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At least 55 records · Page 3Linked to original sources
USGS2006

Hydrologic requirements of and consumptive ground-water use by riparian vegetation along the San Pedro River, Arizona

This study is a coordinated effort by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture, Agricultural Research Service (USDA ARS), and Arizona State University, with assistance from the U.S. Army Corps of Engineers, the University of Wyoming, and the University of Arizona. The specific objectives of the study were: to determine the water needs of riparian vegetation through the riparian growing season and throughout the SPRNCA to ensure its long-term ecological integrity; to quantify the total water use of riparian vegetation within the SPRNCA; and to determine the source of water used by key riparian plant species within the SPRNCA. To meet these objectives, the study was divided into three elements: (1) a characterization of the status and variability of hydrologic factors within the riparian system (USGS), (2) a riparian biohydrology study to relate spatial and temporal aspects of riparian changes and condition to the hydrologic variables (Arizona State University), and (3) a water-use evapotranspiration (ET) study to quantify annual consumptive ground-water use by riparian transpiration and direct evaporation from the stream channel (USDA ARS) in cooperation with the U.S. Army Corps of Engineers, the University of Wyoming, and the University of Arizona. Twenty-six sites within the SPRNCA were selected for collection of vegetation data from three primary streamflow regimes (perennial, intermittent-wet, intermittent-dry), which include the principal vegetation communities. Detailed hydrologic-condition data were collected at a subset of 16 of these sites, called the SPRNCA biohydrology sites. Water-use and water-source data were collected at a subset of 5 of the 16 biohydrology sites. Vegetation data also were collected at supplemental sites within the SPRNCA boundary in the Upper San Pedro Basin and in the Lower San Pedro Basin. In addition to information about vegetation and geomorphic conditions, hydrologic data collected at the 16 biohydrology sites were used to delineate 14 reaches that were internally homogenous in terms of streamflow hydrology (spatial intermittence of streamflow) and geomorphology (channel sinuosity and flood-plain width). Although this overall study consisted of three elements, the elements were closely coordinated to derive integrated results. Specifically, the connection between water demand, water availability, and riparian functioning represents a synthesis of the study elements. The effects of intra- and inter-annual as well as spatial variability of hydrologic and riparian factors were observed in each of the three study elements.

Arizona↗
USGS2006

Timing and Duration of Flow in Ephemeral Streams of the Sierra Vista Subwatershed of the Upper San Pedro Basin, Cochise County, Southeastern Arizona

Frequency, timing, and duration of streamflow were monitored in 20 ephemeral-stream channels across the Sierra Vista Subwatershed of the Upper San Pedro Basin, southeastern Arizona, during an 18-month period. One channel (Walnut Gulch) had Agricultural Research Service streamflow-gaging stations in place. The sediments of the remaining 19 ephemeral-stream channels were instrumented with multiple temperature loggers along the channel lengths. A thermograph-interpretation technique was developed in order to determine frequency, timing, and duration of streamflow in these channels. Streamflow onset was characterized by exceedance of a critical minimum drop in temperature within the channel sediments during any 15-minute interval, whereas streamflow cessation was identified by the local temperature minimum that immediately followed the critical temperature drop. All data for the 18-month period from December 1, 2000, to May 31, 2002, were analyzed in terms of monsoon (June 1 to September 19) and nonmonsoon (September 20 to May 31) periods. Nonmonsoon precipitation during the 2000-2002 study period (excludes October and November 2000) was 82 percent and 39 percent of the 30-year average, respectively, whereas monsoon precipitation during 2001 was 99 percent of the 30-year average. Ephemeral streamflow was detected at least once during the monitoring period at 87 percent of the monitoring sites (45 of the 52 sites that returned useful data; includes 4 streamflow-gaging stations). The summer monsoon period accounted for 82 percent of all streamflow events by number and 71 percent of all events by total streamflow duration. Nonmonsoon streamflow events peaked in number, total streamflow duration, and mean streamflow duration midway between the Huachuca Mountains and the San Pedro River on the west side of the subwatershed. These three streamflow parameters dropped off sharply about 10 kilometers from the mountain front. The number and total duration of nonmonsoon streamflows on the east side of the subwatershed trended downward with increased distance from the mountain fronts. Monsoon streamflow events were more evenly distributed across the subwatershed than nonmonsoon events, and the number and duration of streamflows generally trended upward with distance from the mountain fronts. Additional years of data are needed to determine whether these patterns are consistent year to year, or were due to randomness in the spatial distribution of precipitation. Streamflows in three ephemeral-stream channels were analyzed in detail. More than two-thirds of the streamflow events detected in each of these channels occurred at no more than one monitoring site along the channel length. In only one of the three channels-Garden Canyon-was a streamflow event detected at all logger sites along its length. Five temperature loggers provided data from urbanized areas, and these loggers detected streamflow more than 50 percent more often and of a duration nearly three times greater than did temperature loggers across the rural parts of the subwatershed. Because historical records do not indicate that more precipitation occurs in the urbanized area than in the rural areas, the increased frequency of flow detection in the urban area is attributed to an increase in runoff from the impervious surfaces throughout the urbanized area.

Scientific Investigations Report↗
USGS2011

Assessment of conservation practices in the Fort Cobb Reservoir watershed, southwestern Oklahoma

The Fort Cobb Reservoir watershed encompasses about 813 square kilometers of rural farm land in Caddo, Custer, and Washita Counties in southwestern Oklahoma. The Fort Cobb Reservoir and six stream segments were identified on the Oklahoma 1998 303(d) list as not supporting designated beneficial uses because of impairment by nutrients, suspended solids, sedimentation, pesticides, and unknown toxicity. As a result, State and Federal agencies, in collaboration with conservation districts and landowners, started conservation efforts in 2001 to decrease erosion and transport of sediments and nutrients to the reservoir and improve water quality in tributaries. The U.S. Department of Agriculture selected the Fort Cobb Reservoir watershed in 2003 as 1 of 14 benchmark watersheds under the Conservation Effectiveness Assessment Project with the objective of quantifying the environmental benefits derived from agricultural conservation programs in reducing inflows of sediments and phosphorus to the reservoir. In November 2004, the Biologic, Geographic, Geologic, and Water Disciplines of the U.S. Geological Survey, in collaboration with the Agricultural Research Service, Grazinglands Research Laboratory in El Reno, Oklahoma, began an interdisciplinary investigation to produce an integrated publication to complement this program. This publication is a compilation of 10 report chapters describing land uses, soils, geology, climate, and water quality in streams and the reservoir through results of field and remote sensing investigations from 2004 to 2007. The investigations indicated that targeting best-management practices to small intermittent streams draining to the reservoir and to the Cobb Creek subwatershed may effectively augment efforts to improve eutrophic to hypereutrophic conditions that continue to affect the reservoir. The three major streams flowing into the reservoir contribute nutrients causing eutrophication, but minor streams draining cultivated fields near the reservoir appeared to be disproportionate contributors of nutrients. Increasing conservation practices on small streams may have a greater effect in mitigating eutrophication in the reservoir than additional installation of such measures on the larger creeks.

Oklahoma↗
USGS2000

A Retrospective Analysis on the Occurrence of Arsenic in Ground-Water Resources of the United States and Limitations in Drinking-Water-Supply Characterizations

The Safe Drinking Water Act, as amended in 1996, requires the U.S. Environmental Protection Agency (USEPA) to review current drinking-water standards for arsenic, propose a maximum contaminant level for arsenic by January 1, 2000, and issue a final regulation by January, 2001. Quantification of the national occurrence of targeted ranges in arsenic concentration in ground water used for public drinking-water supplies is an important component of USEPA's regulatory process. Data from the U.S. Geological Survey (USGS) National Water Information System (NWIS) were used in a retrospective analysis of arsenic in the ground-water resources of the United States. The analysis augments other existing sources of data on the occurrence of arsenic collected in ground water at public water-supply systems.The USGS, through its District offices and national programs, has been compiling data for many years on arsenic concentrations collected from wells used for public water supply, research, agriculture, industry, and domestic water supply throughout the United States. These data have been collected for a variety of purposes ranging from simple descriptions of the occurrence of arsenic in local or regional ground-water resources to detailed studies on arsenic geochemistry associated with contamination sites. A total of 18,864 sample locations were selected from the USGS NWIS data base regardless of well type, of which 2,262 were taken from public water-supply sources. Samples with non-potable water (dissolved-solids concentration greater than 2,000 milligrams per liter and water temperature greater than 50o Celsius) were not selected for the retrospective analysis and other criteria for selection included the amount and type of ancillary data available for each sample. The 1,528 counties with sufficient data included 76 percent of all large public water-supply systems (serving more than 10,000 people) and 61 percent of all small public water-supply systems (serving more than 1,000 and less than 10,000 people) in the United States. The arsenic data were summarized for the selected counties by associating the arsenic concentrations measured in the ground-water resource with the numbers and sizes of public water-supply systems using ground water in those counties. Targeted arsenic concentrations of 1, 2, 5, 10, 20, and 50 ug/L were exceeded in the ground-water resource associated with 36, 25, 14, 8, 3, and 1 percent respectively of all public water-supply systems accounted for in the analysis.Contributions to uncertainty such as changes in sampling methods and changes in laboratory reporting appear to be less important to the national occurrence estimates than other factors such as temporal variability in arsenic concentrations at a given well, the types of wells sampled, and density and types of sampling locations. In addition, no attempt was made to quantify arsenic concentrations in relation to depth within aquifers. With these qualifications, the USGS data represent the ground-water resource in general and are not restricted to wells currently used for public drinking-water sources. In this way, the broad spatial extent, large number of water samples, and low detection limits used for the USGS data provide a unique source of information to determine where targeted concentrations of arsenic are likely to occur in the ground-water resources within much of the United States.These results indicate USGS data can be effectively used to augment national estimates of arsenic occurrence in the nation's ground-water resources if limitations are recognized. Existing estimates of the occurrence of arsenic in ground water that are used as a source of drinking water can be supplemented with the USGS arsenic concentration data when associated with the public water-supply data base. One such supplementary application is the additional insight gained by establishing relations between arsenic concentration data in the ground-water resource and small public wat

Water-Resources Investigations Report↗
USGS2020

Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

Remote Sensing↗
USGS2014

Unsaturated flow characterization utilizing water content data collected within the capillary fringe

An analysis is presented to determine unsaturated zone hydraulic parameters based on detailed water content profiles, which can be readily acquired during hydrological investigations. Core samples taken through the unsaturated zone allow for the acquisition of gravimetrically determined water content data as a function of elevation at 3 inch intervals. This dense spacing of data provides several measurements of the water content within the capillary fringe, which are utilized to determine capillary pressure function parameters via least-squares calibration. The water content data collected above the capillary fringe are used to calculate dimensionless flow as a function of elevation providing a snapshot characterization of flow through the unsaturated zone. The water content at a flow stagnation point provides an in situ estimate of specific yield. In situ determinations of capillary pressure function parameters utilizing this method, together with particle-size distributions, can provide a valuable supplement to data libraries of unsaturated zone hydraulic parameters. The method is illustrated using data collected from plots within an agricultural research facility in Wisconsin.

Air, Soil and Water Research↗
USGS1989

History of the Federal Interagency Sedimentation Project

Since 1939, the date of the Project's inception, the team has operated under the direction of two lead agencies - the U.S. Geological Survey and U.S. Army Corps of Engineers. The supporting agencies are the Agricultural Research Service, Bureau of Reclamation, U.S. Forest Service, Bureau of Land Management, Federal Highway Administration, and the Tennessee Valley Authority. Overall direction of the Project rests with the Subcommittee on Sedimentation, Interagency Advisory Committee on Water Data. The Project's goals focus on improving and maintaining the quality of fluvial sediment data by (1) developing sediment samplers, laboratory analyzers, and automatic gages (2) evaluating methods, (3) standardizing equipment and methods and (4) procuring, calibrating and selling equipment.

Conference Paper↗
USGS2021

Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing

USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper discusses the measurement of the intrinsic erosion and geotechnical properties of the soil for improved BSTEM results and is the first of two parts. Sampling and testing were conducted at select sites to obtain the soil stratigraphy and collect samples for estimation of engineering properties. Soil erodibility was measured using the Erosion Function Apparatus test, the Borehole Erosion Test, the Pocket Erodometer Test, and the mini-Jet Erosion Test. Critical evaluation of previously existing datasets and the collection of these new datasets helped to provide better definition of the range in erosion parameters for improved probabilistic erosion estimates using BSTEM for risk-informed levee erosion countermeasure site selection and design.

California↗
USGS2021

Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion

USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper, the second of two parts, addresses processing the collected data to inform inputs for probabilistic bank erosion estimates in BSTEM. Measuring the intrinsic soil properties for BSTEM is discussed in part one. Soil critical shear stress and soil erodibility coefficients were calibrated by Unified Soil Classification soil type to observed erosion on the American River. Adjustments were made in the probability density functions for these parameters to reflect field-measured variability and carry forward the reduction in error achieved during calibration. The resulting calibrated values were tested at additional sites, validating the resulting critical shear stress and soil erodibility coefficient values and probability density functions for more robust probabilistic bank erosion estimates using BSTEM.

California↗
USGS2013

Electron donor concentrations in sediments and sediment properties at the agricultural chemicals team research site near New Providence, Iowa, 2006-07

The concentrations of electron donors in aquifer sediments are important to the understanding of the fate and transport of redox-sensitive constituents in groundwater, such as nitrate. For a study by the U.S. Geological Survey National Water-Quality Assessment Program, 50 sediment samples were collected from below the water table from 11 boreholes at the U.S. Geological Survey Agricultural Chemicals Team research site near New Providence, Iowa, during 2006-07. All samples were analyzed for gravel, sand (coarse, medium, and fine), silt, clay, Munsell soil color, inorganic carbon content, and for the following electron donors: organic carbon, ferrous iron, and inorganic sulfide. A subset of 14 sediment samples also was analyzed for organic sulfur, but all of these samples had concentrations less than the method detection limit; therefore, the presence of this potential electron donor was not considered further. X-ray diffraction analyses provided important semi-quantitative information of well-crystallized dominant minerals within the sediments that might be contributing electron donors.

Iowa↗
USGS2002

Nitrate in aquifers beneath agricultural systems

Research from several regions of the world provides spatially anecdotal evidence to hypothesize which hydrologic and agricultural factors contribute to groundwater vulnerability to nitrate contamination. Analysis of nationally consistent measurements from the U.S. Geological Survey's NAWOA program confirms these hypotheses for a substantial range of agricultural systems. Shallow unconfined aquifers are most susceptible to nitrate contamination associated with agricultural systems. Alluvial and other unconsolidated aquifers are the most vulnerable and shallow carbonate aquifers provide a substantial but smaller contamination risk. Where any of these aquifers are overlain by permeable soils the risk of contamination is larger. Irrigated systems can compound this vulnerability by increasing leaching facilitated by additional recharge and additional nutrient applications. The agricultural system of corn, soybeans, and hogs produced significantly larger concentrations of groundwater nitrate than all other agricultural systems, although mean nitrate concentrations in counties with dairy, poultry, cattle and grains, and horticulture systems were similar. If trends in the relation between increased fertilizer use and groundwater nitrate in the United States are repeated in other regions of the world, Asia may experience increasing problems because of recent increases in fertilizer use. Groundwater monitoring in Western and Eastern Europe as well as Russia over the next decade may provide data to determine if the trend in increased nitrate contamination can be reversed. If the concentrated livestock trend in the United States is global, it may be accompanied by increasing nitrogen contamination in groundwater. Concentrated livestock provide both point sources in the confinement area and intense non-point sources as fields close to facilities are used for manure disposal. Regions where irrigated cropland is expanding, such as in Asia, may experience the greatest impact of this practice.

Conference Paper↗
USGS2026

Assessing pesticide application contributions of per- and polyfluoroalkyl substances (PFAS) in agricultural streams

Research has documented per-and polyfluoroalkyl substances (PFAS) to be prevalent in streams impacted by urban sources; less is known regarding PFAS contributions via pesticide applications: PFAS coming from pesticide containers and “inert” ingredients not listed on pesticide formulation labels. To better understand co-occurrence of pesticides and PFAS in agricultural streams, 10 streams were sampled in two agricultural valleys in northern California, USA during two periods of pesticide application (May and July of 2024). Water samples were analyzed for 183 pesticides and 57 PFAS. Sixty pesticides were detected: 23 herbicides, 18 insecticides, 18 fungicides, and 1 synergist. Overall detection frequencies (DF) ranged from 0 to 100%, and concentrations ranged from non-detect (ND) to 782 ng/L (fluxapyroxad). Fourteen PFAS were detected: 6 perfluoroalkyl carboxylates, 5 perfluoroalkyl sulfonates, 2 perfluoroalkyl acid precursors, and 1 perfluoroalkyl phosphinic acid with DFs ranging from 0 to 60% (PFOA) and concentrations ranging from ND to 20 ng/L (PFBA). Co-occurrence of PFAS and pesticides was observed in 80% of samples. Adopting the global definition of PFAS (includes fluorinated pesticides), pesticide applications are a likely source of instream PFAS. Examination of total organic fluorine and transformations to terminal and ultra-short PFAS could further elucidate pesticide contributions to environmental PFAS.

California↗
USGS2023

Comparing NISAR (using Sentinel-1), USDA/NASS CDL, and ground truth crop/non-crop areas in an urban agricultural region

A general limitation in assessing the accuracy of land cover mapping is the availability of ground truth data. At sites where ground truth is not available, potentially inaccurate proxy datasets are used for sub-field-scale resolution investigations at large spatial scales, i.e., in the Contiguous United States. The USDA/NASS Cropland Data Layer (CDL) is a popular agricultural land cover dataset due to its high accuracy (>80%), resolution (30 m), and inclusions of many land cover and crop types. However, because the CDL is derived from satellite imagery and has resulting uncertainties, comparisons to available in situ data are necessary for verifying classification performance. This study compares the cropland mapping accuracies (crop/non-crop) of an optical approach (CDL) and the radar-based crop area (CA) approach used for the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) L- and S-band mission but using Sentinel-1 C-band data. CDL and CA performance are compared to ground truth data that includes 54 agricultural production and research fields located at USDA’s Beltsville Agricultural Research Center (BARC) in Maryland, USA. We also evaluate non-crop mapping accuracy using twenty-six built-up and thirteen forest sites at BARC. The results show that the CDL and CA have a good pixel-wise agreement with one another (87%). However, the CA is notably more accurate compared to ground truth data than the CDL. The 2017–2021 mean accuracies for the CDL and CA, respectively, are 77% and 96% for crop, 100% and 94% for built-up, and 100% and 100% for forest, yielding an overall accuracy of 86% for the CDL and 96% for CA. This difference mainly stems from the CDL under-detecting crop cover at BARC, especially in 2017 and 2018. We also note that annual accuracy levels varied less for the CA (91–98%) than for the CDL (79–93%). This study demonstrates that a computationally inexpensive radar-based cropland mapping approach can also give accurate results over complex landscapes with accuracies similar to or better than optical approaches.

Maryland↗
USGS2025

USGS—An Unparalleled Scientific Asset

The U.S. Geological Survey (USGS) delivers information critical to powering our economy, managing our natural resources, and keeping Americans safe and healthy. 1 Mapping the Nation $21B Geologic maps save users an estimated 15% in annual costs: a value of between $14B and $21B. $25.6B in annual value to users of imagery from Landsat satellites, which were codeveloped by NASA and the USGS and operated through their lifespans by the USGS. $13.5B in annual benefits is generated by the USGS's 3D Elevation Program. Securing America’s Energy Independence 44% USGS-identified undiscovered geothermal energy is equal to 44% of current U.S. electricity generation. 29.4B barrels of oil and 391.6 trillion cubic feet of gas in recoverable resources are available on U.S. public lands based on USGS assessments. Protecting Americans’ Health and Safety $424B in recent wildland fire damages highlight the need for USGS fire science, which supports efforts to protect communities and reduce risk. USGS earthquake, volcano, landslide, and coastal hazard monitoring and information save lives and minimize costs; for example, $2.8M can be saved because of USGS enhanced information about a Mauna Loa eruption. $4.5B is the estimated cost of annual flooding. Through a network of over 11,885 streamgages, the USGS supports public safety and enables forecasts, early warning systems, and management actions that protect lives and property. Supporting National Security $3.1B The USGS identified a $3.1B risk to the American economy if China restricts gallium imports. This is one example underscoring the importance of the USGS mapping critical minerals, investigating supply chains, and producing the Nation’s critical minerals list. Enhancing Our Lands and Waters $21B in estimated annual costs results from invasive species. The USGS’s invasive species research informs approaches used to reduce their effects on agriculture, water infrastructure, disease transmission, fisheries, and outdoor recreation. USGS innovations support early warnings for harmful algal blooms—over $2M in yearly benefits are provided to Kansas alone. $45B USGS science informs the management of big game (such as deer and elk). The big-game hunting industry contributes $45B to the U.S. economy. Fostering American Prosperity $4.1T Mineral commodities are necessary for the $4.1T in value added to the GDP by major industries that consume processed mineral materials and employ 1 million workers. Because of this, USGS data on mineral supply, demand, and trade are highly valued. 45,000 metric tons Rare earths power the growing technology economy, including cell phones, electric vehicles, and medical devices. For over 70 years, USGS work has supported the discovery of rare earth resources in California’s Mountain Pass area, which produced 45,000 metric tons of rare earth concentrates in 2024—over 11% of the global supply. Guarding American Food Security $70.2B USGS science informs early warning systems and management strategies to mitigate disease outbreaks in agriculture—critical research on highly pathogenic avian influenza, for example, helps safeguard the $70B value in poultry and egg production. $11.8B USGS groundwater tools are vital for agriculture; for example, in the Mississippi Alluvial Plain, 65% of farming relies on groundwater to support its $11.8B annual industry. 1 Values throughout are given in billions (B), millions (M), and trillions (T) of U.S. dollars. GDP is “Gross Domestic Product.” Percentages are shown as %.

General Information Product↗
USGS2023

Atmospheric correction intercomparison of hyperspectral and multispectral imagery over agricultural study sites

In this research effort we assess the performance of atmospheric correction-based surface reflectance (SR) retrievals from two satellite image sources, one with very high spatial resolution (VHR) (<5-m) and the other high spectral resolution (~10-nm). The VHR images are from MAXARs WorldView-3 (WV3) satellite and the high spectral resolution images are from Agenzia Spaziale Italianas (ASI) PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite. We use various atmospheric correction (AC) tools to provide intercomparisons of both AC tools and image source SR estimates. The AC tools we evaluated include Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) within ENVI version 4.7, MODerate resolution atmospheric TRANsmission (MODTRAN) versions 5.3.3 and 6.0, and ASIs Level-2D correction for PRISMA imagery. Prior to correcting WV3 and PRISMA imagery to SR, we performed manual geometric corrections of imagery as both image sources were found to lack consistent georegistration. We performed comparisons at two study sites in Maryland, USA, including the United States Department of Agriculture Beltsville Agricultural Research Center (BARC) and an agricultural study site on Marylands Eastern Shore region. For the BARC site, we used WV3 imagery acquired on 2022-04-02 and PRISMA imagery acquired on 2022-04-28, focusing on evaluation of AC tool SR retrieval performance for each image source separately due to large time differences in image acquisitions where SR values are likely impacted by changing field conditions. For the Eastern Shore site, WV3 imagery was acquired on 2022-05-18 and 2022-05-30, and PRISMA imagery was acquired on 2022-05-21, allowing for quantitative evaluation of both AC tool performance and intercomparison between WV3 and PRISMA imagery. Having WV3 imagery acquired before and after PRISMA imagery allows for interpretation of major changes in field conditions and thus, identification of fields to exclude from intercomparisons. For intercomparison assessments, we computed relative percent difference (RPD) between the AC tool SR retrievals. For image source comparisons, 4-m WV3 pixels were resampled to 30-m PRISMA pixels after which 30-m WV3 bands and PRISMA spectra were compared to one another visually for both study sites. To provide rigorous SR retrieval intercomparisons between image sources, PRISMA spectra were resampled to WV3-equivalent bands for RPD computation for the Eastern Shore site. In addition to the SR retrieval intercomparisons between the AC tools, we carry out a quasi-validation where we retrieve fractional crop residue cover (fR) from the satellite image sources by calculating established spectral indices (SIs) and calibrating SIs with ground-measured fR acquired within several days of satellite overpasses. These SIs include the Cellulose Absorption Index (CAI) (Nagler et al. 2000), Shortwave Infrared Normalized Difference Residue Index (SINDRI) (Serbin et al. 2009), Lignin-Cellulose Absorption Index (LCAI) (Daughtry et al. 2005), and Lignin-Cellulose Peak Center Difference Index (LCPCDI) (Hively et al. 2021) 1-4. The most accurate crop residue SIs are generally based on shortwave infrared (SWIR) reflectance bands ranging from 2000 nm to 2400 nm that measure dry vegetation lignocellulose absorption features at 2100 and 2300 nm 1-5. For instance, the CAI identifies a 2100 nm cellulose absorption feature with a central band positioned on this feature, and two spectrally adjacent bands at 2040 and 2210 nm, while the LCAI identifies the 2300 nm lignin absorption feature compared to bands at 2165 and 2210 nm. Particular focus on intercomparisons for the SWIR region is critical as atmospheric water, carbon dioxide, and methane impact accurate SR retrieval as shown in Figure 1.a. Our final analysis concludes with the selection of the top-performing AC approach between the WV3 and PRISMA imagery (as indicated by low SR RPD) and then compares PRIMSA and 30-m WV3 imagery with original 4-m WV3 imagery to assess the degree to which spatial resolution impacts the retrieval of fR. Figure 1 provides a comparative example of WV3 and PRISMA imagery used to compute SINDRI which is then calibrated to fR using second order polynomial equations from Hively et al. (2018) 6. Figure 1 fR calibrations will be updated with newly acquired ground survey data from May 2022 to further improve the accuracy of image source and AC tool intercomparisons.

Conference Paper↗
USGS1992

Hydrologic research at the Princeton, Minnesota management systems evaluation area

The Management Systems Evaluation Area (MSEA) program is part of a multi-scale, inter-agency initiative to evaluate the effects of agricultural management systems on water quality. The program resulted from the integration of the U.S. Department of Agriculture (USDA) Research Plan for Water Quality and the U.S Geological Survey (USGS) Mid-Continent Herbicide Initiative and is part of the President's Water Quality Initiative. The mid-continental corn belt was selected for study because it is an area where about 60% of the Nation's pesticides and nitrogen fertilizers are used.

Minnesota↗
USGS2013

Nitrous oxide emissions from cropland: a procedure for calibrating the DayCent biogeochemical model using inverse modelling

DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional “trial and error” approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.

Water, Air, & Soil Pollution↗
USGS2019

Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.

conterminous United States↗