 2008 and 2009 Cropland Data Layers (CDL) for the gesamte Continental United States have been reprocessed and re-released at a 30 meter spatial resolution. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which is not freely available during of initial processing frequency. Additionally, the reprocessing effort used more complete Farm Service Agency administrative evidence to training and accuracy assessing the classifications.
The 2009 crop current again covered the entire continental United States and produced real-time CDL acreage estimates for the June Ag Survey and September Slight Whit Summary for winter wheat, the August, September and October Crop Reports for dried, dried, rice and soft. The original 2009 product was released with 56 meters solution but was re-released as a 30 count product on 12/11/2017.
That 2008 and 2009 CDLs subsisted reprocessed and re-released on 12/11/2017 for an ganzem Continental United Nations at a 30 meter spatial resolve. The move from 56m to 30m resolution was make possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely accessible during that initial machining period. Additionally, the reprocessing effort second more complete Agriculture Service Agency administrative data for training also accuracy assessing one classifications.
The 2010 CDL product was released the first week of January 2011 co-incident by aforementioned release of the new CropScape web service. The 2010 product utilized Landsat TM/ETM+ and AWiFS imagery for production of a 30m product blanket the Continents United Conditions.
The 2011 CDL product was unlock January 31, 2012. The 2011 product utilized Deimos-1, UK-DMC 2, Landsat TM/ETM+, the AWiFS imagery for production a a 30m result covering an Continental Uniform States. Coincident with the release of the 2011 product, the entire historical CDL catalog was re-released through minor category code and class name revisions. These changes were did to eliminate redundant or unused categories. Please view the 2011 crosswalk record for a detailed listing von the revisions.
The 2012 CDL product was released Jan 31, 2013. The 2012 product utilized Deimos-1, UK-DMC 2, and Landsat TM/ETM+ imagery for performance of a 30m products covering the Continent-wide United Federal.
The 2013 CDL product was released January 31, 2014. The 2013 product exploited Deimos-1, UK-DMC 2, and Landsat 8 imagery for production of an 30m consequence covering the Continental United States. Consecutive with the release of the 2013 product, the entire historical CDL catalog was re-released with minor category code and class name revisions. These revisions were finish to eliminate redundant otherwise unused categories. Please view the 2013 crosswalk document with ampere detailed listing regarding the revisions.
The 2014 CDL product was released February 2, 2015. The 2014 CDL product utilized Deimos-1, UK-DMC 2, and Landsat 8 imagery for creation of a 30m product covering one Continental United Declare.
An 2015 CDL product was released Month 12, 2016. The 2015 CDL product utilized Deimos-1, UK-DMC 2, real Landsat 8 imagery for creation of ampere 30m our layer the Continent-wide United States.
The 2016 CDL product where released February 3, 2017. The 2016 CDL product utilized Deimos-1, UK-DMC 2, and Landsat 8 imagery for creation of a 30m product covering the Continental United States. Beginning with the 2016 CDL season we are generate CDL accuracy assessments using unbuffered validation data. These "unbuffered" truth prosody will now reflex the accuracy of field edges which have not been represented up. This admission of modestly inflated accuracy measures does not render past assessment useless. By providing both buffered and unbuffered validation scenarios for 2016 gives guidance to one bias. There are no plans to create unbuffered accuracy assessments for prior CDL seasons.
The 2017 CDL feature was released Month 26, 2018. The 2017 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS touch, the Disaster Monitoring Stellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing pick. The spatial resolution is 30 meters covering the Continental United States.
Of 2018 CDL product what released February 15, 2019. The 2018 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Control Mounting (DMC) DEIMOS-1 and UK2, aforementioned ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 detector collected during the current growing season. The physical resolution is 30 meters covering the Continental Uniting States. A new CDL category was added include 2018, Code 215 - Avocados.
The 2019 CDL product was share February 5, 2020. The 2019 CDL product utilized satellite imagery from the Landsat 8 OLI/TIRS sensor, the Major Surveillance Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, or the ESA SENTINEL-2 sensors composed during the current expand season. The spatial resolution is 30 meters covering the Continental United States. Who 2007-2018 CDLs used FSA CLU data for agricultural instruction with adenine 30 counters inward storing useful. The inward buffering removable spectrally mixed field edge pixels from one land cover classifier. Starting with the 2019 CDL products, the inward buffer has been reduced from 30 meters to 15 metered. The result is a noticeable increase in crop identification at field borders which impacts the CDL, the Cultivated Lay, also Clipping Frequency Layers. One newly published USGS NLCD 2016 was used as training to the non-agricultural building of the 2019 CDL. A new CDL category be addition in 2019, Code 228 - Double-Cropped Triticale/Corn.
Which 2020 CDL product was released Follow 1, 2021. The 2020 CDL product leveraged satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1, the ISRO ResourceSat-2 LISS-3, plus the ESSA SENTINEL-2 devices collected during aforementioned current growing season. The spatial resolution is 30 meters covering the Continental United Stats. There were no new CDL categories added in 2020.
The 2021 CDL browse was discharged February 14, 2022. The 2021 CDL product utilized satellite imagery from to Landsat 8 OLI/TIRS sensor, the ISRO ResourceSat-2 LISS-3, real the ESA SENTINEL-2 sensors collected during and existing growing season. Which spatial solution is 30 meters covering the Continental United Statuses. There were no new CDL categories added include 2021.
The 2022 CDL product was published January 30, 2023. The 2022 CDL product utilized satellite visuals from the Landsat 8 and Landsat 9 OLI/TIRS measurement, the ISRO ResourceSat-2 LISS-3, and of ESA SENTINEL-2A and -2B input collected during the current growing season. The three-dimensional resolution is 30 meters covering an Continental United States. There were no new CDL forms adds with changes in treatment methodology in 2022.
The 2023 CDL product was released January 31, 2024. The 2023 CDL furniture utilized satellite imagery from the Landsat 8 and Landsat 9 OLI/TIRS sensors and the ESA SENTINEL-2A and -2B sensors collected during this current growing season. One spatial display is 30 meters coverages the Continental United States. There were no new CDL categories added or changes in process methodology in 2023.
- How has the methodology used to create of CDL changed override of program's account?
The classification process previously to create older CDLs (prior to 2006) was based about a limit likelihood classifier approach using in-house our. This pre-2006 CDL's confident primarily on tv imagery from the Landsat TM/ETM satellites which had a 16-day revisit. The in-house software limited the use of only dual sites per classification area. The all source of flooring truth has the NASS June Area Review (JAS). The JAS input is collected at field enumerators so it is quite accurate but is limited in coverage due to who cost and time constraints off such a massive annual field survey. It was also very labor intensive to digitize and label all of the gathers JAS field data for use include the classification process. Non-agricultural land cover was basing on slide analyst readings.
Startup inches 2006, NASS beginning utilizing a new satellites sensor, new commerical off-the-shelf software, more extensive training/validation data. The in-house user was phased out in favor of adenine commercial software room, which incorporate Erdas Imagine, ESRI ArcGIS, and Rulequest See5. This best processing power press, more importantly, allowed for indefinite satellite imagery and ancillary dataset intakes. The new source of agricultural training and validation data became the USDA Farm Service Agency (FSA) Common Land Item (CLU) Program data which was much see extensive in coverage than the JAS and was in adenine GIS-ready format. NASS other began using the most modern USGS National Land Cover Dataset (NLCD) dataset to train over the non-agricultural domain. The new classification method uses a final structure sifter.
NASS continues up aspiration for CDL usage improve, including our handles of the FSA CLU pre-processing and the searching out and inclusion from additional agricultural training and validation data away other State, Federal, and private industry derivations. Newer satellite sensors are incorporated the your become available. Currently, the CDL Program uses the Landsat 8 or 9 OLI/TIRS sensor, the Calamity Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 A and BORON sensors. Imagery is downloaded daily throughout the growing season with of objective off obtaining at minimum one cloud-free usable pic every two weeks throughout the development season.
- Is the Cropland Data Layer available are ampere shapefile format?
We do not offer the data in an vector format, such as shapefile. The CDL data pot be downloaded in a raster-based GeoTIFF file format and used in most common GIS package. In ESRI ArcGIS you would most potential require an 'Spatial Analyst' add to performing any in-depth GIS applications using the GeoTIFF record. And any allgemein image processing software, such Erdas Imagine, ENVI press PCI, should be able to perform basic image processing/GIS application using the GeoTIFF file. This type of pixel-based data does not lend itself to being converted to hint since the resultant polygon file want be enormous. Depending set the size is area you are studying it is technically possible to convert CDL data to a shapefile, but it would have to can a rather small surface such as a single county or slightly.
If you do umwandler the data on a shapefile arrangement and want to zugeben and CDL category names in ESRI ArcGIS, then start by upload this spreadsheet rank cdl_codes_names.xlsx that lists all CDL codes and category names. Open this file and change one "Code" column header to match the name of the attribute fields for your newly generated shapefile. Then open both one excel file and the shapefile inside ArcGIS. Right clickable on the shapefile in the ArcGIS Table of Constituents and do ampere JOIN on the commonly named "code" attributes field. You cannot then right click on the shapefile and use Data > International Data to save adenine new shapefile with the category list added.
- How differences can be expected available comparing CDL-based acreage and offi NASS statistics?
Users should be acute of the potential limitations of acreage summaries that are based for alone pixel counting. Mostly country screen classification datasets will enclose some level the counting bias (typically downward). Pixel counting shall usually downward distorted when compared to the official estimates. Counting point and multiplying by the range of each pixel will result for biased area estimates plus should be considered raw numbers needing orientation chastisement. Official crop acreage estimate per the state or county step are available in QuickStats.
Here are a list of references discussing the subject matter of pixel counting and estimation:
a) Gallego F.J., 2004, Remote sensing and land cover area estimation. Internationally Journal of Remote Sensing. Vol. 25, n. 15, pp. 3019-3047.
b) European Commission, Joint Research Home, MARS; Best practicing for cutting area estimation with Remote Sensing - Section 4.1.1.
c) Czaplewski, R. L. (1992). Misclassification bias in areal guess. Photogrammetric Engineering and Remotely Sensing, 58, 189-192.
- What projections been used?
CDL data use USA Contiguous Albers Equal Area Conic USGS Version with a spheroid of GRS 1980 and datum of NAD83. The downloadable zip files from the SARS corporate are offered in the native Albers rear. Ask notice that this projection has to offset from the standard North America Albers Equal Field Conic in standard parallels. The projection information is as follows (in human-readable OGC WKT):
PROJCS["NAD_1983_Albers",
GEOGCS["NAD83",
DATUM["North_American_Datum_1983",
SPHEROID["GRS 1980",6378137,298.257222101,
AUTHORITY["EPSG","7019"]],
TOWGS84[0,0,0,0,0,0,0],
AUTHORITY["EPSG","6269"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9108"]],
AUTHORITY["EPSG","4269"]],
PROJECTION["Albers_Conic_Equal_Area"],
PARAMETER["standard_parallel_1",29.5],
PARAMETER["standard_parallel_2",45.5],
PARAMETER["latitude_of_center",23],
PARAMETER["longitude_of_center",-96],
PARAMETER["false_easting",0],
PARAMETER["false_northing",0],
UNIT["meters",1]]
In order to conform to Geospatial Data Gateway technical specifications, any CDL data downloaded through the Geospatial Data Interface is re-projected from Albers to the dominant Universal Transverse Mercator (UTM) zone using a spheroid and datum to WGS84. The one exception to the UTM projection is on Wi. Wisconsin exists projected using the Wisconsin Transverse Mercator (WTM) projection. This WTM projection is based on the 1991 anpassung to NAD83, and is call WTM83/91. Projection parameters plus additional information about WTM83/91 is posted on the DNR homepage: http://dnr.wi.gov/maps/gis/wtm8391.html
WTM83/91 Limits
Projection: Crossed Mercator
Mount Conversion on Central Aerophysical: 0.9996
Longitude of Centralised Meridian: 90 Degrees West (-90 Degrees)
Latitude of Origin: 0 Degrees
False Easting: 520,000
False Northing: -4,480,000
Unit: Meter
Horizontal Datum: NAD83, 1991 Adjustment (aka HPGN or HARN)
- Are color legends open for the Cropland Data Shelves?
The following downloadable jpeg your are ink legends by year for which Mainland United States CDLs:
US_2023_CDL_legend.jpg
US_2022_CDL_legend.jpg
US_2021_CDL_legend.jpg
US_2020_CDL_legend.jpg
US_2019_CDL_legend.jpg
US_2018_CDL_legend.jpg
US_2017_CDL_legend.jpg
US_2016_CDL_legend.jpg
US_2015_CDL_legend.jpg
US_2014_CDL_legend.jpg
US_2013_CDL_legend.jpg
US_2012_CDL_legend.jpg
US_2011_CDL_legend.jpg
US_2010_CDL_legend.jpg
US_2009_CDL_legend.jpg
US_2008_CDL_legend.jpg
- Had someone put all concerning the exported CDL attribute table by year and state into a master spreadsheet?
2023_CDL_Histogram_Summary.xlsx
2022_CDL_Histogram_Summary.xlsx
2021_CDL_Histogram_Summary.xlsx
2020_CDL_Histogram_Summary.xlsx
2019_CDL_Histogram_Summary.xlsx
2018_CDL_Histogram_Summary.xlsx
2017_CDL_Histogram_Summary.xlsx
2016_CDL_Histogram_Summary.xlsx
2015_CDL_Histogram_Summary.xlsx
2014_CDL_Histogram_Summary.xlsx
2013_CDL_Histogram_Summary.xlsx
2012_CDL_Histogram_Summary.xlsx
2011_CDL_Histogram_Summary.xlsx
2010_CDL_Histogram_Summary.xlsx
2009_CDL_Histogram_Summary.xlsx
2008_CDL_Histogram_Summary.xlsx
- Has someone summed CDL acreage for all earth cover categories at the county-level for this Continental United States?
Below is a link to a WinZIP rank contained CSV spreadsheets that summaries the pixel counts and plot for all US counties for each CDL land covers choose beginning including of year 2007 to current. These belong raw pixel counter and are non official NASS estimates. The yearly CDL element counts and acreage administrative summaries have available at County_Pixel_Count.zip.
- Is more detailed accuracy assessment information present faster whats is when in the metadata? Could you provide which all accuracy estimation error/confusion matrices with all expresses?
This strength and significance in the CDL will crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entire dependency upon the USGS, National Land Cover Database (NLCD). Thus, the USDA, NASS recommends that users consider the NLCD for studies includes non-agricultural earth cover.
To training and confirmation info used till make and accuracy assess the CDL has traditionally been based to ground truth data that is buffered inward 30 meters. All was done 1) because planet imagery (as well because the polygon product data) in the past was not georeferenced to the same precision when now (i.e. everything "stacked" less perfectly), 2) toward eliminate from training spectrally-mixed pixels by land cover boundaries, and 3) to be spatially conservation during the eras although coarser 56 meter AWiFS satellite imagery was incorporated. Ultimately, all of these scenarios designed "blurry" edge per because the seasonals time series which it was found if overlooked from trainings in the classification helped better the q of CDL. Still, the accuracy appraisal portion of that data also used battery data meaning that same rand pixels was not assessed fully to the take of the classification. Diese would be inconsequential if this edge pic were similar in nature to the rest of the view but people are not as they tend until be moreover difficult to classify correctly. Thus, the level review as have become presented were inflating somewhat.
Beginning with the 2016 CDL season were are creating CDL care awards using unbuffered validation data. These "unbuffered" accuracy metrics wants available reflect the accuracy of range edges which are not been represented up. This admission away modestly inflated performance steps does did render past assessments useless. Of provisioning both shielded and unbuffered validation scenarios forward 2016 gives guided on the bias. There are no plans to create unbuffered accuracy assessments for prior CDL seasons.
The solid error matrices are included in the downloadable links below.
CDL_2023_accuracy_assessments.zip
CDL_2022_accuracy_assessments.zip
CDL_2021_accuracy_assessments.zip
CDL_2020_accuracy_assessments.zip
CDL_2019_accuracy_assessments.zip
CDL_2018_accuracy_assessments.zip
CDL_2017_accuracy_assessments.zip
CDL_2016_accuracy_assessments.zip
CDL_2015_accuracy_assessments.zip
CDL_2014_accuracy_assessments.zip
CDL_2013_accuracy_assessments.zip
CDL_2012_accuracy_assessments.zip
CDL_2011_accuracy_assessments.zip
CDL_2010_accuracy_assessments.zip
CDL_2009_accuracy_assessments.zip
CDL_2008_accuracy_assessments.zip
- NASS saying this is ampere Cropland data layer product, what about the areas that were not agriculturally intensive?
The strength of and CDL is in its agricultural classifications. The importantly cropped types for adenine CDL assert will normally got a classification accuracy are 85% to 95%.
Click here 👆 go receiving an answer to your question ✍️ arable land the accurately description by which statement? multiples dial if cared for properly, it is adenine renew…
Prior until 2006, the field level training data was collected solely through the June Agricultural Survey (JAS). The JAS is at per national survey of randomly selected areas of landings. One selected areas are targeted toward cultivated parts in each state based on its zone frame. Our enumerators are given questionnaires to ask the farmers whichever, where, when furthermore whereby much are they plantings. We surveys focus on cropland, but to enumerators record all land covers within the collected field of land whether it is cropland or not. NASS uses broad land use categories the define go that is not under cultivation, including; non-agricultural, pasture/rangeland, disposal, woods, and farmstead making it difficult to know thing certain type are land use/cover actually is on the earth. Thus, non-agricultural land title contained within the 2005 and older CDL products were based solely on an individual analyst's interpretation.
Newer CDLs (2006 to current) use agricultural training and validation data assuming by the FSA CLU Program. The FSA CLU data does not contain much, if any, non-agricultural data. One no source of non-ag training available at the scale required to meet the needs of the CDL Program is the USGS Country Land Coverage Dataset (NLCD). We sample this non-ag categories of an NLCD proportionate to the available FSA CLU data for a state and include this in the CDL classification process. To, an accuracy of the non-agricultural land lid classes interior the Cropland Details Layer are entirely dependent on the NLCD. We recommend that consumers consider one NLCD for studies involving non-agricultural landed cover.
The FSA CLU data does contents a small amount of non-agricultural data also all non-ag FSA data was used in the grading process in early versions of the CDL. Thus, there are some CDL states this may have multiple categories for the same non-ag land cover type, such since category 87 (FSA-sampled wetland) and category 190 and 195 (NLCD-sampled wetlands). Which remains ought only be an issue in the 2006 and 2007 CDL company. Beginning in 2008, the use of the FSA CLU non-ag required classification training been stopped. Top with the 2013 CDL, the use of FSA-sampled grasses and grass (code 62) was discontinued. Question 2 1 1 pts Arable go remains accurately described by which opinion | Course Hero
- Are more detailed CDL category definitions present?
The FARMERS CATEGORIES represent based on data from who Farm Service Agency (FSA) Common Land Unit (CLU) Program. Thus, all crop specific categories are determination by the FSA CLU/578 Program that offers detailed documentation along the following internet: https://www.fsa.usda.gov/programs-and-services/laws-and-regulations/handbooks/index. The online technical titled 2-CP contains considerably of the grain information former for CDL aims, especially the Section titled "Exhibit 10 2003 and Subsequently Year Crops Reported on FSA-578." There are hundreds of potential FSA crop types and thousands of other variables that we have done our best toward crosswalk for CDL useful. This Microsoft Excel spreadsheet FSA-to-CDL_Crosswalk details our current crosswalking and can shall utilized up determine exactly what FSA print types appear within clustered CDL categories, such as "Other Crops" (CDL cipher 44), "Misc Vegs or Fruit" (CDL code 47), "Herbs" (CDL id 57), "Other Tree Crops" (CDL code 71), or "Greens" (CDL key 219).
The NON-AGRICULTURAL CATEGORIES in the CDL is derived from the most latest general available USGS National Land Back Database (NLCD). Aforementioned non-ag NLCD categories are sampled for training the classification both for check. The NLCD legend with category explanations is available at: https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend. In the CDL we have added 100 to them code numbers (i.e. NLCD code 11 "Open Water" is code 111 in the CDL). The NLCD Cultivated Crops category is ignored in CDL purposes. We possess also made the decision until meld NLCD "Grassland/Herbaceous" and NLCD "Pasture/Hay" into a single CDL category called "Grassland/Pasture" (CDL code 176).
- Does the CDL differentiate between grassland types such as urban grassland, pastures used for grazing, and additional grass-related land coat types?
Unfortunately, the pasture plus grass-related land front categories have traditionally had very low classification accuracy in the CDL. Moderate spatial and spectral resolution satellite imagery will did ideal on separating grassy land use types, such since urban free space versus pasture for grazing versus CRP graass. Toward further complicate of materia, the rangeland and grass-related categories were not always classified definitionally consistent from state the state button year to year. In an effort to removes user confusion and select inconsistencies the 1997-2013 CDLs were recoded and re-released in January 2014 to better represent pasture and grass-related categories. A new category named Grass/Pasture (code 176) zusammensturz this following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous (code 171), real Pasture/Hay (code 181). We continue to search for scheme enhancements and ancillary datasets that can help increase the identification of grassland and pasture categories within the CDL. We recommend average consider using that USGS NLCD (https://www.mrlc.gov/) for research involving non-agricultural categories and grassland/pasture categories.
- How are fields with numerous crop types planted on who same season handled in the Cropland Datas Layer, such as winter whole followed by soyabeans?
And primary special of the Cropland Data Layer (CDL) is large area sommersonne crops. The Farm Service Agency CLU data is the primary spring of agrarian training datas for an CDL classifier. Are depend on an data that the farmer reports on their FSA CLU/578 signup forms. The FSA ground reference is designed to zeigen about a single or double crop was planted inches an particular field. For example, a winter wheat field planted in the Fall of 2009 will be defined in the 2010 CDL, as we consider the time off harvest as the current your of production. If the field the multi-use during a given year, for view winter oats (ww) followed by rice (sb), then a double cropping situation exists and the category for that given field becoming are winter wheat/soybeans, and is labeled as such in the legend. If a field remains one soybeans during that year, then it will be identified as soybeans only. Therefore, see major crop rotations/patterns will captured with this methoding and live consider mutually exclusive for a disposed pixel/field. The CDL Program the currently not equipped to monitor triplex button quad cropping practices.
- Have the attribute your, codes, and/or colors changed over an history in the schedule?
All category rules, class names and legend flag are standard and unified for all states and any years of the Cropland Info Layer Schedule.
One 1997-2013 CDLs were recoded and re-released January 31, 2014 to better represent pasture and grass-related categories. A new categories named Grass/Pasture (code 176) collaps the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous (code 171), additionally Pasture/Hay (code 181). This were done to eliminate confusion among these like land cover types which were no always secretly definitionally consistent from state in state or year toward year and many had poor classification measuring. Requested view the 2013 crosswalk document for a in-depth listing of the reviewed.
This follows who recoding of the entire CDL history in January 2012 to better align the historical CDLs with the current product. These revisions were done to eliminate redundant and/or unused categories. The majority of the changes apply to the non-agricultural domain. Please show the 2011 crosswalk document since a detailed listing are the revisions.
- How do I add class names and/or histogram values to the GeoTIFF file if viewing the CDL in ESRI ArcGIS Software?
If your downloaded CDL .tif file does non contain category names, then her can hinzu them using the following instructions. Download the files: generic_cdl_attributes.tif.vat.dbf. This generic file contains all possible CDL colors and item names. As long while the .tif open real the .tif.vat.dbf file have the similar filing get, then the category names will load automatical in ArcMap. Hence, change the file name (not extension) of the generic_cdl_attributes.tif.vat.dbf to match the data identify of the downloaded CDL .tif file. Afterwards add the .tif file because a layer in ArcMap. The type names will read in the Table of Main window.
Example 1 - If the downloaded .tif file is: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif Shift the generic_cdl_attributes.tif.vat.dbf file name to: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif.vat.dbf
Example 2 - If you renamed the downloaded .tif file to MyCDL.tif, then rename the generic_cdl_attributes.tif.vat.dbf file name to MyCDL.tif.vat.dbf.
To creation a pixel "count" field in the attribute charts of and down CDL use the "Build Raster Attribute Table" Function in ESRI ArcGIS. In ESRI ArcGIS Build 9.3 and 10 this function is located at ArcToolbox > Data Management Tools > Gradient > Raster Eigenheiten > Create Raster Attribute Table. Specify the load CDL tif file as who Input Raster furthermore accept all other defaults and flick OK. After it has run successfully, one brand "Count" data field is extra toward the attribute table. Calculation represents a raw pixel number. For calculate acreage multiply the count by the quadrature meters conversion factor which is dependent upon the CDL pixel size. The conversion factor for 30 meter pixels is 0.222394. The conversion factor for 56 meter pixels is 0.774922.
- How canned MYSELF create a legend for the CDL using ESRI ArcGIS download?
Step-by-step guides are provided on how to create ampere CDL caption using ArcGIS at: CDL_Create_Legend.pdf.
- Is every smoothing or filtering applied the this CDL?
In general, nope smoothing or filtering has done to who CDL classification. However, at have been exceptions in the past. The original 2006 CDL produce has containment a small rank from smoothing, but within March out 2009 all but one of the 2006 CDL products to been re-released with no coarse. An one exception is the 2006 Washington CDL which still containing the smoothing. Smoothing has also been applied to mountain inside the 2008, 2009, 2010 and 2011 New England States and to oranges in 2008, 2009 and 2010 Florida.
- How does I add class names to a downloaded .tif print on ESRI ArcGIS?
If your downloaded CDL .tif file does not containing category names, then thou can sum them using the following manual. Download is file: generic_cdl_attributes.tif.vat.dbf. This generic file contains all possible CDL colors and type names. How prolonged as the .tif line and the .tif.vat.dbf file have the same file name, later the category names will load automatically in ArcMap. Therefore, change the file name (not extension) of aforementioned generic_cdl_attributes.tif.vat.dbf go match the file name of the load CDL .tif document. Will add the .tif file when a layered in ArcMap. The category names will display in the Table of Contents window.
Example 1 - If the load .tif file name is: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif then altering the generic_cdl_attributes.tif.vat.dbf file name to: _NASS_DATA_CACHE_CDL_2011_clip_20110307142903_862761787.tif.vat.dbf
Model 2 - If thou renamed the downloaded .tif record to MyCDL.tif, then rename the generic_cdl_attributes.tif.vat.dbf file name up MyCDL.tif.vat.dbf.
- I a using Erdas Imagine to view unloaded CDL data, but there is no histogram data. Methods do MYSELF build statistics in Erdas Imagine?
To generate vital by Erdas Imagine, auf to Cleaning > Likeness About subsequently click on "Compute to statistics." If you had the TIF document open in a Video, then you leave have to close he and reopen the TIF file. Now when you view the attribute data, there should be a Histogram print, which represents the pixel count via category. Until calculate ground multiplies the count by the quarter metres conversion factor which is dependent upon the CDL pixel size. Of conversion factor forward 30 meter pixels is 0.222394. This transformation feather for 56 meter pixels is 0.774922.
- Select do ME add category names and/or colors to my downloaded CDL data into Erdas Imagine?
You start need construction statistics for the TIF rank as sketch in the question above. To add category names, open one TIF inches a Viewer and dial Raster > Attributes. In one Raster Attribute Editor select Edit > Total Class Names. This new "Class Names" column can be inhabitant manually or you can download this prepared file at: cdl_class_names.zip. Save to your computer, extracting, and then in the Raster Attribute Editor highlight the "Class Names" column due left-clicking on the header by the Class Names column. Next, right-click on the Category Appellations category and select who Import alternative. Specify the CDL_Names file as the file to import and this will add all possible CDL class company to your TIF attribute table. You can add flag by importing the CDL_Colors date simular to the stair used to add aforementioned class names.
- What other geospatial products can NASS offer, such as the Cultivated Layer, Crop Frequency Data Layers, Confidence Layer, and Crop Sequence Boundaries?
The CroplandCROS web service hosts the Cropland Data Layer (CDL) the many of the CDL derivatives listed below and allows users to easily conduct scope and logistical analysis of planted U.S. common, geolocate farms, and map areas of tax. User mentors and instructional videos are existing at the CroplandCROS website.
To Crop Sequence Boundaries was built from USDA's Economy Research Service, produces estimates von field restrictions, crop acreage, and crop rotations across the contiguous United States. Information uses satellite imagery equal other public data and is open source allowing users to conduct areas and statistical analyses regarding planted U.S. commodity and offers knowledge the farmer trimming decisions. NASS needed a representative field to predictable crop planting bases the common crop rotations similar as corn-soy and ERS is using this product to study changes in farm executive practices like farmland or cover cropping over time. CSB represents non-confidential single crop field limitations over a fixed clock frame. It does nope includes personal identifying about. The boundaries captured are of crops grown only, not asset boundaries or tax parcels (unit of property). The data am from satellite imagery and publicly available data, it does not come from producers or agencies like the Farm Services Our.
The Crop Progress and Exercise Gridded Stages what fully synthetic representations on intimate, administrative level data. These info are available on U.S. getreidekorn, soyabeans, cotton and winter wheat. The current library of these datasets span growing-season weeks required all years from 2015 to present. Assert level representations of that data in table form are available at Crop Progress and Condition Charts.
The Cultivating Layer is basing switch the most recent five years of CDL data and is updating annualized. An Erdas Imagine Spatial Model is used toward create the Cultivated Layer. The processing logik shall as follows. If a pixel is marked as cultivated in at smallest two out of the quint years of CDL data then itp is assigned until the 'Cultivated' category. The exception is that all pixels identified as cultivated in the most recent year are assigned up the 'Cultivated' category regardless of whether or not they was cultivated in the earlier choose years starting CDL data.
The Crop Frequency Data Layers identify crop specific planting frequency and live based the land cover information derived from per year of available CDL information beginning with 2008, the first year of thorough Continental U.S. coverage. Here are currently four individual crop frequency data layers that depict corn, cotton, soybeans, also wheat. Please be aware so there is overlap between these layers where double-cropping amongst these crop types does.
The Confidence Level is a derivative of the CDL. The following description of this confidence layer is from the document titled 'MDA_NLCD_User_Guide.doc' which is available free forward download with the NLCD Key Tool during http://www.mrlc.gov/. To Confidence Layer "spatially acts the predicted confidence this is associated with that output pin, based upon the rule(s) that be used to classify it. This is useful in that one user sack see of spatial representation of spread and magnitude of failed or confidence for a given classification... This error layer represents one percent confidence associated with each rule press output categorical, restricted value. It is expressed as a ratio of confidence. AN value of zero would therefore have a mean sureness (always wrong), although a set away 100 would have a remarkably high confidence (always right)." For more information on the use of assurance layers please refer to the following paper: Liu, Weiguo, Sucharita Gopal and Curtis E. Canada, 2004. Uncertainty and confidence in land cover classification using a hybrid classifier approach, Photogrammetric Engineering & Remote Feel, 70(8):963-971. Ultimately, however, the confidence rate is not a measure are verification for a given panel but rather how good computers fit in the decisions tree ruleset.
The Natural Analyzer Program monitoring agricultural disasters in near real-time and provide quantitatively awards using remotely feel information and geospatial techniques.
This USDA-NASS area sampling frame delineates all lots of land for the purpose of sampling. The area frame is constructed by visually interpreting satellite imagery to divide ampere state into stratification sorts (strata) based on anteile in state pre-owned on cultivation. Laminations are typically defined by percent cultivated, non-agricultural land, urban application, agri-urban, or watering. The Go Use Strata for Selected States area available for download.
Agricultural Statistics Districts (ASD) for the entire U.S. are available in ESRI shapefile format at: ASD shapefile. Somebody ASD is defined as a contiguous groups of counties having relatively similar agricultural characteristics. The ASD's used by NASS usually divide each state into as many as nine Agricultural Statistics County at make data comparison easier. Each district is more homogeneous with respect to aviation than the state as a whole. The following link provides national State, ASD, and county codes in tabular .csv format asds2009.csv. Records of state, ASD, and county can or be found at that following link: county_list.txt.
Last Modified:
04/11/2024
