Geology topics

Gregory W. McCarty

Publications and source records attributed to Gregory W. McCarty.

18 recordsLinked to original sources

USGS2025

Satellite assessment of winter cover crop and conservation tillage outcomes to support adaptive management in working landscapes

The use of winter cover crops and conservation tillage are agricultural practices promoted to reduce nutrient and sediment loss from cropland, improve soil health, increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long-Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no-cost Harmonized Landsat and Sentinel-2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super-spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7-year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.

Maryland↗

USGS2024

The LTAR cropland Common Experiment at Lower Chesapeake Bay

The Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (LTAR) Common Experiment (CE) located in Beltsville, MD, focuses on research of concern to producers of the major regional crops, which are corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], wheat ( Triticum aestivum L.), and various forage species. Livestock production in the region includes broiler and laying chickens ( Gallus gallus domesticus L.) and dairy and beef cattle ( Bos taurus L.). The LCB region is among the most heavily populated in the United States. Urban development pressure is high for both farms and natural areas. The need to restore Chesapeake Bay water quality is a major influence on regional agricultural practices. Conservation practices such as cover cropping, no-till agriculture, and nutrient management planning are more common in the region compared to nationally. However, farmers still face management challenges implementing practices that address water quality and the rise of herbicide-resistant weeds. Researchers at the LCB site recognize the need to protect the Chesapeake and Delaware Bays and maintain farmer profitability. The LCB CE compares a 3-year crop rotation system featuring alternative crop management (cover crop intensification, crop rotation diversification, and integrated weed management [IWM]) with a prevailing 2-year system (no cover crops and no IWM), both under continuous no-tillage, to identify the optimal balance to promote the sustainability of regional cropping systems. The LTAR LCB site provides data-driven tools and solutions to support farmers in the mid-Atlantic region.

Delaware, Maryland, New Jersey, Pennsylvania, Virg↗

USGS2023

Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination

In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 ( n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields ( n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates ( n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US$0.10 per kg of biomass and $4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of $0.08 per kg of biomass and $3.51 per kg of N. Late-planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive.

Maryland↗

USGS2020

Improving the ability to include freshwater wetland plants in process-based models

Considerable effort and resources have been placed into conservation programs designed to reduce or alleviate negative environmental effects of crop production and into evaluation of the benefits of these programs. Wetlands are an important source of ecosystem services, but modeling wetland plants is an emerging science. To date, wetland plant growth has not been explicitly accounted for in ecosystem service models that quantify conservation program effects. As part of an effort to more accurately simulate wetland plants within process-based models, we expanded upon plant growth data collected in an earlier effort with additional sampling at two of four previously sampled areas, and included a fifth sampling site. We then used data from the five sites spanning five years as wetland plant parameters at both the species and functional group levels for the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model. In addition to individual species, modelers are interested in functional groups representing a collection of species because it is unrealistic to model every species occurring in an ecosystem. ALMANAC simulations were completed at three sites for both individual wetland plant species and functional groups. At each site, simulated plant yields were within 1 Mg ha –1 (±7%) of measured values ( r 2 = 0.99). Multisite species simulated yields were within 37% of measured values ( r 2 = 0.95). Functional groups performed as well as individual species simulations. Functional group simulated yields were within 1 Mg ha –1 (±5%) of measured yields. Plant growth is a major component of these wetland ecosystems, and ALMANAC verified wetland plant parameters support more accurate assessments of conservation programs and practices on the influence of wetland ecosystems embedded within agricultural fields. The improved plant parameters we provide here will be transferred to other process-based models that focus on other ecosystem components such as soil and water effects, facilitating wetland evaluations across the United States and elsewhere.

California, Delaware, Maryland, North Dakota, Texa↗

USGS2020

Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling

This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.

Delaware, Maryland↗

USGS2020

Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed

Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass ( p ≤0.01, R 2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R 2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.

Maryland↗

USGS2020

Fluxes of agricultural nitrogen and metolachlor metabolites are highly correlated in a first order stream in Maryland, USA

Nitrogen pollution in watersheds containing significant cropland area is generally problematic. Conservation practices intended to reduce nitrate-N (NO3--N) export from watersheds are being implemented by many regions without necessary tools to assess effectiveness of these abatement tools. A commonly used herbicide metolachlor degrades in the vadose zone of croplands to form two metabolites (metolachlor ethane sulfonic acid (MESA) and metolachlor oxanilic acid (MOXA)) which are both highly soluble in soils. Study of metabolite fates in a first order watershed provided evidence that transport of these metabolites to stream water is highly correlated to transport of the agricultural NO3--N that also forms in the cropland vadose zone. Linear models describing the relationships of stream flux of MESA and MOXA to NO3--N flux generated goodness of fit values of 0.93 and 0.82 respectively. These findings support a conclusion that both MESA and MOXA act as excellent transport analogs of NO3- and become strongly correlated to agricultural NO3--N leaching from the cropland vadose zone. Moreover, their use as conservative tracers in agricultural watersheds can provide valuable information concerning movement and fate of agricultural nitrogen at watershed scales of observation.

Maryland↗

USGS2018

Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices

Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index < 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R 2 values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R 2 = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.

Maryland↗

USGS2018

Comparative analyses of hydrological responses of two adjacent watersheds to climate variability and change using the SWAT model

Water quality problems in the Chesapeake Bay Watershed (CBW) are expected to be exacerbated by climate variability and change. However, climate impacts on agricultural lands and resultant nutrient loads into surface water resources are largely unknown. This study evaluated the impacts of climate variability and change on two adjacent watersheds in the Coastal Plain of the CBW, using the Soil and Water Assessment Tool (SWAT) model. We prepared six climate sensitivity scenarios to assess the individual impacts of variations in CO 2 concentration (590 and 850 ppm), precipitation increase (11 and 21 %), and temperature increase (2.9 and 5.0 °C), based on regional general circulation model (GCM) projections. Further, we considered the ensemble of five GCM projections (2085–2098) under the Representative Concentration Pathway (RCP) 8.5 scenario to evaluate simultaneous changes in CO 2 , precipitation, and temperature. Using SWAT model simulations from 2001 to 2014 as a baseline scenario, predicted hydrologic outputs (water and nitrate budgets) and crop growth were analyzed. Compared to the baseline scenario, a precipitation increase of 21 % and elevated CO 2 concentration of 850 ppm significantly increased streamflow and nitrate loads by 50 and 52 %, respectively, while a temperature increase of 5.0 °C reduced streamflow and nitrate loads by 12 and 13 %, respectively. Crop biomass increased with elevated CO 2 concentrations due to enhanced radiation- and water-use efficiency, while it decreased with precipitation and temperature increases. Over the GCM ensemble mean, annual streamflow and nitrate loads showed an increase of ∼ 70 % relative to the baseline scenario, due to elevated CO 2 concentrations and precipitation increase. Different hydrological responses to climate change were observed from the two watersheds, due to contrasting land use and soil characteristics. The watershed with a larger percent of croplands demonstrated a greater increased rate of 5.2 kg N ha −1 in nitrate yield relative to the watershed with a lower percent of croplands as a result of increased export of nitrate derived from fertilizer. The watershed dominated by poorly drained soils showed increased nitrate removal due do enhanced denitrification compared to the watershed dominated by well-drained soils. Our findings suggest that increased implementation of conservation practices would be necessary for this region to mitigate increased nitrate loads associated with predicted changes in future climate.

Greensboro Watershed, Tuckahoe Creek Watershed↗

USGS2017

Model parameters for representative wetland plant functional groups

Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient ( k ), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush ( Eleocharis macrostachya ), reed canary grass ( Phalaris arundinacea ), smartweed ( Polygonum spp.), cattail ( Typha spp.), and hardstem bulrush ( Schoenoplectus acutus ). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in adjacent areas as they affect wetlands.

Ecosphere↗

USGS2017

Assessing the impacts of future climate conditions on the effectiveness of winter cover crops in reducing nitrate loads into the Chesapeake Bay Watershed using SWAT model

Winter cover crops (WCCs) have been widely implemented in the Coastal Plain of the Chesapeake Bay watershed (CBW) due to their high effectiveness at reducing nitrate loads. However, future climate conditions (FCCs) are expected to exacerbate water quality degradation in the CBW by increasing nitrate loads from agriculture. Accordingly, the question remains whether WCCs are sufficient to mitigate increased nutrient loads caused by FCCs. In this study, we assessed the impacts of FCCs on WCC nitrate reduction efficiency on the Coastal Plain of the CBW using Soil and Water Assessment Tool (SWAT) model. Three FCC scenarios (2085 – 2098) were prepared using General Circulation Models (GCMs), considering three Intergovernmnental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) greenhouse gas emission scenarios. We also developed six representative WCC implementation scenarios based on the most commonly used planting dates and species of WCCs in this region. Simulation results showed that WCC biomass increased by ~ 58 % under FCC scenarios, due to climate conditions conducive to the WCC growth. Prior to implementing WCCs, annual nitrate loads increased by ~ 43 % under FCC scenarios compared to the baseline scenario (2001 – 2014). When WCCs were planted, annual nitrate loads were substantially reduced by ~ 48 % and WCC nitrate reduction efficiency water ~ 5 % higher under FCC scenarios relative to the baseline. The increase rate of WCC nitrate reduction efficiency varied by FCC scenarios and WCC planting methods. As CO2 concentration was higher and winters were warmer under FCC scenarios, WCCs had greater biomass and therefore showed higher nitrate reduction efficiency. In response to FCC scenarios, the performance of less effective WCC practices (e.g., barley, wheat, and late planting) under the baseline indicated ~ 14 % higher increase rate of nitrate reduction efficiency compared to ones with better effectiveness under the baseline (e.g., rye and early planting), due to warmer temperatures. According to simulation results, WCCs were effective to mitigate nitrate loads accelerated by FCCs and therefore the role of WCCs in mitigating nitrate loads is even more important in the given FCCs.

Chesapeake Bay↗

USGS2014

Evaluation of a model framework to estimate soil and soil organic carbon redistribution by water and tillage using 137 Cs in two U.S. Midwest agricultural fields

Cultivated lands in the U.S. Midwest have been affected by soil erosion, causing soil organic carbon (SOC) redistribution in the landscape and other environmental and agricultural problems. The importance of SOC redistribution on soil productivity and crop yield, however, is still uncertain. In this study, we used a model framework, which includes the Unit Stream Power-based Erosion Deposition (USPED) and the Tillage Erosion Prediction (TEP) models, to understand the soil and SOC redistribution caused by water and tillage erosion in two agricultural fields in the U.S. Midwest. This model framework was evaluated for different digital elevation model (DEM) spatial resolutions (10-m, 24-m, 30-m, and 56-m) and topographic exponents ( m = 1.0–1.6 and n = 1.0–1.3) using soil redistribution rates from 137 Cs measurements. The results showed that the aggregated 24-m DEM, m = 1.4 and n = 1.0 for rill erosion, and m = 1.0 and n = 1.0 for sheet erosion, provided the best fit with the observation data at both sites. Moreover, estimated average SOC redistributions were 1.3 ± 9.8 g C m − 2 yr − 1 in field site 1 and 3.6 ± 14.3 g C m − 2 yr − 1 in field site 2. Spatial distribution patterns showed SOC loss (negative values) in the eroded areas and SOC gain (positive value) in the deposition areas. This study demonstrated the importance of the spatial resolution and the topographic exponents to estimate and map soil redistribution and the SOC dynamics throughout the landscape, helping to identify places where erosion and deposition from water and tillage are occurring at high rates. Additional research is needed to improve the application of the model framework for use in local and regional studies where rainfall erosivity and cover management factors vary. Therefore, using this model framework can help to improve the information about the spatial distribution of soil erosion across agricultural landscapes and to gain a better understanding of SOC dynamics within eroding and previously eroded fields.

Iowa↗

USGS2014

Metolachlor metabolite (MESA) reveals agricultural nitrate-N fate and transport in Choptank River watershed

Over 50% of streams in the Chesapeake Bay watershed have been rated as poor or very poor based on the index of biological integrity. The Choptank River estuary, a Bay tributary on the eastern shore, is one such waterway, where corn and soybean production in upland areas of the watershed contribute significant loads of nutrients and sediment to streams. We adopted a novel approach utilizing the relationship between the concentration of nitrate-N and the stable, water-soluble herbicide degradation product MESA {2-[2-ethyl-N-(1-methoxypropan-2-yl)-6-methylanilino]-2-oxoethanesulfonic acid} to distinguish between dilution and denitrification effects on the stream concentration of nitrate-N in agricultural subwatersheds. The ratio of mean nitrate-N concentration/(mean MESA concentration * 1000) for 15 subwatersheds was examined as a function of percent cropland on hydric soil. This inverse relationship (R 2 = 0.65, p < 0.001) takes into consideration not only dilution and denitrification of nitrate-N, but also the stream sampling bias of the croplands caused by extensive drainage ditch networks. MESA was also used to track nitrate-N concentrations within the estuary of the Choptank River. The relationship between nitrate-N and MESA concentrations in samples collected over three years was linear (0.95 ≤ R 2 ≤ 0.99) for all eight sampling dates except one where R 2 = 0.90. This very strong correlation indicates that nitrate-N was conserved in much of the Choptank River estuary, that dilution alone is responsible for the changes in nitrate-N and MESA concentrations, and more importantly nitrate-N loads are not reduced in the estuary prior to entering the Chesapeake Bay. Thus, a critical need exists to minimize nutrient export from agricultural production fields and to identify specific conservation practices to address the hydrologic conditions within each subwatershed. In well drained areas, removal of residual N within the cropland is most critical, and practices such as cover crops which sequester the residual N should be strongly encouraged. In poorly drained areas where denitrification can occur, wetland restoration and controlled drained structures that minimize ditch flow should be used to maximize denitrification.

Choptank River Watershed↗

USGS2012

Advances in spectroscopic methods for quantifying soil carbon

The current gold standard for soil carbon (C) determination is elemental C analysis using dry combustion. However, this method requires expensive consumables, is limited by the number of samples that can be processed (~100/d), and is restricted to the determination of total carbon. With increased interest in soil C sequestration, faster methods of analysis are needed, and there is growing interest in methods based on diffuse reflectance spectroscopy in the visible, near-infrared or mid-infrared spectral ranges. These spectral methods can decrease analytical requirements and speed sample processing, be applied to large landscape areas using remote sensing imagery, and be used to predict multiple analytes simultaneously. However, the methods require localized calibrations to establish the relationship between spectral data and reference analytical data, and also have additional, specific problems. For example, remote sensing is capable of scanning entire watersheds for soil carbon content but is limited to the surface layer of tilled soils and may require difficult and extensive field sampling to obtain proper localized calibration reference values. The objective of this chapter is to discuss the present state of spectroscopic methods for determination of soil carbon.

Book chapter↗

USGS2012

Potential pollutant sources in a Choptank River (USA) subwatershed and the influence of land use and watershed characteristics

Row-crop and poultry production have been implicated as sources of water pollution along the Choptank River, an estuary and tributary of the Chesapeake Bay. This study examined the effects of land use, subwatershed characteristics, and climatic conditions on the water quality parameters of a subwatershed in the Choptank River watershed. The catchments within the subwatershed were defined using advanced remotely-sensed data and current geographic information system processing techniques. Water and sediment samples were collected in May–October 2009 and April–June 2010 under mostly baseflow conditions and analyzed for select bacteria, nitrate-N, ammonium-N, total arsenic, total phosphorus (TP), orthophosphate (ortho-P), and particle-phase phosphorus (PP); n = 96 for all analytes except for arsenic, n = 136, and for bacteria, n = 89 (aqueous) and 62 (sediment). Detections of Enterococci and Escherichia coli concentrations were ubiquitous in this subwatershed and showed no correlation to location or land use, however larger bacterial counts were observed shortly after precipitation. Nitrate-N concentrations were not correlated with agricultural lands, which may reflect the small change in percent agriculture and/or the similarity of agronomic practices and crops produced between catchments. Concentration data suggested that ammonia emission and possible deposition to surface waters occurred and that these processes may be influenced by local agronomic practices and climatic conditions. The negative correlation of PP and arsenic concentrations with percent forest was explained by the stronger signal of the head waters and overland flow of particulate phase analytes versus dissolved phase inputs from groundwater. Service roadways at some poultry production facilities were found to redirect runoff from the facilities to neighboring catchment areas, which affected water quality parameters. Results suggest that in this subwatershed, catchments with poultry production facilities are possible sources for arsenic and PP as compared to catchment areas where these facilities were not present.

Choptank River↗

USGS2011

Relating nutrient and herbicide fate with landscape features and characteristics of 15 subwatersheds in the Choptank River watershed

Excess nutrients and agrochemicals from non-point sources contribute to water quality impairment in the Chesapeake Bay watershed and their loading rates are related to land use, agricultural practices, hydrology, and pollutant fate and transport processes. In this study, monthly baseflow stream samples from 15 agricultural subwatersheds of the Choptank River in Maryland USA (2005 to 2007) were characterized for nutrients, herbicides, and herbicide transformation products. High-resolution digital maps of land use and forested wetlands were derived from remote sensing imagery. Examination of landscape metrics and water quality data, partitioned according to hydrogeomorphic class, provided insight into the fate, delivery, and transport mechanisms associated with agricultural pollutants. Mean Nitrate-N concentrations (4.9 mg/L) were correlated positively with percent agriculture (R 2 = 0.56) and negatively with percent forest (R 2 = 0.60). Concentrations were greater ( p = 0.0001) in the well-drained upland (WDU) hydrogeomorphic region than in poorly drained upland (PDU), reflecting increased denitrification and reduced agricultural land use intensity in the PDU landscape due to the prevalence of hydric soils. Atrazine and metolachlor concentrations (mean 0.29 μg/L and 0.19 μg/L) were also greater ( p = 0.0001) in WDU subwatersheds than in PDU subwatersheds. Springtime herbicide concentrations exhibited a strong, positive correlation (R 2 = 0.90) with percent forest in the WDU subwatersheds but not in the PDU subwatersheds. In addition, forested riparian stream buffers in the WDU were more prevalent than in the PDU where forested patches are typically not located near streams, suggesting an alternative delivery mechanism whereby volatilized herbicides are captured by the riparian forest canopy and subsequently washed off during rainfall. Orthophosphate, CIAT (6-chloro- N -(1-methylethyl)-1,3,5-triazine-2,4-diamine), CEAT (6-chloro- N -ethyl-1,3,5-triazine-2,4-diamine), and MESA (2-[(2-ethyl-6-methylphenyl) (2-methoxy-1-methylethyl)amino]-2-oxoethanesulfonic acid) were also analyzed. These findings will assist efforts in targeting implementation of conservation practices to the most environmentally-critical areas within watersheds to achieve water quality improvements in a cost-effective manner.

Delaware, Maryland↗

USGS2011

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples ( n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R 2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Applied and Environmental Soil Science↗

USGS2007

Near infrared spectroscopic examination of charred pine wood, bark, cellulose and lignin: Implications for the quantitative determination of charcoal in soils

The objective of this research was to investigate the effect of charring on near infrared spectra of materials likely to be present in forest fires in order to determine the feasibility of determining charred carbon in soils. Four materials (cellulose, lignin, pine bark and pine wood) and char from these materials created by charring for various durations (1 to 168 h) and at various temperatures (200 to 450??C) were studied. Near infrared spectra and measures of acidity (total acids, carboxylic acids, lactones and phenols as determined by titration) were available for 56 different samples (Not all samples charred at all temperatures/durations). Results showed spectral changes that varied with the material, temperature and duration of charring. Examination of spectra and correlation plots indicated that changes in the constituents of the materials in question, such as loss of OH groups in carbohydrates, rather than direct determination of typical products produced by charring, such as carboxylic acids, lactones and phenols, were the basis for the spectral changes. Finally, while the spectral changes resulting from charring appeared to be relatively unique to each material, PLS calibrations for total acids, carboxylic acids, lactones and phenols were successfully created (with R2 of 0.991, 0.943, 0.931 and 0.944, respectively) indicating that there is a sufficient commonality in the changes to develop calibrations without the need for unique calibrations for each specific set of charring conditions (i.e. material, temperature and time of heating).

Journal of Near Infrared Spectroscopy↗