Results for “Precision Agriculture”

USGS2014

Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications

An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye (Secale cereale) was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass ( ${mbi {r}} = 0.58$ ), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass ( ${mbi {r}} = 0.73$ ), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content ( ${mbi {r}} = -0.72$ ). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be analyzed as single plots along field transects.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2016

The 3D elevation program - Precision agriculture and other farm practices

A founding motto of the Natural Resources Conservation Service (NRCS), originally the Soil Conservation Service (SCS), explains that “If we take care of the land, it will take care of us.” Digital elevation models (DEMs; see fig. 1) are derived from light detection and ranging (lidar) data and can be processed to derive values such as slope angle, aspect, and topographic curvature. These three measurements are the principal parameters of the NRCS LidarEnhanced Soil Survey (LESS) model, which improves the precision of soil surveys, by more accurately displaying the slopes and soils patterns, while increasing the objectivity and science in line placement. As combined resources, DEMs, LESS model outputs, and similar derived datasets are essential for conserving soil, wetlands, and other natural resources managed and overseen by the NRCS and other Federal and State agencies.

Fact Sheet↗

USGS2022

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400–1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have four multispectral broadbands (MBB) with 3–4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72–100% and 75–100%, respectively, for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56–100% and 61–100%, respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65–100% and 60–94%, respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2016

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Geoderma↗

USGS2021

System characterization report on the Gaofen-1

Executive Summary This report addresses system characterization of Gaofen-1 and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2020. These reports present the detail methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at http://www.cnsageo.com/#/detailIndex?secondIndex=2&id=3&code=8 . The Earth Resources Observation and Science Cal/Val Center of Excellence System Characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.

Open-File Report↗

USGS2024

System characterization report on the Gaofen-6

Executive Summary Gaofen-6 represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellites in this series is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.” The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances of Gaofen-6. Results of these analyses indicate that Gaofen-6 has an interior geometric performance root mean square error ranging from 2.84 meters (m; 0.18 pixel) to 7.42 m (0.46 pixel) in easting and from 2.84 m (0.18 pixel) to 11.57 m (0.72 pixel) in northing in band-to-band registration, an exterior geometric performance root mean square error ranging from 154.50 m (8.80 pixels) in easting to 14.65 m (0.80 pixel) in northing in comparison to a corresponding Sentinel-2 scene, a radiometric performance ranging from 0.018 to 0.055 (in offset) and from 0.620 to 0.858 (in slope), and a spatial performance ranging from 2.10 to 2.30 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency ranging from 0.040 to 0.055.

Open-File Report↗

USGS2013

The benefits of improved national elevation data

This article describes how the National Enhanced Elevation Assessment (NEEA) has identified substantial benefits that could come about if improved elevation data were publicly available for current and emerging applications and business uses such as renewable energy, precision agriculture, and intelligent vehicle navigation and safety. In order to support these diverse needs, new national elevation data with higher resolution and accuracy are needed. The 3D Elevation Program (3DEP) initiative was developed to meet the majority of these needs and it is expected that 3DEP will result in new, unimagined information services that would result in job growth and the transformation of the geospatial community. Private-sector data collection companies are continuously evolving sensors and positioning technologies that are needed to collect improved elevation data. An initiative of this scope might also provide an opportunity for companies to improve their capabilities and produce even higher data quality and consistency at a pace that might not have otherwise occurred.

Photogrammetric Engineering and Remote Sensing↗

USGS2025

Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud

Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.

conterminous United States↗

USGS2013

The 3D Elevation Program: summary for Wisconsin

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Wisconsin, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Wisconsin↗

USGS2013

The 3D Elevation Program: summary for Rhode Island

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Rhode Island, elevation data are critical for flood risk management, natural resources conservation, coastal zone management, sea level rise and subsidence, agriculture and precision farming, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative (Snyder, 2012a,b), managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Rhode Island↗

USGS2013

The 3D Elevation Program: summary for Colorado

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Colorado, elevation data are critical for natural resources conservation, agriculture and precision farming, infrastructure and construction management, flood risk management, geologic resource assessment and hazards mitigation, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Colorado↗

USGS2013

The 3D Elevation Program: summary for Minnesota

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Minnesota, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, coastal zone management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Minnesota↗

USGS2013

The 3D Elevation Program: summary for Virginia

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the Commonwealth of Virginia, elevation data are critical for urban and regional planning, natural resources conservation, flood risk management, agriculture and precision farming, resource mining, infrastructure and construction management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Virginia↗

USGS2013

The 3D Elevation Program: summary for Idaho

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Idaho, elevation data are critical for agriculture and precision farming, natural resources conservation, infrastructure and construction management, geologic resource assessment and hazard mitigation, flood risk management, forest resources management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features. The Idaho LiDAR Consortium provides statewide collaboration and data sharing mechanisms that can be used as a resource by State and Federal partners implementing the 3DEP initiative.

Idaho↗

USGS2013

The 3D Elevation Program: summary for California

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of California, elevation data are critical for infrastructure and construction management; natural resources conservation; flood risk management; wildfire management, planning, and response; agriculture and precision farming; geologic resource assessment and hazard mitigation; and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

California↗

USGS2013

The 3D Elevation Program: summary for Washington

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Washington, elevation data are critical for natural resources conservation, agriculture and precision farming, infrastructure and construction management, flood risk management, geologic resource assessment and hazards mitigation, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Washington↗

USGS2014

The 3D Elevation Program: Summary for South Dakota

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of South Dakota, elevation data are critical for agriculture and precision farming, natural resources conservation, water supply and quality, infrastructure and construction management, flood risk management, geologic resource assessment and hazard mitigation, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment (NEEA; Dewberry, 2011) evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

South Dakota↗

USGS2013

The 3D Elevation Program: summary for Texas

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Texas, elevation data are critical for natural resources conservation; wildfire management, planning, and response; flood risk management; agriculture and precision farming; infrastructure and construction management; water supply and quality; and other business uses. Today, high-quality light detection and ranging (lidar) data are the source for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Texas↗