This study employed a novel combination of data (winter cover crop [WCC] 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 WCC at the watershed scale, from 2008 through 2017, in the Tuckahoe Creek watershed, located within the Choptank River basin. The Choptank River is a tributary of the Chesapeake Bay, and, as a focus watershed for the USDA9s Conservation Effects Assessment Project, 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 large increase in the use of WCC within the Tuckahoe Creek watershed during the study period, rising from 27% of corn (Zea mays L.) fields and 9% of soybean (Glycine max L.) 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 interannual 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 with increased adoption of WCC. The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Upper 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 (NO3−) leaching from cropland attributable to cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. Results suggest that increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat (Triticum aestivum L.), which comprised 34.7% of all WCC planted between 2008 and 2017.
Abstract An atmospheric tracer dispersion study known as Joint Urban 2003 was conducted in Oklahoma City, Oklahoma, during July of 2003. As part of this field program, vertical concentration profiles were measured at approximately 1 km from the downtown ground-level tracer gas release locations. These profiles showed that the urban landscape was very effective in mixing the plume vertically. In general, the lowest concentration measured along the profile was within 50% of the highest concentration in any given 5-min measurement period. The general slope of the concentration profiles was bounded by a Gaussian distribution with Briggs’s urban equations (stability classes D and E/F) for vertical dispersion. However, measured concentration maxima occurred at levels above the surface, which would not be predicted by Gaussian formulations. Variations in tracer concentration observed in the time series between different release periods were related to changes in wind direction as opposed to changes in turbulence. This was demonstrated using data from mobile analyzers that captured the width of the plume by traveling east to west along nearby streets. These mobile-van-analyzer data were also used to compute plume widths. Plume widths increased for wind directions at larger angles to the street grid, and a simple model comprising adjusted open-country dispersion coefficients and a street channeling component, were used to describe the measured widths. This dispersion dataset is a valuable asset not only for developing advanced tools for emergency-response situations in the event of a toxic release but also for refining air-quality models.
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 R2 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, R2 = 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.
Abstract Near‐surface air quality (AQ) observations over coastal waters are scarce, a situation that limits our capacity to monitor pollution events at land‐water interfaces. Satellite measurements of total column (TC) nitrogen dioxide (NO 2 ) observations are a useful proxy for combustion sources, but the once daily snapshots available from most sensors are insufficient for tracking the diurnal evolution and transport of pollution. Ground‐based remote sensors like the Pandora Spectrometer Instrument (PSI) that have been developed to verify space‐based TC NO 2 and other trace gases are being tested for routine use as certified AQ monitors. The KORUS‐OC (Korea‐United States Ocean Color) cruise aboard the R/V Onnuri in May–June 2016 represented an opportunity to study AQ near the South Korean coast, a region affected by both local/regional and long‐distance pollution sources. Using PSI data in direct‐Sun mode and in situ sensors for shipboard ozone, CO, and NO 2 , we explore, for the first time, relationships between TC NO 2 and surface AQ in this coastal region. Three case studies illustrate the value of the PSI and complexities in the surface‐column NO 2 relationship caused by varying meteorological conditions. Case Study 1 (25–26 May 2016) exhibited a high correlation of surface NO 2 to TC NO 2 measured by both PSI and Aura's Ozone Monitoring Instrument, but two other cases displayed poor relationships between in situ and TC NO 2 due to decoupling of pollution layers from the surface. With suitable interpretation the PSI TC NO 2 measurement demonstrates good potential for working with upcoming geostationary satellites to advance diurnal tracking of pollution.
During the summer 1992, environmental and biogenic hydrocarbon emissions data were collected in a mixed hardwood forest at scales ranging from leaf to canopy to the mixed layer for the purpose of investigating issues related to the scale-up of leaf or branch level emission measurements to regional emission inventories. Results from canopy measurements are compared to several different forest canopy emission models. These range in complexity from a no-canopy effects method to the PC-BEIS canopy profile method to a numerical forest canopy radiative transfer model. The investigation includes a model-to-model intercomparison of predicted canopy environmental parameters including photosynthetically active radiation (PAR) and leaf temperature. The work is seeking to evaluate relatively simple modeling approaches for use in regional emission inventories using field data and more sophisticated numerical models.
