Location: Hydrology and Remote Sensing Laboratory
2017 Annual Report
Objectives
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.
Approach
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 2017 was the first year of a new 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, in an interdisciplinary manner, the three components of National Program 212.
Objective 1 concerns data collection and monitoring in the Lower Chesapeake Bay Long Term Agroecological Research study area. Two additional water quality monitoring instruments were deployed at gauging stations established by the United States Geological Survey for monitoring turbidity, nitrate, total organic carbon, and dissolved organic carbon in tributaries of the Choptank River (Maryland). Meteorological data from two sites are now streaming directly into the online archive at the USDA National Agricultural Library (Beltsville, Maryland). Weather-proof digital cameras were installed to bring two carbon and energy flux towers into compliance with established standards. Paired wetlands were equipped with meteorological instruments and digital cameras to measure moisture and vegetation changes.
Research for Objective 2 concerns the development and testing methods using remote sensing by airborne and satellite sensors. Data fusion tools for merging different types of satellite imagery were integrated with tools to assess crop phenology. The combined tool set was applied for erosion risk assessments in Germany and for modeling the effects of the increased urbanization on plant phenology in Utah. Furthermore, data fusion results were evaluated for the South Fork River (Iowa) and the Choptank River (Maryland) watersheds. Ground-based reflectance spectra were acquired for plots with a broad range of crop residue cover and soil moisture conditions. Spectral bands for Landsat, Sentinel-2, and WorldView-3 satellites were calculated from the spectra for evaluation of residue cover indices. Satellite images were acquired for study sites in the Choptank and the South Fork River watersheds. Flights of small unmanned aircraft are not allowed over the established research site for precision agriculture, “Optimizing Production for Economic and Environmental Enhancement (OPE3)” in Beltsville, Maryland, because the area is in the Federal Aviation Administration’s Flight Restricted Zone around Washington, DC. Therefore, experiments were initiated outside this zone. Furthermore, experiments testing capabilities of small unmanned aircraft were established in production fields located in the Choptank River watershed. Wetlands in the Choptank River watershed with different durations of inundation were selected using Landsat imagery. Soil samples were collected in these wetlands for measuring carbon storage.
Objective 3 is to quantify environmental processes and evaluate ecosystem services in the Choptank River watershed. Substantial progress was made toward overcoming practical limitations using the relaxed eddy accumulation approach for sampling concentrations of agrochemicals in air. Techniques for collecting rain water from both stem-flow and through-fall were identified. Additionally, a “leaf rinsing” method was developed for sampling agrochemicals deposited on tree canopies in the absence of rain; the new method accounted for the distribution of leaves throughout a canopy and the sample size required for accurate estimates of total deposition. Significant progress was made in the development of databases needed for using a process-based biogeochemistry model for two tributaries, in order to assess wetland location and function across an agricultural landscape.
Specific progress was made with respect to crop residue and moisture conditions, burned crop residues, and the remote sensing of crop nitrogen status as follows:
1. Variations of moisture conditions within fields and across landscapes increase the uncertainty of remotely-sensed estimates of crop residue cover. ARS scientists from Beltsville, Maryland and Madrid, Spain developed protocols that reduced this uncertainty and significantly improved estimates of residue cover from satellite data. Residue cover indices that used the relatively narrow spectral bands of WorldView-3 were more robust to variations in moisture conditions compared to residue indices that used the broad spectral bands of Landsat-8 or Sentinel-2. These techniques provide new tools to monitor tillage intensity and best management practices, and to identify where additional conservations practices may be required.
2. Burning of agricultural residues is a widespread practice globally, and there is concern that black carbon deposition from agricultural burning resulted in accelerated melting of Arctic ice. Monitoring the area burned using remote sensing is important for calculating emissions. ARS scientists in Beltsville, Maryland and collaborators from the University of Maryland compared different burned-area data products from National Aeronautics and Space Administration satellites for their ability to detect agricultural burning in Russia. Results showed that none of the available algorithms performed well, indicating much more uncertainty in black carbon emissions. Future satellite sensors need additional bands and higher resolution to detect total areas burned more accurately.
3. Multispectral remote sensing and aerial photography from small unmanned aircraft are expected to facilitate crop nutrient management by predicting optimum nitrogen application rates earlier in the growing season. Nitrogen status is detectable later in the growing season by remote sensing the foliar chlorophyll content with spectral indices such as the normalized difference vegetation index. ARS scientists from Beltsville, Maryland, Oregon State University, and collaborators from industry found no differences in timing for detecting nitrogen status with unmanned aircraft compared to other methods. Therefore, coupling remote sensing from unmanned aircraft with current methods of analysis will not save farmers money by reducing nitrogen application rates and will not protect the environment by reducing excess nitrogen leaching into streams and rivers. Remote sensing crop nitrogen status using unmanned aircraft may be possible with new methods of image analysis.
Accomplishments
1. Early crop damage by insects detected by remote sensing with unmanned aircraft. Crop damage by insects is a major threat to food security and application of pesticides is a significant cost for many crops, such as potatoes. However, satellite remote sensing cannot detect insect damage until infestations are well-established. ARS scientists from Beltsville, Maryland, and Oregon State University found that imagery from small unmanned aircraft were able to identify insect damage to potatoes on the day damage was first visible to ground observers. Knowing when and where insects first appear in the field allow a greater range of treatments potentially saving farmers money and reducing the amount of crop damage.
2. Corrections to agrochemical volatilization measurements increase accuracy. Agrochemicals such as herbicides can have negative effects on human and environmental health when transported out of an agricultural field. The most common method for measuring agrochemical volatilization rates is the flux-gradient approach, which requires a number of assumptions regarding environmental conditions. ARS scientists from Beltsville, Maryland and Ames, Iowa evaluated the assumptions and identified a key source of measurement uncertainty; specifically, the air temperature at the ground surface compared to the air temperature directly above it. Correcting volatilization rate measurements will provide more accurate information on the agrochemical losses after application to crops for producers and policymakers.
3. Vegetative environmental buffers are effective in capturing particulates from animal production facilities. Particulates from animal feeding operations are readily transported by the wind to areas surrounding the production facility. Vegetative environmental buffers are often installed around the facilities to capture these particulates, but current point sampling methods used to measure their effectiveness are inadequate. As part of a Conservation Innovation Grant (8042-66000-001-03N) from the USDA Natural Resources Conservation Service, ARS scientists from Beltsville, Maryland, Lubbock, Texas and Ames, Iowa, Oklahoma State University, and the Universities of Iowa, Delaware, and Maryland used light detection and ranging (LiDAR) to examine particle plumes emanating in real-time over three to six hours. Results revealed that the capture efficiency of the vegetative buffers ranged from 20 to 70 percent and depended on meteorological conditions. This information has been incorporated into best management practices and presented at extension and producer meetings.
4. Effects of urban heat island on plant phenology detected using multi-sensor data fusion. Urban areas are significantly warmer than their surrounding rural areas and cause significant differences in the start and length of the growing season. The relationship between urban intensity and plant phenology has been observed, but is not well-quantified due to the high heterogeneity of urban landscapes. Using the high temporal and spatial resolution remote sensing data generated from the data fusion approach, the differences of plant phenology for urban and rural areas were observed and analyzed in the vicinity of Ogden, Utah. The results show that for urban areas, the start of the growing season is earlier and is of longer duration. Understanding the effects that the urban heat island has on plant phenology is important in modeling ecological impacts of expanding cities and land cover changes.
Review Publications
Beeson, P.C., Daughtry, C.S., Hunt Jr, E.R., Sadeghi, A.M., Karlen, D.L., Tomer, M.D. 2016. Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa. Journal of Soil and Water Conservation. 71:385-395. doi:10.2489/jswc.71.5.385.
Hunt Jr, E.R., Horneck, D., Spinelli, C., Turner, R., Bruce, A., Gadler, D., Brungardt, J., Hamm, P. 2017. Monitoring nitrogen status of potatoes using small unmanned aircraft system. Remote Sensing. doi:10.1007/s11119-017-9518-5.
Willis, W., Elchinger, W., Prueger, J.H., Hapeman, C.J., Li, H., Buser, M., Hatfield, J.L., Wanjura, J.D., Holt, G.A., Torrents, A., Plenner, S., Clarida, W., Brown, S.D. 2017. Particulate capture efficiency of a vegetative environmental buffer surrounding an animal feeding operation. Agriculture, Ecosystems and Environment. 240:101-108.
Sheng, Y., Song, C., Wang, J., Lyons, E., Knox, B., Cox, J., Gao, F.N. 2016. Comprehensive lake dynamics mapping at continental scales using Landsat 8. Remote Sensing of Environment. doi:10.1016/j.rse.2015.12.041.
Renkenberger, J., Montas, H., Leisnham, P., Chanse, V., Shirmohammadi, A., Sadeghi, A.M., Brubaker, K., Rockler, A., Hutson, T., Lansing, D. 2017. Climate change impact on critical source area (CSAs) identification in a Maryland watershed. Transactions of the ASABE. doi:10.13031/trans.59.11677.
Bandaru, V., Daughtry, C.S., Codling, E.E., Hansen, D., White-Hansen, S., Green, C.E. 2016. Evaluating leaf and canopy reflectance of stressed rice plants to monitor arsenic contamination. International Journal of Environmental Research and Public Health. 13:606. doi:10.3390/ijerph13060606.
Preece, J.E. 2016. Pomegranate production and marketing. Journal of American Pomological Society. 70(4):214-215.
Jiao, T., Williams, C., Ghimire, B., Masek, J., Gao, F.N., Schaaf, C. 2017. Global climate forcing from albedo change caused by large-scale deforestation and reforestation: quantification and attribution of geographic variation. Journal of Climate Change. doi:10.1007/s10584-017-1962-8.
Mollor, M., Gerstmann, H., Forster, M., Dahms, T., Gao, F.N. 2016. Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk. Catena. 150:192-205.
Willis, W., Eichinger, W., Prueger, J., Hapeman, C.J., Li, H., Buser, M., Hatfield, J., Wanjura, J., Holt, G., Torrents, A., Plenner, S., Clarida, W., Browne, S. 2017. Lidar method to estimate emission rates from extended sources. Journal of Atmospheric and Ocean Technology. 34:335-345.
Hall, J., Loboda, T., McCarty, G.W. 2016. A MODIS-based burned area assessment for Russian croplands: mapping requirements and challenges. Remote Sensing of Environment. 184:506-521.
Gervais, N., Buyantuev, A., Gao, F.N. 2017. Effects of the urban built-up environment on plant phenology: high-resolution analysis with fused Landsat and MODIS data. Remote Sensing of Environment. doi:10.3390/rs9010099.
Lewers, K.S., Fleisher, D.H., Daughtry, C.S. 2017. Low tunnels as a strawberry breeding tool and season-extending production system. International Journal of Fruit Science. https://doi.org/10.1080/15538362.2017.1305941.
Schmidt, W.F., Hapeman, C.J., McConnell, L., Mookherji, S., Rice, C., Nguyen, J.K., Qin, J., Chao, K., Kim, M.S., Broadhurst, C.L., Shelton, D.R. 2017. Using torsional forces to explain the gradient temperature Raman spectra of endosulfan isomers and its irreversible isomerization. Journal of Molecular Spectroscopy. 1139:43-51.
Hunt Jr, E.R., Rondon, S. 2017. Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. Journal of Applied Remote Sensing. 11:16729.
Zhang, Q., Middleton, E., Cheng, Y., Huemmrich, K., Cook, B., Corp, L., Kustas, W.P., Russ, A.L., Prueger, J.H., Yao, T. 2016. Integrating fAPARchl and PRInadir from EO-1/Hyperion to predict cornfield daily gross primary production (GPP). Remote Sensing of Environment. 186:311-321.
Alfieri, J.G., Prueger, J.H., Gish, T.J., Kustas, W.P., McKee, L.G., Russ, A.L. 2016. A note on the effective evaluation height for flux-gradient relationships and its application to herbicide fluxes. Agricultural and Forest Meteorology. 232:682-688.
