2012 Annual Report
1a.Objectives (from AD-416):
The ability to monitor and predict the ecological dynamics of carbon and nitrogen, agrichemicals, and invasive species over large areas will be critical for maintaining sustainable agricultural systems. Accordingly, this project has four objectives that focus on using remote sensing tools and modeling to scale point observations and field data from the landscape to regional scales.
These are as follows:
Objective 1: Develop measurement and remote sensing technologies for monitoring soil carbon and carbon exchange from field to regional scales to improve assessments of agricultural-management impacts on the carbon balance.
Objective 2: Evaluate the impact of nutrient dynamics on the environment and agricultural production at field and watershed scales.
Objective 3: Develop measurement techniques and models for quantifying field-scale herbicide volatilization.
Objective 4: Evaluate the utility of remote sensing methods to detect invasive species and test models of invasive weed potential distribution using remotely-sensed data.
In the first objective, new remote sensing methods will be investigated to quantify soil carbon redistribution, crop residue and carbon dioxide fluxes, and to identify critical process-based variables that will be incorporated into models and decision support systems. Research towards this objective will be focused in three areas:
(1) assessing soil organic carbon at field and landscape scales;
(2) measurement of crop residues with remote sensing; and
(3) estimating regional scale soil carbon sequestration and carbon dioxide fluxes.
In the second objective, innovative remote sensing methods will be used to maximize nitrogen use efficiency and crop nitrogen status, which feed into watershed-scale process models used to predict water quality. The specific goals for this objective are:
(1) determine remotely sensed leaf chlorophyll content at high spatial resolutions; and
(2) determine watershed nutrient budgets and evaluate the use of remote sensing data as inputs into watershed models.
The third objective focuses on quantifying field-scale pesticide volatilization and the soil and climatic factors governing those emissions.
Recent investigations have shown that pesticide emissions depend largely on weather and soil moisture conditions and this work will also strive to develop a model of pesticide volatilization that reproduces observed emissions and their response to these governing climatic factors.
The fourth objective investigates the spread of invasive plant species and uses remote sensing as a cost-effective tool to obtain the distribution of various invasive species. This research will also investigate the utility of image texture analysis techniques for species identification, and use the classified images to test landscape-distribution models.
1b.Approach (from AD-416):
Research planned for this project will be conducted at a range of spatial scales from small fields to regional. Field-scale, intensive studies will be conducted at the Optimizing Production inputs for Economic and Environmental Enhancement (OPE3) site, which has extensive datasets on soils, topography, and surface and subsurface hydrology, and a long-term record of fluxes, weather, and yields. Remote sensing will be used to identify wet and dry areas at the field-scale and will be used to test the effects of soil moisture and temperature on agrichemical behavior using field data and eddy covariance techniques. Additionally, information on carbon and nutrient behavior gleaned from these field-scale studies will be extended to larger scales at two Conservation Effects Assessment Project (CEAP) watersheds: the Choptank River in Maryland and the South Fork of the Iowa River in Central Iowa. While the large watersheds have less data than the intensive site, there are many partners who are providing data to validate models and remote sensing techniques for regional scale applications. Soil organic carbon and crop residues will be measured at laboratory and field scales with high-spectral-resolution sensors. These and other satellite data will be used to test process-based models. Nitrogen status will be assessed for various crops with very-high-resolution imagery (< 1 cm pixel) so that plants and soil can be separated. The maps of nitrogen status and CEAP data will be used to evaluate the Soil and Water Assessment Tool (SWAT) water quality model. Photographs of leaves, plants, and small plots will be used to determine if image texture analysis can be used for invasive species classification. Resulting techniques will be used to plot the distribution of leafy spurge at the landscape scale, which will then be used to test the Weed Invasion Susceptibility Prediction (WISP) model.
Field experiments to measure herbicide (Metolachlor and Atrazine) volatilization (vapor loss to the atmosphere) were continued at Beltsville, Maryland to create the longest record of herbicide volatilization observations in the world. Soil tillage intensity, based on crop residue cover, was measured in selected fields of the South Fork and Walnut Creek watersheds in central Iowa. Multispectral satellite data were used to classify tillage intensity for all fields in both watersheds. This work provides spatially explicit information on crop and soil management practices to biogeochemical models resulting in more reliable estimates of soil carbon and water quality at field, watershed, and regional scales. Soybean residue was collected from multiple fields and a field residue decomposition experiment was started to better understand the environmental factors affecting the rate of residue decomposition. Two models are being integrated to order to increase the ability of remote sensing data to estimate fluxes of carbon, water and energy. The first is a canopy reflectance model designed to estimate leaf area index and chlorophyll content and the second is an energy-budget light-use-efficiency model designed to calculate fluxes based on weather data. The integrated model results in a remotely sensed canopy chlorophyll content value that is used to specify time-varying model parameters to calculate more accurate fluxes. Data collected in multiple corn/soybean fields during large-scale field experiments in central Iowa are being used for testing the integrated model. Field experiments varying the nitrogen fertilization rate for corn were established in Beltsville, Maryland and at the University of Maryland Eastern Shore. Due to drought the year before, half of each N-rate plot is receiving irrigation. Leaf spectral reflectances and leaf properties were acquired in order to better utilize canopy radiative transfer models to use remote sensing to detect nitrogen deficiency. Work continues testing the Soil & Water Assessment Tool (SWAT) model to evaluate water quality in the Choptank watershed (Eastern Shore, Maryland) and the South Fork watershed (Iowa). High-spatial-resolution photographs (true color and color infrared) and spectral reflectances were acquired from an aerial lift over meadows, soybean and corn fields, and illicit drug crops as test data to determine if image texture and pattern recognition software could be used to identify different species within a mixed vegetated landscape. Landsat Thematic Mapper data for study sites in northeastern Wyoming were acquired and analyzed for the presence of leafy spurge, a noxious invasive weed that has distinctive flowers.
Airborne hyperspectral imagery to map soil properties. Spatial assessment of soil properties is important for understanding the dynamics of agricultural ecosystems and often the spatial distribution of soil properties, such as organic carbon content, is unknown. The utility of hyperspectral imagery in conjunction with partial least squares regression models to develop detailed maps of soil properties was investigated. The aircraft-based hyperspectral data was shown to provide accurate maps of important soil properties such as carbon, aluminum, iron, and silt and sand. Application of this technology will greatly improve site specific management of agricultural lands and provide an accurate assessment of the spatial distribution of soil texture, fertility, and carbon storage within agricultural fields.
A robust spectral index for assessing crop residue cover across landscapes. Management of crop residue cover is critical for developing effective conservation tillage practices. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over multiple fields in agricultural regions. Landscape-scale assessment of crop residue cover is possible with remote sensing technology. Spectral indices that detect cellulose in crop residues were found to be robust, not affected crop or soil type in the Pacific Northwest, the Central and Northern Great Plains, and the Midwest regions of the U.S. These spectral indices can provide reliable estimates of crop residue cover and soil tillage intensity for regional assessments of conservation practices.
Hyperspectral remote sensing method to estimate leaf dry matter content. Information on leaf dry matter content is required for accurate estimation of carbon sequestration, plant nutrient concentrations, and wildfire fuel moisture content. However, spectral signatures from leaf dry matter are largely obscured by the liquid water in fresh green leaves. Scientists from George Mason University (Fairfax, Virginia) and Beltsville, Maryland collaborated on the development of a new remote-sensing index, associated with an absorption feature caused by chemical bonds between carbon and hydrogen found in plant organic compounds. This new index will provide accurate leaf dry matter content estimates for improved assessments of crop condition and wildfire potential over large areas.
Method to characterize model uncertainty for setting water quality objectives. The U.S. Environmental Protection Agency (EPA) sets a Total Maximum Daily Load (TMDL) for nutrients being put into streams and rivers to maintain water quality. Computer models such as the Soil & Water Assessment Tool (SWAT) are used to set limitations on the release of impairing substances. The uncertainty associated with predictions of SWAT has not been rigorously quantified, so often the predictions are adjusted using an arbitrary margin of safety. Scientists from Beltsville, Maryland and the University of Maryland, College Park, developed a new approach to estimate model uncertainty based on the desired level of confidence in meeting a given water-quality standard. This approach is a significant improvement over the current EPA strategy because more realistic TMDL's can be defined to meet water-quality objectives for a given river or stream.
Long-term herbicide volatilization evaluated. Field investigations over the past 14 years have demonstrated that volatilization (vapor loss to the atmosphere) is perhaps the most critical loss pathway whereby herbicides leave a production field and enter neighboring ecosystems. The herbicide volatilization experiments conducted in Beltsville, Maryland are the longest record of herbicide vapor loss observations worldwide. Herbicide volatilization is generally the greatest under warm, wet soil moisture conditions during the day when the atmosphere is unstable. Consequently, herbicide volatilization models will need to account not only for atmospheric stability but soil moisture conditions as well. The research will affect USDA and U.S. Environmental Protection Agency policies with regard to herbicide use and the data will improve pesticide behavior models.
Reeves III, J.B., McCarty, G.W., Calderon, F., Hively, W.D. 2012. Advances in spectroscopic methods for quantifying soil carbon. In: Liebig, MA, Franzluebbers, A.J., and Follett, R. editors. Managing Agricultural Greenhouse Gases. Amsterdam, The Netherlands: Elsevier. 20:345-366.
Gish, T.J., Prueger, J.H., Daughtry, C.S., Kustas, W.P., McKee, L.G., Russ, A.L. 2011. Comparison of field-scale herbicide and runoff losses: An eight year field investigation. Journal of Environmental Quality. 40:1432-1442.
Beeson, P.C., Doraiswamy, P.C., Sadeghi, A.M., Di Luzio, M., Tomer, M.D., Arnold, J.G., Daughtry, C.S. 2011. Treatments of Precipitation Inputs to Hydrologic Models: Guages and NEXRAD. Transactions of the ASABE. 54(6):2011-2020.
Sexton, A.M., Sadeghi, A.M., Zhang, X., Srinivasan, R., Shirmohammadi, A. 2009. Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. Transactions of the American Society of Agriculture and Biological Engineers. 53(5):1501-1510.
Hively, W.D., McCarty, G.W., Reeves III, J.B., Lang, M.W., Osterling, R.A., Delwiche, S.R. 2011. Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields. Applied and Environmental Soil Science. DOI: 10.1155/2011/358193.
Aguilar, J.P., Evans, R.G., Daughtry, C.S. 2012. Performance assessment of the cellulose absorption index method for estimating crop residue cover. Journal of Soil and Water Conservation. 67(3): 202-210.
Wang, L., Qu, J.J., Hao, X., Hunt, E.R. 2011. Estimating dry matter content from spectral reflectances for green leaves of different species. International Journal of Remote Sensing. 32(22):7097-7109.
Sexton, A.M., Shirmohammadi, A., Sadeghi, A.M., Montas, H.J. 2011. Impact of parameter uncertainty assessment of critical SWAT output simulations. Transactions of the American Society of Agriculture and Biological Engineers. 54(2):461-471.
Aguilar, J.P., Evans, R.G., Vigil, M.F., Daughtry, C.S. 2012. Spectral estimates of crop residue cover and density for standing and flat wheat stubble. Agronomy Journal. 104:271-279.
Sexton, A.M., Shirmohammadi, A., Sadeghi, A.M., Montas, H.J. 2011. A stochastic method to characterize model uncertainty for a Nutrient TMDL. Transactions of the ASABE. 54(6):2197-2207.
Lang, M.W., McDonough, O., McCarty, G.W., Oesterling, R.A., Wilen, B. 2012. Enhanced detection of wetland-stream connectivity using lidar: Implications for improved wetland conservation and management. Wetlands. 32:461-473.
Eitel, J.U., Vierling, L., Long, D.S., Hunt Jr, E.R. 2011. Early season remote sensing of wheat nitrogen status using a green scanning laser. Agricultural and Forest Meteorology. 151:1338-1345.