Progress 10/01/10 to 08/04/15
Outputs
Progress Report 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. 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. This is the final report for this project that will end in FY2015. A bridging project, 8042-12660-014-00D, Quantifying and Monitoring Nurtient Cycling, Carbon Dynamics and Soil Productivity at Field, Wathershed and Regional Scales, has been initiated to continue this work until a new project can be developed. Soil tillage intensity is indicated by the proportion of the soil surface covered by crop residues shortly after plating. Crop residue cover was measured in corn and soybean fields in the South Fork watershed in central Iowa. Tillage intensity for all fields in the watershed was classified using multispectral imagery. This work provided spatially explicit information on crop and soil management practices that were used in crop models to evaluate conservation practices across the watershed. Two-source energy balance (TSEB) methods for estimating carbon assimilation fluxes based on nominal light-use efficiency (LUE) values retrieved via satellite have been integrated into a regional scale modeling framework. The framework uses thermal remote sensing from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) to generate water and carbon flux distributions at 30-1000 m spatial resolution. Satellite maps of carbon, water and energy fluxes generated with this modeling system at landscape scale have been evaluated in comparisons with multi-year observations collected over rainfed and irrigated corn and soybean at Ameriflux sites in the central Midwest. The methods are currently being integrated within a larger- scale modeling system that can be applied at continental-to-global scales to facilitate regional and global water productivity assessments. Significant progress has been made in calibrating and validating the Soil Water Assessment Tool (SWAT) to two sub-watersheds (Tuckahoe and Greensboro Creeks) of the Choptank River Watershed. Specifically, we quantified winter cover crops impacts in the two Sub-basins that have different soil types, but similar crop rotations. Although the two sub- watersheds are similar in size, but have shown different nitrate exports that is mostly attributed to the soil characteristics. Preliminary results show that net amount of nitrates in watersheds is expected to increase and the current winter cover crops may not be sufficient to mitigate water quality degradation. Nitrogen fertilizer and water availability significantly impact corn growth, development, and grain yields. Active and passive sensors were used to measure corn growth, leaf area index, leaf chlorophyll, and spectral reflectance in field experiments through the growing season. Multiple dates of fine spatial resolution imagery were acquired. Differences in leaf chlorophyll and leaf area associated with rates of N fertilizer applied were estimated across the fields using spectral indices. Research activities supporting the development and validation of pesticide volatilization models progressed, including: Continued in-situ studies of pesticide volatilization, developing refined methods for measuring pesticide losses using the flux gradient approach, and investigating the feasibility of using thermal remote sensing-based approaches to both assess surface soil moisture and model pesticide volatilization at field scales. Small unmanned aircraft were flown in Oregon to replicate experiments on detection of nitrogen status and initiate new experiments for detection of damage caused by Colorado Potato Beetles. Small plots were set up with different numbers of beetle larvae, and the beetle populations were monitored regularly. Damage to the plant canopy was correlated with beetle infestation. Modeling-related activities during the past year have been directed toward accessing the feasibility of using remote sensing to estimate pesticide volatilization. Additional activities included continuing field-scale pesticide volatilization experiments to determine the relative importance of crop residue, as well as other environmental conditions, on volatilization rates; and, developing the instrumentation and experimental methods necessary to assess the effect of atmospheric stability on pesticide volatilization. Related to milestone 8, objective 4.2, this milestone could not be met because of a lack of quality data. In the summer of 2013, DigitalGlobe, Inc. acquired WorldView-2 imagery over two locations while the invasive species, leafy spurge, was in peak bloom in Wyoming and North Dakota. This data set should have been sufficient to perform the analysis. Flower bracts of leafy spurge are yellow-green, and the sensor has a new yellow band with a 2-m pixel sizes. However, classification accuracies of presence versus absence were about 5%, so the data were not useful for developing models of leafy spurge distribution over these landscapes. Accomplishments 01 Remote sensing nutrient status by small unmanned aircraft. Small unmanned aircraft used for remote sensing may provide numerous benefits for precision agriculture by monitoring crops for nutrient deficiencies, insect infestations, and disease outbreaks. Under a Certificate of Authorization, small unmanned aircraft were routinely flown over fields of potatoes in Oregon to evaluate procedures for operations and image processing. Sensor calibration is required to compare image changes over time. Based on leaf chlorophyll measurements, nitrogen deficiency symptoms were only detectable late in the growing season and nitrogen excess symptoms could not be detected. By integrating small unmanned aircraft into the national airspace, there are many opportunities for agribusinesses to integrate this information into precision agriculture processes. 02 Current satellite data products suggested agricultural burning was historically widespread in parts of Eastern Europe and Asia. Black carbon emissions from agricultural burning impact arctic warming by accelerating snow and ice loss. Satellite estimates of cropland burning are inaccurate. Agricultural burning of biomass is an economical practice for crop residue management, but can have severe environmental and health impacts due to black carbon (smoke) emissions. Careful evaluation of the data products for agricultural crops indicated very large errors in historical estimates occurrence of agricultural burn. Land managers should avoid using satellite burn area and active fire products on croplands. New methods of measurement for cropland burning are needed. 03 Better method for remote sensing winter cover crops. Remote sensing can be a powerful tool for estimating growth of winter cover crops; however the ground conditions often found during late fall/early winter challenge the ability of satellites to estimate cover and biomass. Various vegetation indices were evaluated for their ability to strengthen the predictions of cover crop biomass using commonly available remote sensing data. The findings showed that different vegetative indexes performed in different ranges of biomass as a result of the influence of index saturation, senescence and leaf frost burn. Selective or segmented use of multiple vegetative indices optimizes biomass and cover predictions over a range of conditions. This research will enhance water quality and watershed management because winter cover crop biomass is critical for reducing nutrient and sediment losses from croplands.
Impacts
(N/A)
Publications
- Hunt Jr, E.R., Daughtry, C.S., Mirsky, S.B. 2014. Remote sensing with simulated unmanned aircraft systems for precision agriculture applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(11):4566-4571.
- Schull, M.A., Anderson, M.C., Houborg, R., Gitelson, A., Kustas, W.P. 2015. Thermal-based modeling of coupled carbon, water and energy fluxes using nominal light use efficiencies constrained by leaf chlorophyll observations. Biogeosciences. 12:1511-1523.
- Mishra, V., Cruise, J.F., Mecikalski, J., Hain, C., Anderson, M.C. 2013. A remote-sensing driven tool for estimating crop stress and yields. Remote Sensing. 5(7): 3331-3356.
- Houborg, R., McCabe, M., Cescatti, A., Gao, F.N., Schull, M.A., Gitelson, A. 2015. Joint Leaf chlorophyll and leaf area index retrieval from Landsat data using a regularized model inversion system. Remote Sensing of Environment. 159:203-221.
- Houborg, R., Mccabe, M., Cescatti, A., Gao, F.N., Schull, M.A., Gitelson, A. 2015. Joint Leaf chlorophyll and leaf area index retrieval from Landsat data using a regularized model inversion system. Remote Sensing of Environment. 159:203-221.
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Progress 10/01/13 to 09/30/14
Outputs
Progress Report 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. 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. Crop residue cover on the soil surface is an important indicator of the extent of conservation tillage. Soil tillage intensity, based on crop residue cover, was measured in more than 200 fields of the South Fork watershed in central Iowa over 3 consecutive years shortly after most fields were planted. Multispectral satellite imagery were acquired and used to classify tillage intensity for all fields in the watershed. Classification accuracies for conservation and conventional tillage ranged from 64-92%. This work provided spatially explicit information on crop and soil management practices that could be used to produce credible estimates of soil carbon and water quality at field, watershed, and regional scales. The Two Source Energy Balance model, for estimating carbon assimilation fluxes based on nominal light-use efficiency (LUE) values retrieved via satellite, has been integrated into a regional scale-modeling framework. The framework uses thermal remote sensing from Landsat and MODIS to generate water and carbon flux distributions at 30-1000 m spatial resolution. Satellite maps of carbon, water and energy fluxes generated with this modeling system are being evaluated in comparisons with multi- year observations collected over rainfed and irrigated corn and soybean at an Ameriflux site in Mead, NE. These tests will then be replicated over other carbon flux sites in the Corn Belt, including ARS facilities outside of Ames, IA. A remote sensing-based technique to more accurately estimate fraction of photosynthetic active radiation (PAR) absorbed by corn plants for photosynthesis and compute gross primary production (GPP) based on a remote sensing estimate of the LUE was developed. The computed daily crop GPP agreed much more closely with the measurements indicating that carbon assimilation models need to incorporate remote sensing-based techniques providing crop chlorophyll information to more accurately estimate LUE and daily GPP. Significant progress has been made in improving The Soil and water assessment tool (SWAT) model performance using various digital evaluation model (DEM) resolutions. Recently, the availability of much finer resolution (~1-3 m) DEMs derived from LiDAR data has increased and water quality modelers are eager to take advantage of these finer-resolution DEMs. However, recent findings have shown that the most accurate nutrients and sediment predictions from SWAT models do not necessarily occur when using a high-resolution DEM (20 m), as compared to low- resolution DEM (500 m). Specifically, use of the SWAT model was investigated with different DEM resolutions and sources to quantify their impact on sediment load prediction. An herbicide metabolite (MESA) was shown to be a reliable tracer of agricultural nitrate-N fate and transport in the Choptank River Watershed. This work aids identification of critical areas in the Choptank River Watershed contributing to high nitrate concentrations in streams and aids the evaluation of BMPs for nitrate mitigation. Wetlands are an important natural resource in watersheds and they provide important ecological services. Understanding local groundwater hydrology and geochemistry is critical for evaluating the effectiveness of depressional wetlands in mitigating nitrate transport from croplands. Fluxes of nitrate were measured into and out of depressional wetlands located in the Choptank River Watershed. Depressional wetlands were a minor source of nitrate in local streams, their effectiveness as landscape sinks for agricultural nitrogen varied substantially based on local hydrology. Further research is required to understand the geologic, hydrologic, and geochemical processes in wetlands to mitigate agricultural non-point-source pollution. Field experiments at Beltsville and University of Maryland, Eastern Shore (UMES) evaluated a wide range of N-rates on corn growth, leaf chlorophyll, and spectral reflectance. Drip irrigation systems provided supplemental water to minimize drought stress at critical growth stages. Pairs of spectral indices were useful for distinguishing the subtle changes in canopy reflectance related to leaf chlorophyll from those associated with changes in soil reflectance and leaf area index. Field experiments at the Hermiston Agricultural Research and Extension Center (HAREC) of Oregon State University tested several unmanned aircraft as remote sensing platforms for nitrogen management of irrigated potatoes. Analyses of the high-resolution data from the unmanned aircraft showed that remotely sensed indices were highly correlated to nitrogen status. A USDA-wide agreement with the company, DigitalGlobe, allowed acquisition of WorldView-2 imagery of HAREC, and analyses of the data are continuing. Significant Activities that Support Special Target Populations: Research is being conducted in collaboration with the University of Maryland Eastern Shore, an 1890 Land-Grant University. Accomplishments 01 Determined cause of variability for measurements using leaf chlorophyll meters. Leaf chlorophyll meters are used worldwide for crop nitrogen management, but the relationship between meter values and measured chlorophyll in leaves vary depending on species and environmental conditions. ARS researchers in Beltsville, Maryland used leaf spectral data and simulations with a leaf-optics model to show that variation in the calibrations depend on the ratio of transmitted to reflected light at near-infrared wavelengths. The research will lead to new types of leaf chlorophyll meters, which will have greater accuracy and improve crop nitrogen management. 02 Finer special resolution data should be used with caution in water quality models. Medium resolution data (30 m) for digital elevations are normally used to estimate hillslope angles for the parameterization of non-point-source water quality models. Recently, digital elevation data with much finer resolution (~2-3 m) are available from LiDAR (Light Detection and Ranging). ARS researchers in Beltsville, Maryland compared different spatial resolutions of digital elevation data (90, 30, 10, and 3 m) as input to the SWAT model for the South Fork watershed in Iowa. Overall, a 250% increase was found in estimated slopes from the coarsest to finest resolutions (90 m to 3 m), which led to a 130% increase in the estimated amount of soil loss by erosion. LiDAR-produced digital elevation data should be used with caution in water quality models, because unrealistically high rates of soil loss will result in unrealistically low estimates of water quality. 03 Satellite-derived leaf area index shows improvement over time for crops. NASA scientists developed algorithms to determine leaf area index using data from global-scale environmental satellites. ARS researchers in Beltsville, Maryland showed that the initial results with the NASA Terra satellite�s MODIS sensor had large errors in leaf area index for corn and soybean. As NASA refined the algorithm, the resulting leaf area index estimates were closer to the measured data. With the latest version of NASA�s algorithm, MODIS-derived leaf area index still showed a significant offset. For predicting crop yields, MODIS 250-m red and near-infrared reflectances were better than the NASA standard data products.
Impacts
(N/A)
Publications
- Tan, B., Masek, J., Wolfe, R., Gao, F.N., Huang, C., Vermote, E., Sexton, J., Ederer, G. 2013. Improved forest change detection with terrain illumination corrected landsat images. Remote Sensing of Environment. 136:469-483.
- Yebra, M., Chuvieco, E., Jurdao, S., Danson, M., Dennison, P., Hunt, E.R., Qi, Y., Riano, D., Zylstra, P. 2013. A review of satellite-based methods of estimating live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment. 136:455-468.
- Denver, J., Ator, S.W., Lang, M., McCarty, G.W., Clune, J., Fisher, T., Fox, R., Gustfson, A. 2014. Nitrogen fate and transport through palustrine depressional wetlands along an alteration gradient in an agricultural landscape, upper Choptank Watersheds, Maryland, USA. Journal of Soil and Water Conservation. DOI: 10.2489/jswc.69.1.1.
- Feng, M., Sexton, J., Huang, C., Masek, J., Vermote, E., Gao, F.N., Narasimhan, R., Channan, S., Wolfe, R., Townshend, J. 2013. Global, long- term surface reflectance records from Landsat. Remote Sensing of Environment. 134:276-293.
- Cheng, Y., Middleton, E.M., Zhang, Q., Corp, L.A., Huemmrich, K.F., Campbell, P., Cook, B.D., Kustas, W.P., Daughtry, C.S. 2013. Integrating solar induced flourescence and the photochemical reflectance index for estimating gross primary production in a cornfield. Remote Sensing. 5:6857- 6879.
- Gassman, P.W., Sadeghi, A.M., Srinivansan, R. 2013. Applications of the SWAT model special section: overview and insights. Journal of Environmental Quality. 43:1-8. DOI: 10.2134/jeq2013.11.0466.
- Lee, S., In Young, Y., Sadeghi, A.M., McCarty, G.W., Hively, D.W. 2013. Assessing effectiveness of winter cover crops to improve water quality. Journal of Hydrology and Earth System Sciences. 10:14229-14263.
- Hunt, E.R., Daughtry, C.S. 2014. Assessment of chlorophyll meter calibrations for chlorophyll content using leaf spectral transmittances. Agronomy Journal. 106:931-939.
- Wang, C., Hunt, E.R., Zhang, L., Guo, H. 2013. Spatial distributions ofC3 and C4 grass functional types in the U.S. great plains and their despendency on inter-annual climate variability. Remote Sensing of Environment. 138:90-101.
- Stern, A.J., Doraiswamy, P.C., Hunt, E.R. 2014. Comparison of different MODIS data product collections over an agricultural area. Geoscience and Remote Sensing Letters. 5:1-9.
- Zhu, X., Gao, F.N., Liu, D., Chen, J. 2012. An enhanced neighborhood similar pixel interpolator approach for removing thick clouds in landsat images. Geoscience and Remote Sensing Letters. 9(3):521-525.
- Zhang, X., Beeson, P.C., Link, R., Manowitz, D., Izaurralde, R.C., Sadeghi, A.M., Thomson, A.M., Sahajpal, R., Srinivasan, R., Arnold, J.G. 2013. Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python. Environmental Modelling & Software. 46:208-218.
- He, T., Liang, S., Yu, Y., Gao, F.N., Liu, Q. 2014. Greenland surface albedo changes 1981-2012 from satellite observations. Geophysical Research Letters. DOI:10.1088/1748-9326/8/4/044043.
- Roman, M., Gatebe, C., Shuai, Y., Wang, Z., Gao, F.N., Masek, J., He, T., Schaaf, C. 2013. Use of in situ and airborne multiangle data to assess MODIS-and landsat-based estimates of directional reflectance and albedo. IEEE Transactions on Geoscience and Remote Sensing. 51(3):1393-1404.
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Progress 10/01/12 to 09/30/13
Outputs
Progress Report 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. 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) 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. Significant Activities that Support Special Target Populations: Research is being conducted in collaboration with the University of Maryland Eastern Shore, an 1890 Land-Grant University on precision agriculture and genotype trials. Accomplishments 01 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. 02 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. 03 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. 04 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.
Impacts
(N/A)
Publications
- 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.
- Hunt, E.R., Doraiswamy, P.C., McMurtrey III, J.E., Daughtry, C.S., Perry, E.M., Akhmedov, B. 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation. 21:102-112.
- Yakirevich, A., Gish, T.J., Simunek, J., Van Genuchten, M.T., Pachepsky, L. , Nicholson, T.J. 2009. Potential impact of seepage face on solute transport to a pumped well. Vadose Zone Journal. 9:686-696.
- Wang, L., Hunt, E.R., Qu, J.J., Hao, X. 2013. Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry- matter indices. Remote Sensing of Environment. 129:103-110.
- Serbin, G., Hunt, E.R., Daughtry, C.S., Brown, D.J., McCarty, G.W. 2013. Assessment of spectral indicies for crop residue cover estimation. Geoscience and Remote Sensing Letters. 4(6):552-560.
- Stern, A.J., Hunt, E.R., Doraiswamy, P.C. 2012. Changes of crop rotation in Iowa determined from the USDA-NASS cropland data layer product. Journal of Applied Remote Sensing (JARS). 6:1-17.
- Sadeghi, A.M., Cardosol, F., Shelton, D.R., Shirmohammadi, A., Pachepsky, Y.A., Dulaney, W.P. 2012. Effectiveness of vegetated filter stripsin retention of E. coli and salmonella from swine manure slurry. Environmental Management. 110:1-7.
- Ustin, S.L., Riano, D., Hunt, E.R. 2013. Estimating canopy water content from spectroscopy. Israel Journal of Plant Science. 60(1):9-23.
- Cheng, Y., Middleton, E.M., Zhang, Q., Corp, L.A., Dandois, J., Kustas, W. P. 2012. The photochemical reflectance index from directional cornfield reflectances: Observations and simulations. Remote Sensing of Environment. 124:444-453.
- Houborg, R., Cescatti, A., Migliavacca, M., Kustas, W.P. 2013. Satellite retrievals of leaf chlorophyll and photosynthetic capacity for improved modeling of GPP. Agricultural and Forest Meteorology. 177:10-23.
- Knyazikhin, Y., Schull, M.A., Stenberg, P., Mottus, M., Rautiainen, M., Yang, Y., Marshak, A., Carmona, P.L., Kaufmann, R., Lewis, P., Vanderbilt, V.C., Davis, F., Baret, J., Jacquemoud, S., Lyapustin, A., Myneni, R.B. 2012. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences. 110(3):E185-E192.
- Townshend, J., Masek, J., Huang, C., Vermonte, E., Gao, F.N., Channan, C., Sexton, J., Fenng, M., Narasimhan, R., Kim, D., Song, K., Song, D., Song, X. 2012. Global characterization and monitoring of forest cover using Landsat data: opportunities and challanges. International Journal of Digital Earth. DOI: 10.1080/17538947.2012.713190.
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Progress 10/01/11 to 09/30/12
Outputs
Progress Report 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. 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. Significant Activities that Support Special Target Populations: Research is being conducted in collaboration with the University of Maryland Eastern Shore, an 1890 Land-Grant University. The collaboration focuses on remote sensing research to develop precision management of nitrogen fertilizer applications to corn and wheat production fields in the lower Chesapeake Bay watershed. This research is supported by NIFA Capacity Building Grants awarded to faculty UMES. Accomplishments 01 Long-term herbicide volatilization evaluated. Field investigations over the past 14 years have demonstrated that volatilization (vapor loss to t atmosphere) is perhaps the most critical loss pathway whereby herbicides leave a production field and enter neighboring ecosystems. The herbicid volatilization experiments conducted in Beltsville, Maryland are the longest record of herbicide vapor loss observations worldwide. Herbicid 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 researc will affect USDA and U.S. Environmental Protection Agency policies with regard to herbicide use and the data will improve pesticide behavior models. 02 Airborne hyperspectral imagery to map soil properties. Spatial assessme 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 regressi models to develop detailed maps of soil properties was investigated. Th 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 t spatial distribution of soil texture, fertility, and carbon storage with agricultural fields. 03 A robust spectral index for assessing crop residue cover across landscap 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 resid 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 wer found to be robust, not affected crop or soil type in the Pacific Northwest, the Central and Northern Great Plains, and the Midwest region of the U.S. These spectral indices can provide reliable estimates of cro residue cover and soil tillage intensity for regional assessments of conservation practices. 04 Hyperspectral remote sensing method to estimate leaf dry matter content. Information on leaf dry matter content is required for accurate estimati of carbon sequestration, plant nutrient concentrations, and wildfire fue 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, Marylan collaborated on the development of a new remote-sensing index, associate 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. 05 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 river 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 SW has not been rigorously quantified, so often the predictions are adjuste using an arbitrary margin of safety. Scientists from Beltsville, Maryla and the University of Maryland, College Park, developed a new approach t 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.
Impacts
(N/A)
Publications
- 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.
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Progress 10/01/10 to 09/30/11
Outputs
Progress Report 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. 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. 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. Partial Least Squared (PLS) regression models were developed for data contained in hyperspectral imagery. These models predicted soil properties including organic carbon and particle size distribution (texture). Aircraft imagery, flux observations and in-situ canopy and soil measurements were collected at the field site in Beltsville, Maryland for evaluating relationships between modeled leaf chlorophyll content and canopy light-use efficiency and used in mapping carbon and water fluxes. Flux observations from ARS collaborators in Bushland, Texas and Ames, Iowa were processed for additional model validation studies. Low-cost true-color and color-infrared digital cameras were compared for different rates of nitrogen fertilization in corn and rye crops in Beltsville, Maryland. Furthermore, nitrogen rate experiments were established at the University of Maryland Eastern Shore. The new remote sensing methods are being compared to currently accepted methods of chlorophyll meter readings, on-the-go sensor NDVI, and canopy reflectance spectra. In collaboration with the U.S. Geological Survey, field data were collected in the Choptank Watershed (Maryland) on the amounts of nitrogen incorporated into winter cover crops and remaining in the soil. In collaboration with the Forest Service information on wetland location and hydro-period was collected in the Tuckahoe sub-basin for use in modeling water quality. Data collection continued for a long- term field-scale experiment in Beltsville, Maryland where atrazine and metolachlor volatilization and surface runoff losses were simultaneously measured. Using an aerial lift, multiple digital camera images, with pixel sizes from 5 cm to 2 m, were collected over the same targets of crops, weeds, a meadow, and illicit drug crops to determine if image texture and pattern recognition software may be used for classification. Reflectance spectra were also acquired for the different targets. Furthermore, collaborations were established with ARS scientists in Cheyenne, Wyoming and Dubois, Idaho to use the camera technologies for determination of range health. The Weed Invasion Susceptibility Prediction (WISP) model was revised using field and remote sensing data obtained during the multidisciplinary program, The Ecological Area-wide Management (TEAM) of Leafy Spurge. Remote sensing and geographic information data were obtained for Theodore Roosevelt National Park, which was another field site for TEAM Leafy Spurge, for model testing. Accomplishments 01 Low-cost color-infrared digital camera for monitoring crops and rangelan Digital camera technologies are readily available at low cost; however sensors with near-infrared bands are better for monitoring crops because leaves and canopies are highly reflective at near-infrared wavelengths. Scientists in Beltsville, Maryland developed a method in which the red channel of some digital cameras is modified to respond to near-infrared radiation instead. USDA was awarded a patent for this method in order t help private companies bring low-cost color-infrared sensors to market. The scientists used the new technology to monitor growth of winter cover crops and rangeland health. 02 New hyperspectral remote sensing method for estimating leaf dry matter content. Leaf dry matter content is obscured in remotely sensed data because of liquid water in the leaves absorbs much more of the short-wav infrared radiation emitted by the sun. Using advanced hyperspectral sensors, scientists from George Mason University (Fairfax, Virginia) and ARS scientists in Beltsville, Maryland collaborated on the development o a new index based on absorption spectra of liquid water and dried plant materials. The new index was highly correlated with dry matter content leaf and canopy scales. Information on leaf dry matter content is required for estimation of carbon sequestration, plant nutrient concentrations, and wildfire fuel moisture content. 03 Herbicide volatilization exceeds herbicide runoff losses. Surface runof was thought to be the major off-site transport mechanism for herbicide. However, until recently no field investigations monitored both surface runoff and turbulent volatilization fluxes simultaneously. An 8-year, field-scale experiment in Beltsville, Maryland was conducted where herbicide (atrazine and metolachlor) volatilization and surface runoff losses were simultaneously monitored and evaluated. Results demonstrate that regardless of weather conditions, volatilization losses consistentl exceeded surface runoff losses. Surprisingly, herbicide volatilization losses were up to 25 times larger than herbicide surface runoff losses. The research will affect USDA and USEPA policy with regard to herbicide behavior and the data will be used to develop or improve pesticide behavior models. 04 Improved data mining approaches for measurement of soil properties by us of infrared spectroscopy. Near-infrared and mid-infrared diffuse reflectance spectroscopy hold great promise for rapid measurement of soi properties such as organic carbon, but ability to extract information fr soil spectra is limited by data mining approaches. Mathematical treatme of spectral data is shown to be an important determinant of success. Th results indicated that calibration models for soil are quite sensitive t the complexity of the model and the ability of locally weighted regressi helped selection of appropriate calibration samples for development of robust calibrations. These findings will improve our ability to develop robust calibrations for soil properties such as organic carbon and shoul enable better assessment of soil carbon storage in agricultural ecosyste under management to sequester atmospheric carbon in soil. 05 Limited information on spatial distribution of manure applied to agricultural fields can cause significant uncertainty in model predictio of bacteria in runoff. Concerns for microbial safety of surface water facilitate development of predictive models to estimate concentrations o pathogen and indicator organisms from manure-fertilized fields in runoff Experiments carried out in Beltsville, Maryland indicated that there was high spatial variation of the concentrations of fecal coli-form bacteria in applied manure. Using average bacterial concentrations lead to substantial over-estimates of the amount of bacteria reaching the edge o a field suggesting that inaccurate representation of bacteria concentrations in manure with a small number of samples can substantiall distort estimates of bacteria loss from the field in runoff. Therefore better sampling and modeling strategies need to be developed. 06 Low-cost remote sensing for crop nitrogen management using a new chlorophyll index for digital cameras. Leaf chlorophyll concentration i closely related to nitrogen status and the demand for fertilization; however, satellite data are expensive, have coarse spatial resolution, have long turnaround times for information delivery, and are susceptible to clouds. A new index was developed called the Triangular Greenness Index, which has high sensitivity to leaf chlorophyll concentration and not sensitive to other soil and canopy variables. Digital cameras are readily mounted onto small unmanned aerial vehicles so individual fields can be quickly monitored at high spatial resolution and low cost enablin better nitrogen management decisions to insure crop yields and reducing fertilizer runoff. 07 Improving capacity of developing countries to participate in carbon cred markets. International aid and development funding for agricultural improvement in developing countries may be linked to verifiable credits for carbon sequestration in agricultural soils as influenced by better soil management. Improved ability for within country measurement of soi carbon and modeling the fate of carbon in agricultural production system can build capacity of developing countries to engage in emerging carbon markets. Evaluation and validation of low technology approaches such as the loss on ignition technique for measurement soil carbon has improved the capabilities of soil analytical laboratories in West Africa to efficiently monitor soil carbon in agricultural systems. Soil carbon modeling efforts has reduced uncertainty concerning sequestration and storage of soil carbon in agricultural production systems in both West Africa and Central Asia. A combined capacity for measurement and modeli soil carbon in agricultural soils will strengthen capacity of developing countries to gain development aid for improved agricultural production a greater food security.
Impacts
(N/A)
Publications
- Wang, L., Hunt Jr, E.R., Qu, J.J., Hao, X., Daughtry, C.S. 2010. Estimating dry matter content of fresh leaves from residuals between leaf and water reflectance. Remote Sensing Letters. 2(2):137-145.
- McCarty, G.W., Hively, W.D., Reeves, J.B., Lang, M.W., Lund, E., Weatherbee, O. 2010. Infrared sensors to map soil carbon in agricultural ecosystems. In: Viscarra-Rossel, R., McBratney, A., Minasny, B., editors. Proximal Soil Sensing, Progress in Soil Science Volume 1. New York, NY: Springer Science. 14:165-176.
- Hunt, E.R., Gillham, J.H., Daughtry, C.S. 2010. Improving potential geographic distribution models for invasive plants by remote sensing. Ecology and Management of Rangelands. 63(5):505-513.
- Anderson, M.C., Hain, C., Wardlow, B., Pimstein, A., Mecikalski, J., Kustas, W.P. 2011. Evaluation of a drought index based on thermal remote sensing of evapotranspiration over the continental U.S. Journal of Climate. 24:2025-2044.
- Hunt, E.R., Hively, W.D., McCarty, G.W., Daughtry, C.S., Forrestal, P., Carr, J.L., Allen, N.F., Fox-Rabinovitz, J., Miller, C. 2011. NIR-green- blue high-resolution digital images for assessement of winter cover crop biomass. GIScience and Remote Sensing. 48(1):86-89.
- McCarty, G.W., Reeves, J.B., Yost, R., Doraiswamy, P.C., Doumbia, M. 2010. Evaluation of methods for measuring soil organic carbon in West African soils. African Journal of Agricultural Research. 5(16):2169-2177.
- Hunt, E.R., Daughtry, C.S., Long, D.S., Eitel, J.U. 2011. Remote sensing of chlorophyll content at leaf and canopy scales using a visible band index. Agronomy Journal. 103:1090-1099.
- Houborg, R., Anderson, M.C., Daughtry, C.S., Kustas, W.P., Rodell, M. 2011. Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model. Remote Sensing of Environment. 115:1694-1705.
- Baffaut, C., Sadeghi, A.M. 2010. Bacteria modeling with SWAT for assessment and remediation studies � a review. Transactions of the ASABE. 53(5):1585-1594.
- Wang, L., Hunt, E.R., Qu, J.J., Hao, X., Daughtry, C.S. 2010. Estimation of canopy foliar biomass with spectral reflectance measurements. Remote Sensing of Environment. 115:836-840.
- Cheng, Y., Middleton, E.M., Huemmrich, K.F., Zhang, Q., Campbell, P., Corp, L.A., Russ, A.L., Kustas, W.P. 2010. Utilizing in situ directional hyperpectral measurements to validate bio-indicator simulations for a corn crop canopy. Ecological Informatics. 5:330-338.
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