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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #431325

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

2020 Annual Report

Objective 1: Assess the status and trends of the Lower Chesapeake Bay agroecosystem through measurements and modeling. Subobjective 1.1 Establish long-term data streams for the LCB-LTAR project to assess agroecosystem status and trends. Sub-objective 1.2 Assess data streams as a function of spatial differences in land use. Objective 2: Develop and test remote sensing methods to assess crop conditions, conservation practices, and nutrient use efficiency. Subobjective 2.1: Improve remote sensing methods for assessing crop conditions using plant phenology at field to watershed scales. Subobjective 2.2: Develop remote sensing methods to assess crop residue cover and soil tillage intensity at field to watershed scales. Subobjective 2.3: Develop and test methods using high-spatial-resolution remote sensing from small unmanned aircraft systems for precision agriculture. Subobjective 2.4: Retrieve leaf optical properties by remote sensing foliar water content to improve estimation of plant nitrogen status. Subobjective 2.5: Use LiDAR, Synthetic Aperture Radar, and Landsat to map and characterize wetlands and riparian buffers. Objective 3: Quantify environmental processes within agricultural landscapes to evaluate ecosystem services and best management practices. Subobjective 3.1: Improve measurement and modeling approaches to describe agrochemical emissions and transport from agricultural operations. Subobjective 3.2: Characterize the influence of canopy structure on the deposition of agrochemicals to riparian buffers. Subobjective 3.3: Quantify the spatial and temporal variability and assess the fate of atmospheric ammonia on the Delmarva Peninsula. Subobjective 3.4: Assess the effects of wetland hydroperiod on carbon storage. Subobjective 3.5: Quantifying impacts of watershed characteristics and crop rotations on winter cover crop nitrate uptake capacity within agricultural watersheds using the SWAT model.

Much of the research will be conducted within the LCB-LTAR study area (Appendix 2) in support of the LTAR network goals. Two types of studies will be performed as part of the network, monitoring for long-term trends and conducting experiments to identify, quantify, and understand the underlying agroecosystem processes causing the trends. Thus, measurements of soil, water, and air quality are a priority. Within the LCB-LTAR, the Choptank River Watershed on the Delmarva Peninsula (Figure 3) has been a research site since 2004 for the USDA-NRCS Conservation Effects Assessment Program (CEAP) (Hively et al. 2011; Maresch et al. 2008; McCarty et al. 2008; Niño de Guzmán et al. 2012; Richardson et al. 2008; USDA-NRCS 2011; Tomer and Locke 2011; Tomer et al. 2014, Whithall et al. 2010). The approaches include remote sensing, in-situ monitoring, long term sampling scenarios, and modeling efforts. The Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site consists of a 22-ha production field and adjacent riparian area that has been studied by this team since 1998. OPE3 is an outdoor laboratory at the USDA-ARS Beltsville Agricultural Research Center (BARC) to explore energy, water, nutrient, and agrochemical processes.

Progress Report
Fiscal Year 2020 was the fourth year of the project in National Program 212, Soil and Air. ARS scientists from Beltsville, Maryland, made progress on all three project objectives and their sub-objectives, which address the three components of National Program 212 in an interdisciplinary manner. The first objective is establishment and data collection for the Lower Chesapeake Bay Long-Term Agroecosystem Research (LCB-LTAR) site. The two main sites of the LCB-LTAR project are the Beltsville Agricultural Research Center (BARC) in Beltsville, Maryland, and the Choptank River watershed (located on the Delmarva Peninsula). Landsat 4, 5, 7 and 8 data over the study sites were acquired from the USGS. Furthermore, USGS acquired high-resolution WorldView-3 (WV-3) data over selected sites in the Choptank River watershed. In situ water quality data were collected at Tuckahoe and Greensboro gaging stations in the Choptank River watershed and monthly grab samples were collected at 15 subwatersheds. Monthly integrative samples from POCISs (polar organic chemical integrative samplers) were collected at Tuckahoe and Greensboro gage stations for monitoring MESA, a chemical from the breakdown of the herbicide metolachlor. Geographical data on poultry house locations on the Delmarva Peninsula were compiled and poultry house density maps were produced for models of air quality. The effects of temporary wetlands on water quality and carbon storage was assessed at the watershed-scale. The Soil-Water Assessment Tool (SWAT) and the Agricultural Productivity/Environmental Extender (APEX) model were modified and calibrated for evaluation of new components simulating wetlands. The second objective is research on using remote sensing to measure biophysical variables related to agricultural production, nitrogen use efficiency, and environmental assessment. The major site for this research is the long-term experiment, Optimizing Productivity for Economic and Environmental Enhancement (OPE3), located in BARC, with other sites located around the United States for comparison. Crop condition monitoring at the field scale requires remote sensing data from multiple sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was modified to combine data from different sensors by considering the difference in spectral response from different sensors. The method was evaluated for winter wheat monitoring. A new algorithm was developed to monitor vegetation phenology at 30-m resolution using two routine data products, the Harmonized Landsat and Sentinel-2 (HLS) and Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance. The 30-m green-up dates detected from the new algorithm were compared to the standard VIIRS phenology product and ground observations. MODIS data were used to assess crop growth conditions in Iowa. An approach to monitoring crop conditions based on crop growth stages has been developed. The crop growth conditions were assessed using USDA crop progress reports and compared to the traditional approach based on calendar dates. The within-season crop emergence (WISE) mapping approach was applied to map emergence dates for winter cover crops in the Choptank River Watershed. An algorithm to determine the date when winter cover crops are terminated is under development. The cover crop emergence and termination dates for the 2019-2020 growing season were provided weekly to the Maryland Department of Agriculture (MDA) winter cover crop program. Tillage practices that maintain crop residue cover on the soil surface reduce soil erosion and improve soil and water quality. Direct methods of measuring crop residue cover are impractical for assessing many fields over a large region in a timely manner. Landsat satellites provide 16-day repeat global coverage with several spectral bands; however, empirical indices of crop residue require extensive ground data for reliable calibrations. The WorldView-3 (WV-3) satellite does not provide global coverage, but its multiple spectral bands at shortwave-infrared wavelengths are used to estimate residue cover with a physically-based index, which is robust and requires minimal ground data. Landsat and WorldView-3 images were acquired over study sites in the Lower Chesapeake Bay (Maryland) and Southfork (Iowa) watersheds. The WV-3 index was used to calibrate the Landsat empirical index, which was then used to estimate residue cover for the two watersheds. Research on application of unmanned aircraft for agricultural remote sensing conducted in Oregon concluded with two publications. Collaboration was initiated with the Cover Crop Systems Project at BARC to compare true-color cameras with 5-mm pixel resolution to multispectral sensors with 5 cm pixel resolution. Adaption of a canopy radiative transfer model to simulate hyperspectral reflectance progressed, but not enough to meet 48-month milestone. Application of computer learning to agriculture is an important USDA goal. One algorithm, deep neural networks, was explored for analyses of Synthetic Aperture Radar data to map inundated areas of temporary wetlands. The third objective concerns reducing the environmental risks of agricultural operations. Research focuses on elucidating the physical/chemical/biological aspects of environmental processes and using this new knowledge to assess and improve conservation measures. Significant progress was made toward implementing the relaxed eddy accumulation (REA) approach to monitor metolachlor and atrazine fluxes. Analyses of the data collected during the 2019 growing season confirmed the applicability of the REA approach and its ability to provide measurements of volatilization losses at a much higher temporal resolution than the historical gradient-based approach. It also illuminated several refinements that enhance the accuracy of the approach. Additionally, initial field trials were conducted for the pesticide transport project developed in 2019 in collaboration with ARS scientists from Ames, Iowa and other scientists; this work will greatly augment the work conducted at OPE3 by elucidating the mechanisms controlling pesticide loss at the time of application and under a broader range of environmental conditions. Research activities focusing on ammonia and particulate emissions from poultry houses culminated in the development of a simple approach for assessing the effectiveness of vegetative buffers. The results of the work demonstrated the utility of the model under a range of environmental conditions. Research activities focusing on the volatilization process using data collected at OPE3 via relaxed eddy accumulation (REA) were more limited because the data represent only a very limited range of conditions that are insufficient to conduct a thorough investigation of the influence of similarity to agrochemical volatilization. Modeling approaches to utilize poultry house density maps for examining the transport of poultry house emissions were initiated. Data assessing impact of wetland hydroperiod on wood decomposition were analyzed. An overview paper on the use of the SWAT model for assessment of winter cover crop performance was published.

1. Vegetative buffers mitigate poultry house emissions. The emissions from poultry houses, which include ammonia and particulate matter (PM), can adversely affect the health of people and ecosystems nearby. Vegetative environmental buffers (VEBs) consisting of trees, shrubs, and grasses are low-cost methods to capture poultry house emissions, but measuring their effectiveness is difficult and costly. ARS scientists in Beltsville, Maryland, working with partners at the University of Maryland, Oklahoma State University, and the University of Delaware developed a simple approach to determine the effectiveness of VEBs under different conditions using a Gaussian plume model. NRCS is currently updating guidelines for the use of VEBs as a remediation strategy.

2. Assessment of crop residue cover with remote sensing. Crop residue cover provides a reliable indicator of soil tillage intensity, but direct measurements of crop residue cover are impractical for assessing many fields in a timely manner. Empirical residue indices that use the broad spectral bands of Landsat require extensive ground-truth data to reliably estimate crop residue cover. Physically-based crop residue indices that use the narrow spectral bands of the WorldView-3 (WV-3) satellite are robust and require minimal ground-truth data. However, WV-3 scenes are small and cannot provide the wall-to-wall coverage required for monitoring tillage that Landsat provides. The crop residue cover from the classified WV-3 image was used as training data to calibrate the empirical Landsat indices, resulting in greater accuracy for residue cover in a Landsat image covering the Eastern Shore of the Chesapeake Bay. Combining WV-3 and Landsat is a low-cost and practical method for timely monitoring of soil tillage intensity regionally.

3. Improved method for remote sensing of crop condition assessment. Crop growth condition information can benefit farmers in scheduling irrigation, fertilization, and harvest operation. Satellite remote sensing data have been used for crop condition monitoring for several decades. Traditional remote sensing approaches compare vegetation indices of the current year to previous years on the same calendar date, ignoring the year-to-year variability of crop growth stages. A new remote sensing approach compares vegetation indices at the same crop growth stages estimated from active accumulated temperature. Results show that the crop growth condition assessment using aligned growth stages is more consistent with the reported USDA National Agricultural Statistics Service (NASS) results than using aligned calendar dates.

4. High resolution maps of vegetation phenology from satellite image data fusion. Land surface phenology (LSP) provides critical information for investigating forest and crop growth, but the satellite sensors, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), only provide phenology information at coarse resolution (500-m per pixel). Agricultural lands have mixtures of different crops, each of which differs in phenology and planting date, therefore phenology information at the field scale requires the spatial resolution of Landsat (30-m per pixel). However, the temporal frequency of Landsat coverage is inadequate for reliable phenology measurements, particularly in areas with large amounts of clouds. A new algorithm was developed to detect vegetation phenology at 30-m resolution using the Harmonized Landsat and Sentinel-2 (HLS) and VIIRS surface reflectance data products. At different times during the growing season, crops have different sensitivities to environmental stress. Using the new algorithm the impact of stresses, such as drought, may be calculated more accurately.

5. Low-cost agricultural remote sensing with unmanned aircraft. Remote sensing from unmanned aircraft promised to be a low-cost method for monitoring crop health. However, the costs have been much higher than expected, because the image products require large numbers of overlapping images to be stitched together. Scientists in Beltsville, Maryland, showed that small images acquired during low-altitude unmanned-aircraft flights over a field are analogous to sample plot transects for crop heath. Each sample image displayed as a point in a geographic information system (GIS) and interpolated to show crop health for the whole field. The unmanned-aircraft images do not require overlap and are not stitched together saving time and money. With modern farm equipment having on-board global positioning systems and GIS, the resulting field map is used for application of fertilizers and agrochemicals when and where they are needed.

6. Determining water flow pathways from depressional wetlands to streams. Depressional wetlands are inundated intermittently and have higher productivity and biodiversity compared to the surrounding woodlands. Watershed models route precipitation downstream to estimate surface stream flow and ground water storage, but these models perform poorly in watersheds with many depressional wetlands. Scientists in Beltsville, Maryland, used in-situ observations of surface and groundwater levels to show the primary downstream water-flow pathway from depressional wetlands is through the ground. This knowledge has been incorporated into a new version of the SWAT and other models to improve watershed and ecosystem management.

7. Incorporated riverbed biogeochemical processes in a revised USDA SWAT model. Despite the widely recognized importance of aquatic processes for bridging gaps in the global carbon cycle, the role of riverbed processes for carbon flows and stocks in aquatic environments is not well understood. The USDA Soil Water Assessment Tool (SWAT) model was modified to include two new modules to capture sediment dynamics for particulate and dissolved organic carbon. The revised model was tested on a four-year observational dataset from a watershed in the Lower Chesapeake Bay long-term agroecosystem research site and model predictions showed good agreement with the data. Improved understanding of carbon fluxes and stocks in riverbeds are necessary to determine the value of ecosystem services provided at the watershed scale.

Review Publications
Fischer, S., McCarty, G.W., Ramirez, M., Torrents, A. 2020. Dissolved organic matter (DOM) profiles and nutrient mineralization rates of anaerobically digested biosolids. International Biodeterioration and Biodegradation.
Lee, S., McCarty, G.W., Moglen, G.E., Li, X., Wallace, C. 2020. Assessing the effectiveness of riparian buffers for reducing organic nitrogen loads in the Coastal Plain of the Chesapeake Bay watershed using a watershed model. Journal of Hydrology.
Yang, Z., Yao, Q., Buser, M.D., Alfieri, J.G., Li, H., Torrents, A., Mcconnell, L., Downey, P.M., Hapeman, C.J. 2020. Modification and validation of the Gaussian Plume Model (GPM) to predict ammonia and particulate matter dispersion . Popular Publication.
Campbell, P., Huemmerich, K., Middleton, E., Ward, L., Daughtry, C.S., Burkart, A., Russ, A.L., Kustas, W.P. 2019. Diurnal and seasonal dynamics in vegetation fluorescence associated with photosynthetic function and CO2 dynamics. Remote Sensing.
Dennison, P., Kokaly, R., Thompson, D., Qi, Y., Daughtry, C.S., Meerdink, S., Quemada, M., Gader, P., Robertson, D., Wetherley, E. 2019. Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra. Remote Sensing of Environment.
Hively, W., Schermeyer, J., Lamb, B., Daughtry, C.S., Quemada, M. 2019. Mapping crop residue by combining Landsat and WorldView-3 satellite imagery. Remote Sensing.
Quan, Y., Yang, Z., Di, L., Rahman, M., Xue, L., Tran, Z., Gao, F.N., Yu, E., Zhang, X. 2019. Crop growth condition assessment at county scale based on heat-aligned growth stages. Remote Sensing. 11:2439.
Zhang, X., Wang, J., Henebry, G., Gao, F.N. 2020. Development and evaluation of a new algorithm for detecting 30m land surface phenology from VIIRS and HLS time series. Journal of Photogrammetry and Remote Sensing. 161:37-51.
Qi, J., Lee, S., Zhang, X., Yang, Q., McCarty, G.W., Moglen, G.E. 2019. Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US. Journal of Hydrology. 581:124415.
Lee, S., McCarty, G.W., Moglen, G.E., Lang, M., Jones, C., Palmer, M., Yeo, I., Anderson, M.C., Sadeghi, A.M., Rabenhorst, M. 2020. Seasonal drivers of geographically isolated wetland hydrology in a low-gradient, Coastal Plain landscape. Journal of Hydrology.
Qi, J., Zhang, X., Lee, S., Moglen, G.E., Sadeghi, A.M., McCarty, G.W. 2019. A coupled surface water storage and subsurface water dynamics model in SWAT for characterizing hydroperiod of geographically isolated wetlands. Advances in Water Resources.
Yao, Q., Torrents, A., Li, H., Buser, M., McConnell, L., Downey, P.M., Hapeman, C.J. 2019. Using a vegetative environmental buffer to reduce the concentrations of volatile organic compounds in poultry-house atmospheric emissions. Journal of Food Chemistry. 66:8231-8236.
Cao, Z., Chen, S., Gao, F.N., Li, X. 2020. Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion. Physics and Chemistry of the Earth. 116:102859.
Hunt, E.R., Daughtry, C.S., Stern, A.J., Russ, A.L. 2019. Linear transects of imagery increase crop monitoring using fixed-wing unmanned aircraft systems. Agricultural and Environmental Letters. 04(1):1-4.
Hunt, E.R., Stern, A.J. 2019. Evaluation of incident light sensors on unmanned aircraft for calculation of spectral reflectance. Remote Sensing. 11(22):2622.