Location: Hydrology and Remote Sensing Laboratory2013 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.
3. Progress Report:
Field experiments to measure herbicide (Metolachlor and Atrazine) losses to the atmosphere were continued at Beltsville, Maryland to create the longest record of herbicide volatilization observations in the world. This study demonstrated that soil moisture is the most critical factor governing herbicide losses to the atmosphere. Two computer simulation models are being integrated to increase the ability of remote sensing data to estimate fluxes of carbon, water and energy. Methods for estimating carbon fluxes using the Two-Source Energy Balance model and remotely sensed chlorophyll contents have been developed and evaluated using both ground-based eddy flux tower measurements and satellite data. Methods for estimating impact of soil tillage intensity on soil carbon sequestered in soils at watershed and state scales have been developed based on the Environmental Policy Integrated Climate (EPIC) agricultural model. Soil tillage intensity, based on crop residue cover, was measured in selected fields in central Iowa and used with multispectral satellite data to classify tillage intensity at larger scales. This work provides 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. Field experiments varying the nitrogen fertilization rate were established in Beltsville, Maryland, the University of Maryland Eastern Shore, and the Oregon State University Hermiston Agricultural Research and Extension Center. Leaf spectral reflectances and leaf properties were measured in order to better utilize canopy radiative transfer models for the detection of nitrogen deficiency by remote sensing. Airborne and satellite image data were acquired and the radiative transfer models were applied to the image data for comparison to field measurements. Significant progress is being made toward developing and/or modifying SWAT model as well as the combination of EPIC, APEX & SWAT model components to allow the assessment of crop residue removal as impacts soil organic matter content and carbon storage capacity at watershed and basin scales. Although the majority of this work is being done at the South Fork watershed in Iowa, our intention is to also test the combined models at the Choptank Watershed, a tributary of the Chesapeake Bay Watershed in Maryland. 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.
1. Herbicide volatilization, a critical contaminate source. Surface runoff of herbicides after application was thought to be a major environmental problem, but studies in Beltsville, Maryland demonstrated that over a 15 year period, herbicide loss to the atmosphere was over 25 times greater than through surface runoff. Moist soils subjected to high temperatures and windy conditions create a worst case scenario leading to herbicide vapor losses of over 20% of that applied. The research will affect USDA and U.S. Environmental Protection Agency policies with regard to herbicide application and the data will improve pesticide behavior models.
2. Continental-scale variation in soils will not affect estimation of residue cover using remote sensing. Crop residue left on the soil surface protects the soil from erosion and enhances carbon sequestration, but variations in soil reflectance for different soil types reduce the ability of remote sensing to detect crop residues. ARS researchers in Beltsville, Maryland used a USDA-National Resources Conservation Service (NRCS) database of geographically-referenced soil samples to show that one spectral index, called the Cellulose Absorption Index, is able to accurately detect crop residue throughout the Conterminous United States. NASA plans to launch satellite sensors into Earth orbit which have the appropriate spectral bands, so data from NASA sensors could be used for operational estimation of crop residue cover, very useful to NRCS crop residue assessment program.
3. Spectral index for crop nitrogen management. Remote sensing has the potential to be a low cost method to determine crop nitrogen requirements, but current methods cannot detect plant nitrogen status in time for side-dress fertilizer applications. ARS researchers in Beltsville, Maryland developed the Triangular Greenness Index by combining red, green and blue sensor data to estimate leaf chlorophyll content. With airborne and satellite imagery acquired over irrigated corn in Nebraska, the Triangular Greenness Index was highly and consistently related to chlorophyll measurements. This spectral index is ideal for digital cameras and low-cost sensors with very high pixel resolution; both of which could be mounted in small unmanned aircraft, ultimately providing an operational means for efficient nitrogen applications reducing costs and environmental impacts.
4. Using remote sensing to create high resolution topographic maps for carbon storage in wetlands. The length of time that a wetland is saturated with water is important for determining the ability of wetland ecosystems to store soil organic carbon, but standard topographic data do not have sufficient resolution for estimating the amount of time that a wetland is inundated after rainfall. ARS researchers in Beltsville, Maryland acquired Light Detection and Ranging (LiDAR) data from aircraft and calculated topographic metrics, which were found to be highly predictive of wetland inundation. The ability to extrapolate high-resolution topographic data to the landscape and watershed scales holds promise for large scale estimates of carbon storage by wetlands. Maps of carbon storage will help state and federal agencies to manage wetlands by better representing the multiple ecological benefits of maintaining and restoring wetlands.
Daughtry, C.S., Hunt, E.R., Beeson, P.C., Lang, M., Serbin, G., Alfieri, J.G., McCarty, G.W., Sadeghi, A.M. 2012. Remote sensing of soil carbon and greenhouse gas dynamics across agricultural landscapes. In: Liebig, M., Franzluebbers, A., Follett, R., editors. Managing Agricultural Greenhouse Gases. Amsterdam, The Netherlands: Elsevier. p. 385-408.