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USGS2018

Current research in land, water, and agroecosystems: ASABE journals 2017 year in review

This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals (Transactions of the ASABE and Applied Engineering in Agriculture) in 2017. This article reviews the context, scope, and key results of the published articles and perhaps more importantly recommends areas for increased research attention. Experimental and modeling advances were described in hydrology, agroecosystems, climate-change effects, soil erosion, irrigation, drainage, forest resources, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling to Optimize Water Use, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land and water resources, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to grow its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. In this vane, recommendations for future research direction are discussed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results.

Transactions of the ASABE↗

USGS2004

Developing index maps of water-harvest potential in Africa

The food security problem in Africa is tied to the small farmer, whose subsistence farming relies heavily on rain-fed agriculture. A dry spell lasting two to three weeks can cause a significant yield reduction. A small-scale irrigation scheme from small-capacity ponds can alleviate this problem. This solution would require a water harvest mechanism at a farm level. In this study, we looked at the feasibility of implementing such a water harvest mechanism in drought prone parts of Africa. A water balance study was conducted at different watershed levels. Runoff (watershed yield) was estimated using the SCS curve number technique and satellite derived rainfall estimates (RFE). Watersheds were delineated from the Africa-wide HYDRO-1K digital elevation model (DEM) data set in a GIS environment. Annual runoff volumes that can potentially be stored in a pond during storm events were estimated as the product of the watershed area and runoff excess estimated from the SCS Curve Number method. Estimates were made for seepage and net evaporation losses. A series of water harvest index maps were developed based on a combination of factors that took into account the availability of runoff, evaporation losses, population density, and the required watershed size needed to fill a small storage reservoir that can be used to alleviate water stress during a crop growing season. This study presents Africa-wide water-harvest index maps that could be used for conducting feasibility studies at a regional scale in assessing the relative differences in runoff potential between regions for the possibility of using ponds as a water management tool. ?? 2004 American Society of Agricultural Engineers.

Applied Engineering in Agriculture↗

USGS2018

Satellite psychrometric formulation of the operational simplified surface energy balance (SSEBop) model for quantifying and mapping evapotranspiration

Remote sensing-based evapotranspiration (ET) can be derived using various methods, from soil moisture accounting to vegetation-index based approaches to simple and complex surface energy balance techniques. Due to the complexity of fully representing and parameterizing ET sub-processes, different models tend to diverge in their estimations. However, most models appear to provide reasonable estimations that can meet user requirements for seasonal water use estimation and drought monitoring. One such model is the Operational Simplified Surface Energy Balance (SSEBop). This study presents a formulation of the SSEBop model using the psychrometric principle for vapor pressure/relative humidity measurements where the “dry-bulb” and “wet-bulb” equivalent readings can be obtained from satellite-based land surface temperature estimates. The difference in temperature between the dry (desired location) and wet limit (reference value) is directly correlated to the soil-vegetation composite moisture status (surface humidity) and thus producing a fractional value (0-1) to scale the reference ET. The reference ET is independently calculated using available weather data through the standardized Penman-Monteith equation. Satellite Psychrometric Approach (SPA) explains the SSEBop model more effectively than the energy balance principle because SSEBop does not solve all terms of the surface energy balance such as sensible and ground-heat fluxes. The SPA explanation demonstrates the psychrometric constant for the air can be readily adapted to a comparable constant for the surface, thus allowing the creation of a “surface” psychrometric constant that is unique to a location and day-of-year. This new surface psychrometric constant simplifies the calculation and explanation of satellite-based ET for several applications in agriculture and hydrology. The SPA formulation of SSEBop was found to be an enhancement of the ET equation formulated in 1977 by pioneering researchers. With only two key parameters, improved model results can be obtained using a one-time calibration for any bias correction. The model can be set up quickly for routine monitoring and assessment of ET at landscape scales and beyond.

Arizona↗

USGS2011

Agricultural herbicide transport in a first-order intermittent stream, Nebraska, USA

The behavior of herbicides in surface waters is a function of many variables, including scale of the watershed, physical and chemical properties of the herbicide, physical and chemical properties of the soil, rainfall intensity, and time of year. In this study, the transport of 6 herbicides and 12 herbicide degradates was examined during the 2004 growing season in an intermediate-scale agricultural watershed (146 ha) that is drained by a first-order intermittent stream, and the mass load for each herbicide in the stream was estimated. The herbicide load during the first week of storm events after application ranged from 17% of annual load for trifluralin to 84% of annual load for acetochlor. The maximum weekly herbicide load in the stream was generally within the first 3 weeks after application for those compounds that were applied within the watershed during 2004, and later for herbicides not applied within the watershed during 2004 but still detected in the stream. The apparent dominant mode of herbicide transport in the stream-determined by analysis amongst herbicide and conservative ion concentrations at different points in the hydrograph and in base flow samples-was either overland runoff or shallow subsurface flow, depending on the elapsed time after application and type of herbicide. The load as a percentage of use (LAPU) for the parent compounds in this study was similar to literature values for those compounds applied by the farmer within the watershed, but smaller for those herbicides that had rainfall as their only source within the watershed.

Nebraska↗

USGS2003

Using regression methods to estimate stream phosphorus loads at the Illinois River, Arkansas

The development of total maximum daily loads (TMDLs) requires evaluating existing constituent loads in streams. Accurate estimates of constituent loads are needed to calibrate watershed and reservoir models for TMDL development. The best approach to estimate constituent loads is high frequency sampling, particularly during storm events, and mass integration of constituents passing a point in a stream. Most often, resources are limited and discrete water quality samples are collected on fixed intervals and sometimes supplemented with directed sampling during storm events. When resources are limited, mass integration is not an accurate means to determine constituent loads and other load estimation techniques such as regression models are used. The objective of this work was to determine a minimum number of water-quality samples needed to provide constituent concentration data adequate to estimate constituent loads at a large stream. Twenty sets of water quality samples with and without supplemental storm samples were randomly selected at various fixed intervals from a database at the Illinois River, northwest Arkansas. The random sets were used to estimate total phosphorus (TP) loads using regression models. The regression-based annual TP loads were compared to the integrated annual TP load estimated using all the data. At a minimum, monthly sampling plus supplemental storm samples (six samples per year) was needed to produce a root mean square error of less than 15%. Water quality samples should be collected at least semi-monthly (every 15 days) in studies less than two years if seasonal time factors are to be used in the regression models. Annual TP loads estimated from independently collected discrete water quality samples further demonstrated the utility of using regression models to estimate annual TP loads in this stream system.

Arkansas↗

USGS2019

Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert

Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (K e ) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (K ss ) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that K e parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of K ss or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent K ss or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of K ss , and, ultimately, arid rangeland soil erosion simulation.

New Mexico↗

USGS2009

Lysimetric evaluation of simplified surface energy balance approach in the Texas high plains

Numerous energy balance (EB) algorithms have been developed to make use of remote sensing data to estimate evapotranspiration (ET) regionally. However, most EB models are complex to use and efforts are being made to simplify procedures mainly through the scaling of reference ET. The Simplified Surface Energy Balance (SSEB) is one such method. This approach has never been evaluated using measured ET data. In this study, the SSEB approach was applied to 14 Landsat TM images covering a major portion of the Southern High Plains that were acquired during 2006 and 2007 cropping seasons. Performance of the SSEB was evaluated by comparing estimated ET with measured daily ET from four large monolithic lysimeters at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. Statistical evaluation of results indicated that the SSEB accounted for 84% of the variability in the measured ET values with a slope and intercept of 0.75 and 1.1 mm d -1 , respectively. Considering the minimal amount of ancillary data required and excellent performance in predicting daily ET, the SSEB approach is a promising tool for mapping ET in the semiarid Texas High Plains and in other parts of the world with similar hydro-climatic conditions.

Applied Engineering in Agriculture↗

USGS2009

Lysimetric Evaluation of Simplified Surface Energy Balance Approach in the Texas High Plains

Numerous energy balance (EB) algorithms have been developed to make use of remote sensing data to estimate evapotranspiration (ET) regionally. However, most EB models are complex to use and efforts are being made to simplify procedures mainly through the scaling of reference ET. The Simplified Surface Energy Balance (SSEB) is one such method. This approach has never been evaluated using measured ET data. In this study, the SSEB approach was applied to fourteen Landsat TM images covering a major portion of the Southern High Plains that were acquired during 2006 and 2007 cropping seasons. Performance of the SSEB was evaluated by comparing estimated ET with measured daily ET from four large monolithic lysimeters at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. Statistical evaluation of results indicated that the SSEB accounted for 84% of the variability in the measured ET values with a slope and intercept of 0.75 and 1.1 mm d-1, respectively. Considering the minimal amount of ancillary data required and excellent performance in predicting daily ET, the SSEB approach is a promising tool for mapping ET in the semiarid Texas High Plains and in other parts of the world with similar hydro-climatic conditions.

Applied Engineering in Agriculture↗

USGS2022

Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R 2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R 2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R 2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R 2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.

Remote Sensing of Environment↗

USGS2013

Estimating groundwater recharge

Understanding groundwater recharge is essential for successful management of water resources and modeling fluid and contaminant transport within the subsurface. This book provides a critical evaluation of the theory and assumptions that underlie methods for estimating rates of groundwater recharge. Detailed explanations of the methods are provided - allowing readers to apply many of the techniques themselves without needing to consult additional references. Numerous practical examples highlight benefits and limitations of each method. Approximately 900 references allow advanced practitioners to pursue additional information on any method. For the first time, theoretical and practical considerations for selecting and applying methods for estimating groundwater recharge are covered in a single volume with uniform presentation. Hydrogeologists, water-resource specialists, civil and agricultural engineers, earth and environmental scientists and agronomists will benefit from this informative and practical book. It can serve as the primary text for a graduate-level course on groundwater recharge or as an adjunct text for courses on groundwater hydrology or hydrogeology.

Book↗

USGS2005

Upper Auglaize watershed AGNPS modeling project

The Upper Auglaize Watershed agricultural non-point source modeling project was an interagency effort to use a Geographic Information System (GIS)-based modeling approach for assessing and reducing pollution from agricultural runoff and other non-point sources. This project applied the U.S. Department of Agriculture (USDA), Agricultural Research Service’s AGricultural Non-Point Source (AGNPS) suite of models to the Upper Auglaize River Watershed, a major watershed within the Maumee River Basin. This modeling project was conducted by an interagency team consisting of a partnership between the: (1) USDA, Agricultural Research Service (ARS); (2) USDA, Natural Resources Conservation Service (NRCS); (3) U.S. Army Corps of Engineers (USACE); (4) U.S. Geological Survey (USGS); (5) Ohio State University; (6) University of Toledo (UT); (7) Heidelberg College; (8) Ohio Department of Natural Resources (ODNR), Division of Soil and Water Conservation; (9) Ohio Environmental Protection Agency (OEPA); and (10) Allen, Auglaize, Van Wert, and Putnam Soil and Water Conservation Districts. The partnership was the first step in a process to eventually apply the model in a portioned subset of watersheds for the Maumee Basin, and then to link them to form a comprehensive basin-wide model This work was performed under the authority of Section 516(e) of the Water Resources Development Act (WRDA) of 1996, as amended, for the purpose of assisting State and local watershed managers with their evaluation, prioritization and implementation of alternatives for soil conservation, sediment trapping and non-point source pollution prevention in the Upper Auglaize River watershed. The project team, working in a cooperative effort, used the models to determine sediment sources, contributing locations, and the effect of application of best management practices (BMPs) on rates of sediment delivery to the mouth of the watershed. The results will be used to guide conservation incentive and land treatment programs. The team relied heavily on Geographic Information System (GIS)-based applications to expedite the application of the model. The results of the analysis demonstrated that the application of BMPs would have a positive effect on reducing the loadings of sediment leaving the mouth of the Upper Auglaize Watershed. An application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth to 82 percent of the simulated existing condition loadings. No-till, conversion of cropland to grassland, other uses including grass buffers, and reforestation of parts of the watershed, were all shown by the model to have a measurable effect on reducing sediment loads. Conversion of all of the cropland in the watershed to no-till would reduce the average unit load (tons of sediment per acre) leaving the mouth of the watershed to a level that is 42 percent of the simulated existing condition load. Ephemeral gullies were found to be the primary source of erosion (72 percent), sediment yield (73 percent), and sediment loading (73 percent). Controlling sediment load means controlling gully erosion and possibly trapping sediment yield before it reaches the stream system. Most BMPs (e.g., no-till, conversion of cropland, etc.) that reduce sheet and rill erosion and its sediment yield will also reduce gully erosion and its sediment yield. However, grassed waterways, which have no effect on sheet and rill erosion, are frequently an effective BMP to prevent ephemeral gullies. And, of course, riparian vegetation and sediment traps would reduce the delivery ratios of all types of landscape erosion. New techniques were developed by the team to quantify the ephemeral gully erosion within the model. When calibrated to available stream gage data the model suggests that more (73% in the existing condition simulation) of the sediment load originates from ephemeral gully erosion than from traditional sheet and rill erosion. The model quantified the value of tile drainage in reducing the sediment load from the watershed. Loadings under drained conditions were always less than loadings under undrained conditions for otherwise identical land uses. The average sediment load of all alternatives for drained loadings was 89.2 percent of the load for the corresponding undrained loadings. The model established that while many conservation incentive programs treat tile drainage as a production practice, significant erosion and sediment control benefits are provided by the practice in comparison to cultivation in an undrained state.

Ohio↗

USGS2016

Sediment acoustic index method for computing continuous suspended-sediment concentrations

Suspended-sediment characteristics can be computed using acoustic indices derived from acoustic Doppler velocity meter (ADVM) backscatter data. The sediment acoustic index method applied in these types of studies can be used to more accurately and cost-effectively provide time-series estimates of suspended-sediment concentration and load, which is essential for informed solutions to many sediment-related environmental, engineering, and agricultural concerns. Advantages of this approach over other sediment surrogate methods include: (1) better representation of cross-sectional conditions from large measurement volumes, compared to other surrogate instruments that measure data at a single point; (2) high temporal resolution of collected data; (3) data integrity when biofouling is present; and (4) less rating curve hysteresis compared to streamflow as a surrogate. An additional advantage of this technique is the potential expansion of monitoring suspended-sediment concentrations at sites with existing ADVMs used in streamflow velocity monitoring. This report provides much-needed standard techniques for sediment acoustic index methods to help ensure accurate and comparable documented results. A sediment acoustic index gage is used to collect continuous acoustic backscatter data, using an ADVM deployed in a fixed location, which are related to results from discrete suspended-sediment samples. The raw ADVM backscatter data are adjusted for variables affecting backscatter other than the sediment concentration to compute the sediment-corrected backscatter ( SCB ) and sediment attenuation coefficient ( SAC ). The sediment acoustic index rating (rating) is then developed by relating the sediment characteristics from the periodic samples to the SCB and (or) SAC and other explanatory variables in a site-specific, instrument-specific, simple or multiple linear regression model. The rating is reviewed and checked to ensure the technique has been applied appropriately. This review includes an assessment of the theoretical soundness, the adequacy of the model calibration dataset, and the quality of the regression model and regression diagnostics. The rating can then be applied to the acoustic surrogates and other explanatory variables to obtain continuous records of computed suspended-sediment concentration. The estimates of suspended-sediment concentration can then be paired with streamflow data, if available, to compute continuous records of suspended-sediment load. Once developed, sediment acoustic index ratings must be validated with additional suspended-sediment samples, beyond the period of record used in the rating development, to verify that the regression model continues to adequately represent sediment conditions within the stream. Changes in ADVM configuration or installation, or replacement with another ADVM, may require development of a new rating. The best practices described in this report can be used to develop continuous estimates of suspended-sediment concentration and load using sediment acoustic surrogates to enable more informed and accurate responses to diverse sedimentation issues.

Techniques and Methods↗

USGS1911

Ground water in Juab, Millard, and Iron Counties, Utah

Location and extent of area - Juab, Millard, and Iron counties lie in western Utah, and, with the exception of a small part of Iron County, are entirely within the Great Basin. (See fig. 1.) They comprise about 13,650 square miles, of which approximately 3,500 belong to Juab, 6,775 to Millard, and 3,375 to Iron County. Beaver County, which lies between Millard and Iron counties, is not discussed in this paper because its water resources have been described by W. T. Lee, of the United States Geological Survey, in Water-Supply Paper 217. Purpose of investigation - The investigation was begun in the summer of 1908, under cooperative agreement between the Director of the United States Geological Survey and Caleb Tanner, State engineer of Utah, the object of the work being to obtain and disseminate information which should lead to a greater utilization of the ground-water supplies. The agricultural development of an arid section, such as this, is primarily dependent on the amount of water available. Large tracts of fertile soil remain idle year after year for lack of water for irrigation, while much water that falls as rain and snow sinks into the ground, saturates the porous materials underlying the valleys and deserts, and eventually reappears at the surface in low alkali flats, where it is dissipated by evaporation without producing useful vegetation. If the water thus lost can be applied to fertile soil it will substantially increase the agricultural yield of the region. An urgent demand for information in regard to ground-water prospects has been created in recent years by the adoption of dry farming methods in localities where water is not readily obtained. The water required for culinary purposes and for supplying the horses and traction engines used in tilling the soil on some of the dry farms is at present hauled long distances. In most of the arid parts of this region watering places of any sort are so scarce that certain sections are accessible for grazing only in the winter when sheep will depend on snow for their water supply. In some of these sections an intelligent search would probably discover ground-water supplies which would increase greatly the value of the range.

Utah↗

USGS2011

Assessing conceptual models for subsurface reactive transport of inorganic contaminants

In many subsurface situations where human health and environmental quality are at risk (e.g., contaminant hydrogeology petroleum extraction, carbon sequestration, etc.),scientists and engineers are being asked by federal agency decision-makers to predict the fate of chemical species under conditions where both reactions and transport are processes of first-order importance. In 2002, a working group (WG) was formed by representatives of the U.S. Geological Survey, Environmental Protection Agency, Department of Energy Nuclear Regulatory Commission, Department of Agriculture, and Army Engineer Research and Development Center to assess the role of reactive transport modeling (RTM) in addressing these situations. Specifically the goals of the WG are to (1) evaluate the state of the art in conceptual model development and parameterization for RTM, as applied to soil,vadose zone, and groundwater systems, and (2) prioritize research directions that would enhance the practical utility of RTM.

Eos, Transactions, American Geophysical Union↗

USGS2015

Methods for evaluating potential sources of chloride in surface waters and groundwaters of the conterminous United States

Chloride exists as a major ion in most natural waters, but many anthropogenic sources are increasing concentrations of chloride in many receiving waters. Although natural concentrations in continental waters can be as high as 200,000 milligrams per liter, chloride concentrations that are suitable for freshwater ecology, human consumption, and agricultural and industrial water uses commonly are on the order of 10 to 1,000 milligrams per liter. “Road salt” frequently is identified as the sole source of anthropogenic chloride, but only about 30 percent of the salt consumed and released to the environment is used for deicing. Furthermore, several studies in Southern States where the use of deicing salt is minimal also show anthropogenic chloride in rising concentrations and in strong correlation to imperviousness and road density. This is because imperviousness and road density also are strongly correlated to population density. The term “road salt” is a misnomer because deicers applied to parking lots, sidewalks, and driveways can be a substantial source of chloride in some catchments because these land covers are comparable to roadways as a percentage of the total impervious area and commonly receive higher salt application rates than some roadways. Other sources of anthropogenic chloride include wastewater, dust control on unpaved roads, fertilizer, animal waste, irrigation, aquaculture, energy production wastes, and landfill leachates. The assumption that rising chloride concentrations in surface water or groundwater is indicative of contamination by deicing chemicals rather than one or more other potential sources may preclude the identification of toxic, carcinogenic, mutagenic, or endocrine-disrupting contaminants that are associated with many sources of elevated chloride concentrations. Once the sources of anthropogenic chloride in an area of interest have been identified and measured, water and solute budgets can be estimated to guide decisionmakers to identify and apply potential mitigation measures that can reduce the problem. Scientists, engineers, regulators, and decisionmakers need information about potential sources of chloride, water and solute budgets, and methods for collecting water-quality data to help identify potential sources. This information is needed to evaluate potential sources of chloride in areas where chloride may have adverse ecological effects or may degrade water supplies used for drinking water, agriculture, or industry. Knowledge of potential sources will help decisionmakers identify the best mitigation measures to reduce the total background chloride load, thereby reducing the potential for water-quality exceedances that occur because of superposition on rising background concentrations. Also, knowledge of potential sources may help decisionmakers identify the potential for the presence of contaminants that have toxic, carcinogenic, mutagenic, or endocrine-disrupting effects at concentrations that are lower by orders of magnitude than the chloride concentrations in the source water. This report is a comprehensive synthesis of relevant information, but it is not the result of an exhaustive search for literature on each topic. The potential adverse effects of chloride on infrastructure and the environment are not discussed in this report because these issues have been extensively documented elsewhere.

Open-File Report↗

USGS2017

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2010

Introduction to the JEEG Agricultural Geophysics Special Issue

Near-surface geophysical methods have become increasingly important tools in applied agricultural practices and studies. The great advantage of geophysical methods is their potential rapidity, low cost, and spatial continuity when compared to more traditional methods of assessing agricultural land, such as sample collection and laboratory analysis. Agricultural geophysics investigations commonly focus on obtaining information within the soil profile, which generally does not extend much beyond 2 meters beneath the ground surface. Although the depth of interest oftentimes is rather shallow, the area covered by an agricultural geophysics survey can vary widely in scale, from experimental plots (10 s to 100 s of square meters), to farm fields (10 s to 100 s of hectares), up to the size of watersheds (10 s to 100 s of square kilometers). To date, three predominant methods—resistivity, electromagnetic induction (EMI), and ground-penetrating radar (GPR)—have been used to obtain surface-based geophysical measurements within agricultural settings. However, a recent conference on agricultural geophysics (Bouyoucos Conference on Agricultural Geophysics, September 8–10, 2009, Albuquerque, New Mexico; www.ag-geophysics.org) illustrated that other geophysical methods are being applied or developed. These include airborne electromagnetic induction, magnetometry, seismic, and self-potential methods. Agricultural geophysical studies are also being linked to ground water studies that utilize deeper penetrating geophysical methods than normally used.

Journal of Environmental & Engineering Geophysics↗

USGS2024

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high-resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine-class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant ( p less than 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16-year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.

California↗