Hawaii Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

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

Publication_Date: 20260612
Title: Hawaii Cropland Data Layer
Edition: 2025 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

USDA NASS Marketing and Information Services Office, Washington, D.C.

Publisher: USDA NASS

Other_Citation_Details:

Data available free for download at <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php>. Li, Z., Chen, Q., Yang, Z., Mueller, R., Willis, P., Chen, H., Lincoln, N.K., and Cao, C., 2026, Hawaii Cropland Data Layers (HCDL) v2.1: A Satellite Embedding Approach to Crop Mapping, IGARSS 2026 - 2026 IEEE International Geoscience and Remote Sensing Symposium, Washington D.C., United States, August 9 - 14, 2026.

Online_Linkage: <https://agriwatch.projects.earthengine.app/view/hcdl-viewer>

Description:

Abstract:

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced for the COntinental United States (CONUS) using satellite imagery and extensive agricultural ground reference data. The Hawaii Cropland Data Layer (HCDL) is the first-ever raster, geo-referenced, crop-specific land cover data layer for Hawaii at a 10-meter spatial resolution for the years 2023, 2024 and 2025. Both years of data were released to the public August 25, 2025, on the official download website: <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php>. Please note that no farmer reported data are derivable from the Cropland Data Layer. This paper contains more information: Li, Z., Chen, Q., Yang, Z., Chen, H., Lincoln, N.K., Cao, C., Mueller, R., and Willis, P., 2024, Innovative Crop Mapping in Hawaii: The First 10-Meter Resolution Cropland Data Layers for 2023, The 12th International Conference on Agro-Geoinformatics. Novi Sad, Serbia, 2024, pp. 1-6, 10.1109/Agro-Geoinformatics262780.2024.10660811.

The HCDL leveraged the 64-dimensional geospatial embeddings from the Satellite Embedding dataset developed by Google DeepMind's new geospatial AI model, AlphaEarth Foundations. This dataset was derived from multiple datastreams, including optical, radar, LiDAR, and other sources. Ground-reference data for crop type classification was developed based on satellite/aerial imagery interpretation and field surveys conducted by the University of Hawaii at Manoa beginning December 2023. Fieldwork covered the islands of Kauai, Oahu, Maui, Molokai, and the Big Island. HCDL 2025 was derived from 2025 embeddings using the SmileRandomForest classifier in Google Earth Engine, based on the 2025 ground-reference data. Since the embedding space is consistent across years, it can be used for condition change detection. Due to the limited ground- reference data available in 2023, HCDL 2023 was derived from 2023 embeddings solely based on the trained 2024 models, thus reducing uncertainties caused by the subjective human-involved training data labeling process. More information available in this paper: Li, Z., Chen, Q., Yang, Z., Mueller, R., Willis, P., Chen, H., Lincoln, N.K., and Cao, C., 2026, Hawaii Cropland Data Layers (HCDL) v2.1: A Satellite Embedding Approach to Crop Mapping, IGARSS 2026 - 2026 IEEE International Geoscience and Remote Sensing Symposium, Washington D.C., United States, August 9 - 14, 2026.

HCDL was a collaborative effort between USDA NASS and the University of Hawaii at Manoa. This research is part of AgriWatch: Innovating Agricultural Disaster Response with AI-Empowered Real- Time Monitoring project supported by the USDA National Institute of Food and Agriculture (NIFA) Agriculture and Food Research Initiative Competitive Grants Program under award number [2024- 68016-41561].

Note on spelling: The plain ASCII spelling Hawaii is used in this metadata for system compatibility.

Purpose:

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.

Supplemental_Information:

The data is available free for download at <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php>.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20250101
Ending_Date: 20251231

Currentness_Reference: 2025 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: annual updates

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -160.2497
East_Bounding_Coordinate: -154.8083
North_Bounding_Coordinate: 22.2324
South_Bounding_Coordinate: 18.9173

Keywords:

Theme:

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

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

Theme:

Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: farming
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: ESA SENTINEL-2
Theme_Keyword: Landsat

Place:

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

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: USA
Place_Keyword: Hawaii

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2025

Access_Constraints: none
Use_Constraints:

The USDA NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA NASS does not warrant any conclusions drawn from these data.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA NASS, Spatial Analysis Research Section
Contact_Person: USDA NASS, Spatial Analysis Research Section staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Data_Set_Credit: USDA National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 10 Enterprise; Google Earth Engine <https://earthengine.google.com/>; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.8 and ArcGIS Pro 3.1.3 <https://www.esri.com/>.

The Hawaii CDL uses Google Earth Engine to produce the land cover classification. ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based data.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

If the following table does not display properly, then please visit the CDL Metadata webpage at <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original file. Accuracy at the individual state-level can be viewed at the CDL Metadata webpage.

The accuracy metrics below are for the 2024 Hawaii CDL as a general reference. An accuracy assessment has not yet been run for the 2025 Hawaii CDL.
USDA National Agricultural Statistics Service, 2024 Hawaii Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Cover                   Attribute        F-1
Type                         Code      Score
----                         ----     ------
Corn                            1      0.965
Sunflower                       6      0.999
Sugarcane                      45      0.998
Sweet Potatoes                 46      0.992
Misc Vegs and Fruits           47      0.985
Forest                         63      0.953
Citrus                         72      0.991
Developed                      82      0.935
Banana                         93      0.974
Other Exotic                   94      0.922
Coffee                         95      0.950
Macadamia                      96      0.924
Papaya                         97      0.975
Pineapple                      98      0.998
Commercial Forest              99      0.913
Taro                          100      0.985
Sweet Basil                   101      0.995
Coconut                       102      0.899
Plumeria                      103      0.999
Noni Fruit                    104      0.985
Dracaena                      105      0.990
Other Crops                   106      0.901
Open Water                    111      0.999
Barren                        131      0.981
Shrubland                     152      0.936
Grassland/Pasture             176      0.920
Avocados                      215      0.995

The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

Classification accuracy for the CONUS CDL is generally 85% to 95% correct for the major crop-specific land cover categories.

Attribute_Accuracy_Explanation:

The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

An F-1 score provides a single measure of classification accuracy by combining how often the model correctly identifies a class and how reliably those identified results are correct. Higher F-1 scores indicate stronger overall performance for that category. This metric helps users quickly assess the quality of the classification without needing to interpret precision and recall separately.

Logical_Consistency_Report:

The Hawaii Cropland Data Layer (HCDL) was produced using ground reference and validation data developed based on satellite/aerial imagery interpretation and field surveys conducted by the University of Hawaii at Manoa beginning December 2023. Fieldwork covered the islands of Kauai, Oahu, Maui, Molokai, and the Big Island.

Completeness_Report: The Hawaii CDL covers the entire State of Hawaii.
Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat 8 and 9 OLI/TIRS imagery uses the Collection 2 Level-1 specifications. Please reference the metadata on the USGS Glovis website for the positional accuracy of each Landsat scene. The Sentinel 2A and 2B imagery uses using the S2MSI1C product type which is orthorectified Top-of-Atmosphere reflectance. Please reference the metadata on the Copernicus website for positional accuracy details.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Google DeepMind
Publication_Date: 2025
Title: AlphaEarth Foundations Satellite Embedding
Geospatial_Data_Presentation_Form: derived data product
Publication_Information:

Publication_Place: London, United Kingdom
Publisher: Google DeepMind

Other_Citation_Details:

AlphaEarth Foundations Satellite Embedding is a machine-learning-derived representation built from multi-sensor satellite observations. Additional information about the AlphaEarth platform and related products is available at <https://deepmind.google/>.

Source_Scale_Denominator: 10000
Type_of_Source_Media: online access
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20230101
Ending_Date: 20251231

Source_Currentness_Reference: model-derived representation

Source_Citation_Abbreviation: AlphaEarth Embedding
Source_Contribution:

Machine-learning-generated satellite embeddings used as ancillary inputs supporting the classification process.

Source_Information:

Source_Citation:

Citation_Information:

Originator: USDA Forest Service
Publication_Date: 2021
Title: Hawaii TreeCanopy GIS Layer
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: Vallejo, California
Publisher: USDA Forest Service, Region 5

Other_Citation_Details:

Additional information and access to the Hawaii TreeCanopy GIS Layer are available at <https://www.fs.usda.gov/detail/r5/communityforests/?cid=fseprd1009698>.

Source_Scale_Denominator: 1200
Type_of_Source_Media: online access
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20200101
Ending_Date: 20201231

Source_Currentness_Reference: publication date

Source_Citation_Abbreviation: Hawaii TreeCanopy
Source_Contribution:

Ancillary canopy extent and density information used to support classification refinement.

Source_Information:

Source_Citation:

Citation_Information:

Originator: USDA Farm Service Agency (FSA)
Publication_Date: 2022
Title:

National Agriculture Imagery Program (NAIP) 2022 60 cm CIR Imagery

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Other_Citation_Details:

NAIP imagery is publicly available through USDA public imagery distribution platforms and state GIS clearinghouses.

Source_Scale_Denominator: 5000
Type_of_Source_Media: online access
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20220101
Ending_Date: 20221231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NAIP 2022
Source_Contribution:

Source imagery used for visual interpretation and classification input support.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Planet Labs PBC
Publication_Date: 2025
Title: PlanetScope Ortho Scene Imagery (2023-2025)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: San Francisco, California
Publisher: Planet Labs PBC

Other_Citation_Details:

PlanetScope imagery was accessed under an institutional license. Additional information on PlanetScope products is available at <https://www.planet.com/>.

Source_Scale_Denominator: 1200
Type_of_Source_Media: online access
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20230101
Ending_Date: 20251231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: PlanetScope TS
Source_Contribution:

Multi-temporal imagery used to evaluate crop phenology and improve classification separability.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Google LLC
Publication_Date: 2025
Title: Google Earth High-Resolution Imagery
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publication_Place: Mountain View, California
Publisher: Google LLC

Other_Citation_Details:

Google Earth imagery is a composite of multiple commercial and governmental sources. More information is available within the Google Earth platform.

Source_Scale_Denominator: 1000
Type_of_Source_Media: online visualization
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20200101
Ending_Date: 20251231

Source_Currentness_Reference: imagery acquisition date varies by location

Source_Citation_Abbreviation: Google Earth Imagery
Source_Contribution:

High-resolution visual reference used to support manual review and verification of classification features.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Hawaii Department of Agriculture
Publication_Date: 2020
Title:

2020 Update to the Hawaii Statewide Agricultural Land Use Baseline

Geospatial_Data_Presentation_Form: report and associated spatial data
Publication_Information:

Publication_Place: Honolulu, Hawaii
Publisher: Hawaii Department of Agriculture

Other_Citation_Details:

Report and spatial data are available at <https://hdoa.hawaii.gov/wp-content/uploads/2022/01/2020_Update_Ag_Baseline_all_Hawaiian_Islands_v5.pdf>.

Source_Scale_Denominator: 24000
Type_of_Source_Media: online report and GIS data
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20190101
Ending_Date: 20201231

Source_Currentness_Reference: publication date

Source_Citation_Abbreviation: HI Ag Baseline 2020
Source_Contribution:

Baseline agricultural land-use information used to support interpretation of land cover categories.

Process_Step:

Process_Description:

OVERVIEW: The HawaiiCropland Data Layer (HCDL) is the first-ever raster, geo-referenced, crop-specific land cover data layer for Hawaii. Unlike the 10-meter resolution CDL for CONUS or HCDL V1.x, which utilized 10-day image composites of surface reflectance data across multiple spectral bands and NDVI, crop classification for HCDL 2023, 2024 and 2025 V2.1 leveraged the 64-dimensional geospatial embeddings from the Satellite Embedding dataset developed by Google DeepMind's new geospatial AI model, AlphaEarth Foundations. This dataset was derived from multiple datastreams, including optical, radar, LiDAR, and other sources. Ground-reference data for crop type classification was developed based on satellite/aerial imagery interpretation and field surveys conducted by the University of Hawaii at Manoa beginning December 2023. Fieldwork covered the islands of Kauai, Oahu, Maui, Molokai, and the Big Island. HCDL 2025 was derived from 2025 embeddings using the SmileRandomForest classifier in Google Earth Engine, based on the 2025 ground-reference data. Since the embedding space is consistent across years, it can be used for condition change detection. Due to the limited groundreference data available in 2023, HCDL 2023 was derived from 2023 embeddings solely based on the trained 2024 models, thus reducing uncertainties caused by the subjective human-involved training data labeling process. The following citations contain additional information.

Li, Z., Chen, Q., Yang, Z., Chen, H., Lincoln, N.K., Cao, C., Mueller, R., and Willis, P., 2024, Innovative Crop Mapping in Hawaii: The First 10-Meter Resolution Cropland Data Layers for 2023, The 12th International Conference on Agro-Geoinformatics. Novi Sad, Serbia, 2024, pp. 1-6, 10.1109/Agro-Geoinformatics262780.2024.10660811.

Li, Z., Chen, Q., Yang, Z., Mueller, R., Willis, P., Chen, H., Lincoln, N.K., and Cao, C., 2026, Hawaii Cropland Data Layers (HCDL) v2.1: A Satellite Embedding Approach to Crop Mapping, IGARSS 2026 - 2026 IEEE International Geoscience and Remote Sensing Symposium, Washington D.C., United States, August 9 - 14, 2026.

PUBLIC RELEASE: The USDA NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2025
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA NASS, Spatial Analysis Research Section
Contact_Person: USDA NASS, Spatial Analysis Research Section staff

Contact_Address:

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Cloud_Cover: 0

Spatial_Data_Organization_Information:

Indirect_Spatial_Reference: Hawaii, United States
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 40485
Column_Count: 61603

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area as used by mrlc.gov (NLCD)
Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:

Abscissa_Resolution: 10
Ordinate_Resolution: 10

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000

Denominator_of_Flattening_Ratio: 298.257223563

Entity_and_Attribute_Information:

Overview_Description:

Entity_and_Attribute_Overview:

The 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. The Hawaii CDL aims to map major, specialty, and culturally significant crops in Hawaii. The "Other Exotic Fruits" category encompasses a variety of tropical fruits such as mango, litchi, longan, cacao, dragon fruit, jackfruit, breadfruit, and rambutan. The " Misc Vegs & Fruits " category includes tomato, pumpkin, turmeric, mustard, eggplant and others.

Entity_and_Attribute_Detail_Citation:

NOTE: Codes 93 to 106 are specific to the Hawaii CDL. All other categories and codes are standardized to the existing Continential United States version of the CDL.

 Data Dictionary: USDA National Agricultural Statistics Service, Hawaii 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: HCDL Land Cover Codes

 Categorization Code   Land Cover
           "1"       Corn
           "6"       Sunflower
          "45"       Sugarcane
          "46"       Sweet Potatoes
          "47"       Misc Vegs & Fruits
          "63"       Forest
          "72"       Citrus
          "82"       Developed
          "93"       Banana (Hawaii Only)
          "94"       Other Exotic Fruits (Hawaii Only)
          "95"       Coffee (Hawaii Only)
          "96"       Macadamia (Hawaii Only)
          "97"       Papaya (Hawaii Only)
          "98"       Pineapple (Hawaii Only)
          "99"       Commercial Forest (Hawaii Only)
         "100"       Taro (Hawaii Only)
         "101"       Sweet Basil (Hawaii Only)
         "102"       Coconut (Hawaii Only)
         "103"       Plumeria (Hawaii Only)
         "104"       Noni Fruit (Hawaii Only)
         "105"       Dracaena (Hawaii Only)
         "106"       Other Crops (Hawaii Only)
         "111"       Open Water
         "131"       Barren
         "152"       Shrubland
         "176"       Grassland/Pasture
         "215"       Avocados

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA NASS Customer Service
Contact_Person: USDA NASS Customer Service Staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5038-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Contact_Instructions:

Please visit the official website <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php> for distribution details. The Cropland Data Layer is available free for download at the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Resource_Description: 2025 Hawaii Cropland Data Layer
Distribution_Liability:

Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (SM.NASS.RDD.GIB@usda.gov) if technical questions arise in the use of the CDL. NASS maintains a Frequently Asked Questions (FAQ's) section at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Format_Name: GEOTIFF
Format_Version_Date: 2025
Format_Information_Content: GEOTIFF

Digital_Transfer_Option:

Online_Option:

Computer_Contact_Information:

Network_Address:

Network_Resource_Name:

<https://www.nass.usda.gov/Research_and_Science/Cropland/Release/>

Access_Instructions:

The data is available free for download at <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>.

Fees:

The data is available free for download at <https://www.nass.usda.gov/Research_and_Science/Cropland/Release/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. Distribution questions can be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Ordering_Instructions:

Technical_Prerequisites: Software capable of viewing GEOTIF (.tif) file formats.

Hawaii Cropland Data Layer

Metadata: