Location: Hydrology and Remote Sensing Laboratory
2023 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
During Fiscal Year 2023, ARS scientists from Beltsville, Maryland, made progress conducting interdisciplinary research to address the three overarching objectives of this project supporting National Program 212, Soil and Air.
The first objective of this project is to 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. To achieve this objective, meteorological, surface fluxes, crop phenology, and other environmental measurements 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. Real time water quality data were collected, and the evaluation of a prototype phosphorus probe continued at several USGS gage stations in the CRW and elsewhere in the LTAR Network. Additional watersheds have been added as study areas for the LCB site.
In support of the manureshed optimization efforts, a county-level dataset centered on the Chesapeake Bay watershed (CBW), which includes the derived the quantities needed to model manure-based nutrient transport and redistribution within the watershed and conduct a statistical analysis of the influence of manuresheds management on nutrient fate, was developed. For example, the representative centroid coordinate of each county, the distances along the road network for each possible county-pair in the CBW, and the distance along the drainage network from county centroids to tidewater of the CBW were determined. An optimization framework based on this dataset was all developed.
An analysis of water samples from the USDA Watershed Lag Time Project (WLTP) and the USGS National Water Quality Assessment (NAWQA) network revealed that smaller watersheds are more predictable, and that complexity increases with scale of the observations. Point-in-time sampling was initiated to determine the concentrations of nitrate-N and the metolachlor degradation product (MESA, metolachlor ethane sulfonic acid) in the Choptank and Monocacy River watersheds and at several LTAR Network and CEAP sites. Several manuscripts are in preparation. The USDA WLTP was used as the basis for the new national CEAP Legacy N project which is led by ARS-Beltsville, Maryland.
The second objective of this project is to advance, develop and validate remote sensing methods to assess crop condition, conservation practices, and nutrient use efficiency. An investigation into the use of Sentinel-1 SAR data for detecting the termination of winter cover crops (WCC) was conducted. However, the results obtained from the analysis were mixed and inconclusive. Consequently, the research activities focusing on determining the termination of WCC will focus on utilizing Landsat and Sentinel-2 data. Also, the within-season termination (WIST) algorithm was refined for use with the Harmonized Landsat and Sentinel-2 (HLS) data and the results of that work have been published. As a part of an ongoing collaboration, WCC termination dates determined via this approached were produced on a bi-weekly basis and delivered to the Maryland Department of Agriculture (MDA) WCC incentive program.
Historical satellite, weather, and crop type and crop condition data for the corn belt states were collected and processed. Together with the crop emergence dates determined using HLS time series, these data provide the foundation for developing enhanced remote sensing-based approaches to monitor and assess summer crop conditions at field scales. Focusing specifically on central Iowa, the utility of several different spectral indices and classification methods for mapping crop residue was investigated using Landsat imagery. HLS data was also collected and will be used to assess the potential benefits of considering Sentinel-2 data when identifying crop residue via remote sensing.
The final objective of this project is to 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. Post-application samples of metolachlor and atrazine emissions (fluxes) were collected over a five-day period at the OPE3 study site near Beltsville, Maryland. These data will be used for both evaluating the utility of the relaxed eddy accumulation (REA) method when monitoring agrochemical fluxes and investigating the effects of atmospheric stability on the magnitude of the fluxes. Concomitant field samples of the throughfall and stem flow rinsate were collected at multiple locations within the riparian buffer downwind of the study site. Because prior analyses with samples from the site showed that an unidentified compound masked the chemical signature of metolachlor and atrazine, the rinsate samples will be used to develop more robust extraction and chemical analysis methods; this is in accord to the contingencies described in the Project Plan. Additionally, a database of metolachlor and atrazine fluxes, along with contemporaneous measurements of the surface energy fluxes, meteorological conditions, and soil properties, was developed using the data collected since 2004 as a part of the ongoing field studies at the OPE3 site. This dataset will be used to develop and evaluate a novel remote sensing-based approach for monitoring agrochemical fluxes at larger spatial scales. Finally, research was conducted to develop and refine models to describe the relationship between both ammonia and particulate matter from poultry houses and vegetative buffers. Several manuscripts concerning the model modifications and application are in preparation or have been submitted for publication.
A preliminary analysis of the effectiveness of using sterile Miscanthus to mitigate legacy phosphorous suggests it may sequester some phosphorous from the soil, thereby preventing it from entering nearby waterways. An analysis of the long-term studies into the effectiveness of using compost or biowalls to remediate legacy organochlorine pesticides and similar pollutants revealed that biowalls require continual maintenance and that new modeling techniques are needed to conduct more accurate ecosystem risk assessments. The results of this work have been published.
Studies were conducted in collaboration with U.S. Geological Survey and MDA to estimate the cover crop biomass, nitrogen, and carbon accumulation within regular- and delayed-terminated fields using satellite remote sensing and in situ measurements. The results of the studies, which have been published, show that the delayed-termination fields accumulated additional biomass, nitrogen. Measurements of the methane and carbon dioxide fluxes from natural, restored, and drained wetlands were collected as a part of an ongoing study; these data will be used to analyze differences in soil organic carbon stocks in wetlands versus agricultural systems on the Delmarva Peninsula.
Deep convolutional neural network models were used to map and characterize wetland connectivity and ditch networks in low relief landscapes of the Delmarva Peninsula. The physical processes in terrestrial and aquatic ecosystems that regulate stream water temperature were integrated into the Soil and Water Assessment Tool (SWAT) model, enabling the model to assess the impact of agricultural activities, e.g., tillage, residue management, and irrigation, on stream temperature and other related water quality indicators. The model has been applied to the Choptank River watershed along with several watersheds in the central United States. Modifications were also made to the algorithms used to calculated soil organic carbon in the Soil and Water Assessment Tool – Carbon (SWAT-Carbon) model. The capability of the modified model to simulate soil carbon dynamics was evaluated at seven locations distributed across the corn belt states. As an open-source model, SWAT-C is freely available so that it can be used in carbon assessments and as a robust decision support tool for managing agroecosystems.
Accomplishments
1. Near real-time detection of winter cover crop termination to support operational ecosystem assessment. Cover crops are planted to reduce soil erosion, enhance soil fertility, and improve watershed management. A cost-share program has been created to encourage their use with specific planting and termination requirements. Typically, participants report cover crop termination dates, which are verified through labor-intensive field visits. Remote sensing imagery provides time-series information for detecting cover crop terminations timely. ARS scientists in Beltsville, Maryland, have developed the within-season termination (WIST) algorithm and refined it for the freely available Harmonized Landsat and Sentinel-2 (HLS) data to detect cover crop termination dates. The approach offers quick and valuable support for the cover crop program and saves half of the staff field visit time.
2. Winter cover crops reduce soil erosion and take up excess nitrogen. Winter cover crops serve as conservation tools to reduce erosion and take up excess N, and can improve soil health, increase infiltration, and be used as a form of C sequestration. The Maryland Department of Agriculture (MDA) Winter Cover Crop Program introduced a delayed termination (after May 1) incentive program to promote springtime biomass accumulation. However, the impact of the delayed termination has not been evaluated. ARS scientists in Beltsville, Maryland, in collaboration with U.S. Geological Survey and MDA conducted studies to estimate cover crop biomass, nitrogen, and carbon accumulation associated with regular- and delayed-terminated fields using satellite remote sensing and in situ measurements. Results showed that the delayed-termination fields accumulated additional biomass, nitrogen, and carbon and that additional biomass, carbon, and nitrogen sequestration achieved through the delayed termination incentive were more cost-effective than early-termination base payments. The findings support the MDA Winter Cover Crop Program for improving agroecosystem services which should decrease the amount of nutrients lost to waterways.
3. SWAT-Carbon Model released. Agricultural practices, such as conservation tillage, nutrient management, and cover crops, hold great potential to sequester and store carbon in agricultural soils to mitigate greenhouse gas emissions and improve soil health. ARS researchers in Beltsville, Maryland, noted these agricultural practices also have significant implications for water quality and quantity. Modifications were made to the soil organic carbon (SOC) algorithms within the Soil and Water Assessment Tool - Carbon (SWAT-Carbon) model. The modified model was then applied to simulate SOC dynamics across diverse cropping systems in the U.S. Corn Belt. These sites include locations supported by USDA GRACEnet (Greenhouse gas Reduction through Agricultural Carbon Enhancement network) and REAP (Renewable Energy Assessment Project). The results demonstrated that the modified SWAT-Carbon model effectively captured SOC dynamics at various sites, soil depths, and under different tillage intensities. Such capabilities allow SWAT-Carbon to be a first-of-its-kind watershed model that can simultaneously assess multidimensional indicators of agroecosystem sustainability, such as soil carbon sequestration, agricultural water use, and water quality. As an open-source model, SWAT-Carbon is freely shared to contribute to future carbon assessment and management in climate-smart agroecosystems.
Review Publications
Yang, Z., Diao, C., Gao, F.N. 2023. Towards scalable within-season crop mapping with phenology normalization and deep learning. Geoscience and Remote Sensing Letters. 16:1390-1402. https://doi.org/10.1109/JSTARS.2023.3237500.
Menefee, D.S., Scott, R.L., Abraha, M., Alfieri, J.G., Baker, J.M., Browning, D.M., Chen, J., Gonet, J.M., Johnson, J.M., Miller, G.R., Nifong, R.L., Robertson, P., Russel, E.R., Saliendra, N.Z., Schreiner-Mcgraw, A.P., Suyker, A., Wagle, P., Wente, C.D., White Jr, P.M., Smith, D.R. 2022. Unraveling the effects of management and climate on carbon fluxes of U.S. croplands using the USDA Long-Term Agroecosystem (LTAR) network. Agricultural and Forest Meteorology. 326. Article 109154. https://doi.org/10.1016/j.agrformet.2022.109154.
Liang, K., Qi, J., Zhang, X. 2022. Replicating measured site-scale soil organic carbon dynamics in the U.S. corn belt using the SWAT-C model. Environmental Modelling & Software. 158. Article 105553. https://doi.org/10.1016/j.envsoft.2022.105553.
Liang, K., Zhang, X., Liang, X., Jin, V.L., Birru, G.A., Schmer, M.R., Robertson, P.G., McCarty, G.W., Moglen, G.E. 2023. Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model. Science of the Total Environment. 879. Article 162906. https://doi.org/10.1016/j.scitotenv.2023.162906.
Gao, F.N., Jennewein, J.S., Hively, W.D., Soroka, A., Thieme, A., Bradley, D., Keppler, J., Mirsky, S.B., Akumaga, U. 2022. Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment. Science of Remote Sensing. 7. Article 100073. https://doi.org/10.1016/j.srs.2022.100073.
Thieme, A., Hively, W.D., Gao, F.N., Jennewein, J.S., Mirsky, S.B., Soroka, A., Keppler, J., Bradley, D., Skakun, S., McCarty, G.W. 2023. Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination. Agronomy Journal. 15:442–458. https://doi.org/10.1002/agj2.21207.
Bianca, M., Owen, D.C., Plummer, R.E., Rice, C., McCarty, G.W., Hapeman, C.J. 2023. Chiral separation of metolachlor metabolites in a single, large volume injection to facilitate watershed tracer studies. ACS Agricultural Science and Technology. 3(3):270–277. https://doi.org/10.1021/acsagscitech.2c00265.
Bianca, M., Rice, C., Lupitskyy, R., Plummer, R.E., McCarty, G.W., Hapeman, C.J. 2022. Trans enantiomeric separation of MESA and MOXA, two environmentally important metabolites of the herbicide, metolachlor. MethodsX. 9. Article 10188. https://doi.org/10.1016/j.mex.2022.101884.
Saffari Ghandehari, S., Boyer, J., Ronin, D., White, J., Hapeman, C.J., Jackson, D., Kaya, D., Torrents, A., Kjellerup, B.V. 2023. Use of organic amendments derived from biosolids for groundwater remediation of TCE. Chemosphere. 323. Article 138059. https://doi.org/10.1016/j.chemosphere.2023.138059.
