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 Hydrology and Remote Sensing Laboratory: Beltsville, MD
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ARS Home Northeast Area Beltsville, Maryland (BARC) Beltsville Agricultural Research Center Hydrology and Remote Sensing Laboratory Research





- Alfieri, Joseph
- Kustas, William - Bill
- McCarty, Gregory
- Gao, Feng
- Hapeman, Cathleen
- Cosh, Michael
- Kraatz, Simon
- Zhang, Xuesong
- Crompton, Octavia
- 2023
- 2022
- Publications
- Soil and Air (212)
- Sustainable Agricultural Systems Research (216)

Research Project: Enhancing Agricultural Management and Conservation Practices by Advancing Measurement Techniques and Improving Modeling Across Scales **Location:
                            Hydrology and Remote Sensing Laboratory** 2022 Annual Report **Objectives**
                                

Objective 1: Quantify agricultural and environmental processes in the Lower Chesapeake Bay (LCB) along with other LTAR and USDA network locations to facilitate the development and assessment of agricultural management and conservation practices leading to the sustainable intensification of agricultural production.
Subobjective 1.1: Maintain existing and establish new long-term data streams for the LCB-LTAR watershed site to assess agroecosystem status and trends and for use in modeling efforts. 
Subobjective 1.2: Quantify the spatial and temporal variability and assess atmospheric ammonia fate on the Delmarva Peninsula.
Subobjective 1.3: Use LCB-LTAR data streams collected to assess pollutant fate as a function of spatial differences in land use and temporal changes.
Subobjective 1.4: Characterize groundwater lag time for agricultural watersheds across climatic regions and different drainage conditions (e.g., well drained, karst hydrology, ditch drained, and tile drained).
Objective 2: Advance, develop, and validate remote sensing methods to assess crop condition and conservation practices.
Subobjective 2.1: Develop and validate remote sensing methods for assessing winter cover crop operations.
Subobjective 2.2: Improve remote sensing methods for assessing summer crop conditions.
Subobjective 2.3: Develop remote sensing methods to assess crop residue cover and soil tillage intensity at field to watershed scales.
Subobjective 2.4: Develop new methods to assess crop growth and N status using remote sensing for precision agriculture.
Objective 3: Quantify the environmental factors regulating interconnected atmosphere, soil, and water processes within agricultural landscapes to identify the potential risks associated with pollutants, assess conservation and management practices, and develop remediation strategies. 
Subobjective 3.1: Develop enhanced measurement and modeling techniques for accurately quantifying the emission and atmospheric transport of agrochemicals that are required to design and evaluate both management and remediation strategies.
Subobjective 3.2: Evaluate the use of compost and grass buffers to remediate pollutants in soils.
Subobjective 3.3: Evaluating conservation practice performance in agricultural landscapes.
Subobjective 3.4: Improve representation of wetland location and biogeochemistry within process-based models to support the assessment of wetland functions within the LCB-LTAR region. **Approach**
                                

Increase in agricultural production while maintaining natural resources and environmental quality requires a deeper understanding of natural processes in agricultural systems, new and better measurement techniques, robust decision support tools, and improved management practices. To address these needs, this project by focuses on improving techniques to assess agricultural practices, developing novel in-situ and remote sensing methods for measuring natural and agricultural processes, and both creating and maintaining long-term datasets through the Long-Term Agroecosystem Research (LTAR) and other USDA networks. 

Specifically, this project will continue the current data collection for the LTAR network as the Lower Chesapeake Bay (LCB) watershed site while creating new data streams focused on nutrient loading in Chesapeake Bay waterways for research efforts and to meet network goals (Objective 1). It will also develop and ascertain the utility of remote sensing to monitor crop conditions and tillage practices, assess the impacts of cover crops, and measure pesticide volatilization (Objective 2 and 3). The project will also explore new insights into optimizing agricultural management practices at landscape and regional scales which will improve rural prosperity (Objective 3). The results will lead to improved techniques for measuring ground water lag time within watersheds for modeling efforts and a deeper understanding the fate of agricultural and agroecosystem emissions, including ammonia, methane, agrochemicals, and particulate matter. The new measurement and modeling techniques, along with the other products of this research will benefit diverse customers including agricultural producers, policymakers, and non-governmental organizations. **Progress Report**
                                

ARS scientists from Beltsville, Maryland, made progress conducting interdisciplinary research to address all Objectives of this project plan in support of National Program 212, Soil and Air.
The first Objective of this project is to assess the Lower Chesapeake Bay agroecosystem via measurements and modeling and the establishment of the Lower Chesapeake Bay Long-Term Agroecosystem Research (LCB-LTAR) sites. To achieve this Objective, measurements of meteorological conditions, surface fluxes, crop phenology, and other environmental conditions were collected at the LCB-LTAR locations at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental watershed located near the Beltsville Agricultural Research Center (BARC) in Beltsville, Maryland, and the Choptank River watershed (CRW) located on Maryland’s Delmarva Peninsula. Additionally, real time water quality data were collected at two USGS gage stations in the CRW and a new in-situ sensor for dissolved nitrogen and phosphorus was evaluated. Similarly, the metolachlor degradation product and nitrate data were acquired for the Choptank and Monocacy watersheds. 
Focusing on the counties within the Chesapeake Bay watershed, preliminary data sets were assembled to investigate the relationship between manure redistribution and water quality. Tools built on the earlier work of Spiegal et al. (2020) were developed to optimize the tradeoff between manure transportation and water quality costs, which are defined in terms of the waste treatment processes required to remove nutrients before they enter the Chesapeake Bay. Preliminary results suggest that cost disparities between the use of manure rather than chemical fertilizers for crop production are not offset by water treatment cost alone and a broader range of factors, such as ecosystem health and services, must be considered to justify the transport of manure as a resource.
Improvements were made in the Soil and Water Assessment Tool (SWAT) model to represent energy hydrologic processes more accurately. Specifically, the river routing scheme was modified to examine the effects of varying the river routing time step (from 1 minute to 1 day) on model simulations of streamflow, stream water depth, and water storage in river networks. It was found that the time step must be less than 1 hour to reliably assess hydrologic connectivity and aquatic ecosystem health. These improvements to the SWAT model will enhance its utility for assessing ecosystem services within agroecosystems.
The second Objective is to enhance the utility of remote sensing using airborne and satellite platforms to measure biophysical variables related to agricultural production and environmental assessment. Specifically, these studies used data collected in the LCB-LTAR, along with sites distributed across the United States, to develop and test remote sensing methods for assessing crop conditions, conservation practices, and nutrient use efficiency. Using the within-season emergence and termination algorithms and imagery from the Landsat and Sentinel-2, emergence and termination dates for winter cover crops were generated bi-weekly for Maryland and Delaware. The results of this effort have been provided to the Maryland and Delaware Departments of Agriculture for their use to evaluate their operational winter cover crop incentive programs. In addition, the within-season emergence algorithm has been applied to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, Nebraska) from 2017-2020. The 30-m spatial resolution maps of crop emergence dates have been produced and compared to the NASS crop progress reports. Similarly, land surface phenology and conditions generated from remote sensing data were assessed over dryland and grassland. The results of studies have been published. Likewise, two new remote sensing-based phenology algorithms (the hybrid phenology model and spatiotemporal shape-matching model) have been developed and published. 
A new hybrid model that combines to different techniques (the phenometric extraction model and phenology matching model) to reduce the data requirements for monitoring crop growth was developed. This new hybrid model was assessed for its capacity to map crop growth stages at 500-m spatial resolution over corn and soybean in Illinois from 2002-2017 using different time-space scenarios.
A new remote sensing algorithm has been developed to reconstruct the daily two-band Enhanced Vegetation Index (EVI2) for accurate vegetation monitoring at 30-m spatial resolution.
A remote sensing approach was used to classify soil tillage intensity in Iowa based on multiple years of field observations. The study assessed the capability of various vegetation indices and different classification techniques for mapping soil tillage intensity. In an effort to map crop residue cover using the current Landsat-8, Landsat-9, and the future Landsat Next mission, multiple shortwave infrared-derived indices from different sensors were evaluated. Results and recommendations for the Landsat Next mission have been published. 
Additionally, Sentinel-1 radar data for the growing season (March to November for period from 2017 to 2021) was assembled for the CRW. The workflows for preprocessing radar data to generate analysis-ready data sets that coincide with the USDA-NASS Cropland data and can be used with the USDA supercomputing infrastructure were also developed. The cross-polarized data were further processed according to the cropland mapping approach to determine the temporal coefficient of variation (CV) for each of three sub-annual periods: March to June; June to August; and August to November. Conceptually, the CV can also be considered an index, much like the normalized difference vegetation index (NDVI) or leaf area index (LAI), that relates crop and soil conditions. Preliminary results indicate CV values are highly dynamic and can be associated with localized features in agricultural fields. Further analysis is ongoing for determining the relation of CV values to cover crops, crop residue, and tillage intensity.
The third Objective focuses on characterizing environmental processes within agricultural landscapes to evaluate ecosystem services and best management practices. Although field and laboratory activities were limited due to restrictions related to the COVID19 pandemic, important advancements were achieved for several of the research tasks associated with this objective. Research was conducted to examine chemical and environmental processes affecting dicamba drift and volatilization. Although dicamba is a commonly used herbicide, it can be transported downwind where it can deposit onto sensitive crops and adversely affect yield. ARS Scientists in Beltsville, Maryland, along with industry and university collaborators conducted a meta-analysis of numerous studies to characterize the effects of dicamba exposure as a function of chemical formulation, application method, and plant sensitivity. The results and recommendations of this study were published in a featured perspective article.
Using eight future climate scenarios and three representative concentration pathways (RCPs), the Soil and Water Assessment Tool (SWAT) model was applied to project future water storage in non-floodplain wetlands (NFW) and analyze the sources of uncertainty. The initial results of the Analysis of Variance (ANOVA) showed the variability among the projected climate scenarios is the most significant single source of model uncertainty and can explain nearly half of the uncertainty associated with NFW water storage estimates. The research suggests that the benefits of wetland management are highly dependent on future climate conditions. 
Multiple approaches for representing the effects of soil temperature (3 methods), soil water content (2 methods), and tillage practices (4 methods) on the decomposition of soil organic carbon (SOC) were evaluated both individually and in combination using the SWAT model. Once the optimal modeling approach was identified, further analyses were conducted to assess the ability of the model to describe SOC dynamics under different field conditions, soil depths, and tillage practices. These improvements to the SWAT model enhance its utility for describing carbon dynamics in agroecosystems.
Machine learning classification algorithms that use a combination of satellite imagery and topographic data to determine inundation and vegetation dynamics in Arctic wetlands was developed. This approach, which performed well with an overall classification accuracy of 87%, could aid in the creation and maintenance of wetland inventories, such as the National Wetlands Inventory (NWI), thereby facilitating a deeper understanding of long-term wetland dynamics in Arctic regions. **Accomplishments**
                                

1. Topographic models for mapping soil organic carbon. Digital mapping of soil properties is an important emerging technology for improving the management of agricultural landscapes. ARS scientists in Beltsville, Maryland, along with university partners, investigated the potential for using a regional soil carbon prediction model that relies solely on readily obtained elevation data to generate localized maps of soil carbon at the sub-field scales. For this application, the regional model was calibrated using a limited number of field samples to generate local maps of soil carbon. Since only a small number of field samples are needed, accurate maps of soil carbon can be created without the need of intensive data collection which can be both costly and time consuming. This approach greatly improves the efficiency of soil carbon mapping in agricultural landscapes and will facilitate improved management of agricultural landscapes. 2. Analysis of dicamba drift and non-target crop harm. Dicamba is a common herbicide used in producing many crops, but it can move through the air, deposit on sensitive crops, and potentially decrease crop yields. ARS-Beltsville in collaboration with university and industry partners examined many studies designed to prevent harm to non-target organisms. Chemists have developed new commercial formulations utilizing the chemical properties of dicamba to keep it in its non-volatile form. Engineers have designed application equipment that can substantially reduce dicamba drift from the target fields. Plant scientists have discovered that some crops, such as soybeans, are very sensitive to extremely low levels of dicamba and will show some leaf deformities, but the yield is not affected until much higher levels are deposited on the plants. This information is essential for understanding and mitigating the scientific and engineering challenges linked to the application of dicamba. It is critical for farmers, producers, and applicators to ensure that dicamba is used properly and effectively. **Review Publications**
                            *Lee, S., Qi, J., McCarty, G.W., Yeo, I., Zhang, X., Moglen, G.E., Du, L. 2021. Uncertainty assessment of multi-parameter, multi-GCM, and multi-RCP simulations for streamflow and non-floodplain wetland (NFW) water storage. Journal of Hydrology. 600:126564. https://doi.org/10.1016/j.jhydrol.2021.126564.*
                                *Yang, Z., Evans, M.D., Buser, M.D., Hapeman, C.J., Torrents, A., Whitelock, D.P. 2022. Improving modeling of low-altitude particulate matter emission and dispersion: A cotton gin case study. Journal of Environmental Science. https://doi.org/10.1016/j.jes.2022.03.048.*
                                *Kearney, S.P., Porensky, L.M., Augustine, D.J., Derner, J.D., Gao, F.N. 2022. Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series. Ecological Applications. 32. Article e2503. https://doi.org/10.1002/eap.2503.*
                                *Zhang, X., Gao, F.N., Wang, J., Ye, Y. 2021. Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data. International Journal of Applied Earth Observation and Geoinformation. 104:102545. https://doi.org/10.1016/j.jag.2021.102545.*
                                *Diao, C., Yang, Z., Gao, F.N. 2021. Hybrid phenology matching model for robust crop phenological retrieval. Journal of Photogrammetry and Remote Sensing. 181:308-326. https://doi.org/10.1016/j.isprsjprs.2021.09.011.*
                                *Du, L., McCarty, G.W., Li, X., Rabenhorst, M., Wang, Q.L., Lee, S., Hinson, A., Zou, Z. 2021. Spatial extrapolation of topographic models for mapping soil organic carbon using local samples. Geoderma. 404:115290. https://doi.org/10.1016/j.geoderma.2021.115290.*
                                *Zou, Z., Devries, B., Huang, C., Lang, M.W., Mccarty, G.W., Thielke, S., Robertson, A., Knopt, J., Du, L. 2022. Characterizing wetland inundation and vegetation dynamics in the arctic coastal plain using recent satellite data and field  photos. Remote Sensing. 13(8):1492. https://doi.org/10.3390/rs13081492.*
                                *Qi, Y., Lee, S., Du, X., Ficklin, D., Wang, Q., Myers, D., Singh, D., Moglen, G.E., Mccarty, G.W., Zhou, Y., Zhang, X. 2021. Coupling terrestrial and aquatic thermal processes for improving stream temperature modeling at watershed scale. Journal of Hydrology. 603. Article 126983. https://doi.org/10.1016/j.jhydrol.2021.126983.*
                                *Qui, H., Qi, J., Lee, S., Moglen, G.E., Mccarty, G.W., Chen, M., Zhang, X. 2022. Effects of temporal resolution of river routing on hydrologic modeling and aquatic ecosystem health assessment with the SWAT model. Environmental Modelling & Software. 145:105232. https://doi.org/10.1016/j.envsoft.2021.105232.*
                                *Taylor, S.D., Browning, D.M., Baca, R.A., Gao, F.N. 2021. Constraints and opportunities for detecting land surface phenology in drylands. Journal of Remote Sensing. 2:1-15. https://doi.org/10.1101/2021.05.21.445173.*
                                *Anderson, M., Yang, Z., Hapeman, C.J., Mcconnell, L., Green, C.E., Jackson, D., Evans, M., Torrents, A. 2021. On-site evaluation of the effects of carbonaceous amendments on the bioavailability of aged organochlorine pesticide residues in soil. Environmental Pollution. 6:100126. https://doi.org/10.1016/j.envadv.2021.100126.*
                                *Riter, L., Pai, N., Vieria, B., Macinnes, A., Reiss, R., Hapeman, C.J., Kruger, G. 2021. Conversations about the future of dicamba: the science behind off target movement. Journal of Agricultural and Food Chemistry. 69:14435-14444. https://doi.org/10.1021/acs.jafc.1c05589.*
                                *Hively, W., Lamb, B., Daughtry, C.S., Serbin, G., Dennison, P., Kokaly, R., Wu, Z., Masek, J. 2021. Evaluation of SWIR crop residue bands for the landsat next mission. Remote Sensing. 13(18). https://doi.org/10.3390/rs13183718.*
                                *Campbell, P., Middleton, E., Hummerich, K., Ward, L., Julitta, T., Yang, P., Van Der Tol, C., Daughtry, C.S., Russ, A.L., Alfieri, J.G., Kustas, W.P. 2021. Dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with photosynthetic function and CO2 dynamics for maize. Data in Brief. 39:107600. https://doi.org/10.17632/b84jk376c3.1.*


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