United States of America agriculture data

Napa Valley American Viticultural Area (AVA) This layer is composed of a polygon that depicts the land in Napa VALLEY that is part of the Napa Valley Viticultural Area

The Pasture feature class designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data. The data will be used to create permit maps, field working maps and any other logical uses that the internal and external public may determine this information is helpful for. Pasture is a feature class in the Rangeland Management data set. It represents the area boundaries of livestock grazing pastures. The area corresponds to tabular data in the RIMS (Rangeland Information Management System). See all details of this USFS Enterprise Content: https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-colorado-grazing-allotments/about

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

These data display the locations of Cityworks tree planting work orders. The work orders show the locations of tree plantings planned during the current and next two fiscal years.

Farmland leaseholds within Peoria County, Illinois.

Update Frequency: Daily Summary: Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain. The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products. SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model. The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product. Suggested Use: This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values. Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade. The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from the top four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200cm. The 0-100 cm cumulative layer adjusts more slowly to precipitation episodes or the lack thereof compared to the 0-10 cm and 0-40 cm percentiles. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper 0-10 cm and 10-40 cm layers into the next model layer at 40-100 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the 0-100 cm cumulative layer to respond on the order of weeks to months (or longer, especially during dry periods), depending on the soil classification and soil responsiveness. Data Caveats: The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues. Data Visualization: The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies as follows: Further Reference: NASA SPoRT project page: https://weather.ndc.nasa.gov/sport/ Real-time SPoRT-LIS viewer: https://weather.ndc.nasa.gov/sport/viewer/?dataset=lis_conus Daily animations of experimental 2-week forecasts of SPoRT-LIS soil moisture percentiles: https://geo.nsstc.nasa.gov/SPoRT/modeling/lis/conus3km/forecasts/ NASA Land Information System project page: https://lis.gsfc.nasa.gov/ U.S. Drought Monitor page: https://droughtmonitor.unl.edu/ Esri REST Endpoint: See URL Section on right side of page

The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircraft and drones), and ground based hyperspectral imaging spectroscopy. The GHISA for the Conterminous United States (GHISACONUS) Version 1 product provides dominant crop data in different growth stages for various agroecological zones (AEZs) of the United States. The GHISA hyperspectral library of the five major agricultural crops (e.g., winter wheat, rice, corn, soybeans, and cotton) for CONUS was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired from 2008 through 2015 from different AEZs of CONUS using the United States Department of Agriculture (USDA) Cropland Data Layer (CDL) as reference data. GHISACONUS is comprised of seven AEZs throughout the United States covering the major agricultural crops in six different growth stages: emergence/very early vegetative (Emerge VEarly), early and mid vegetative (Early Mid), late vegetative (Late), critical, maturing/senescence (Mature Senesc), and harvest. The crop growth stage data were derived using crop calendars generated by the Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison. Provided in the CSV file is the spectral library including image information, geographic coordinates, corresponding agroecological zone, crop type labels, and crop growth stage labels for the United States.

This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, branch, etc.). It depicts core areas of agriculture (cultivated crops), core fragmentation, and patterns of connectivity among core patches. Agriculture is treated as foreground in this dataset; cultivated crops are separated from the all other land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircraft and drones), and ground based hyperspectral imaging spectroscopy. The GHISA spectral library for California’s Central Valley (CCV) derived from German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) (GHISACCVDESIS) Version 1 product provides information for nine crop classes: cotton, grapes, hay, tomatoes, tree crops, winter wheat, other crops, fallow, and other. GHISACCVDESIS was developed using 75 DESIS hyperspectral images of California’s Central Valley acquired during the month of August for years 2021 through 2023. The United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) was used as reference data. The spectral library is provided in an Excel spreadsheet, which includes unique sample identifier, sample location (latitude and longitude), sensor product, acquisition month and year, reflectance spectra in percent surface reflectance, and crop class label for each sample point within California’s Central Valley.

Update Frequency: Daily Summary: Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain. The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products. SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model. The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product. Suggested Use: This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values. Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade. The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from the top four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200cm. The 0-100 cm cumulative layer adjusts more slowly to precipitation episodes or the lack thereof compared to the 0-10 cm and 0-40 cm percentiles. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper 0-10 cm and 10-40 cm layers into the next model layer at 40-100 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the 0-100 cm cumulative layer to respond on the order of weeks to months (or longer, especially during dry periods), depending on the soil classification and soil responsiveness. Data Caveats: The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues. Data Visualization: The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies as follows: Further Reference: NASA SPoRT project page: https://weather.ndc.nasa.gov/sport/ Real-time SPoRT-LIS viewer: https://weather.ndc.nasa.gov/sport/viewer/?dataset=lis_conus U.S. Drought Monitor page: https://droughtmonitor.unl.edu/ Esri REST Endpoint: See URL Section on right side of page

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web service depict ecosystem services at the census block group level for all EnviroAtlas communities. These layers illustrate the ecosystems and natural resources that are associated with clean air, clean and plentiful water, natural hazard mitigation, climate stabilization, recreation, culture, and aesthetics, and biodiversity conservation, and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).