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

Location: Hydrology and Remote Sensing Laboratory

2020 Annual Report

Accomplishments
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. https://doi.org/10.1016/j.wasman.2019.12.049.
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. https://doi.org/10.1016/j.jhydrol.2020.124779.
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. https://doi.org/10.1016/j.apr.2020.03.012.
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. https://doi.org/10.3390/rs11050488.
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. https://doi.org/10.3390/rs11182072.
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. https://doi.org/10.3390/rs11161857.
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. https://doi.org/10.3390/rs11202439.
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. https://doi.org/10.1016/j.isprsjprs.2020.01.012.
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. https://doi.org/10.1016/j.jhydrol.2019.124415.
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. https://doi.org/10.1016/j.jhydrol.2020.124608.
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. https://doi.org/10.1016/j.advwatres.2019.103380.
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. https://doi.org/10.1016/j.pce.2020.102859.
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. https://doi.org/10.2134/ael2019.09.0040.
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. https://doi.org/10.3390/rs11222622.