Location: Hydrology and Remote Sensing Laboratory
2024 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 2024, ARS scientists from Beltsville, Maryland, made substantial progress toward addressing the three overarching Objectives of this project supporting National Program 212, Soil and Air.
The first Objective of the project is to quantify agricultural and environmental processes in the Lower Chesapeake Bay (LCB) and other LTAR and USDA locations to develop and assess agricultural management and conservation practices. To achieve this, meteorological, crop phenology, soil moisture, and other environmental data were collected at the LCB-LTAR sites at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental watershed located near Beltsville, Maryland, and the Choptank River watershed (CRW) located on Maryland’s Delmarva Peninsula.
Measurements of precipitation, solar radiation, soil moisture and soil temperature were collected at sites distributed across the LCB-LTAR as a part of the CRW soil moisture network. These data, which are available through Ag Commons, are being used to understand the relationships between climate trends, soil processes, and crop status on the Delmarva Peninsula and surrounding Chesapeake Bay region.
The OPE3 site was also the focus area for evaluating the utility of synthetic aperture radar (SAR) data to map land cover. Scientists at the Beltsville Agricultural Research Center (BARC) found that mapping land cover type using radar data from Sentinel-1 yielded similar results as the USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) for urban and forested regions but produced significantly better results when mapping croplands. Over the study period (2017 to 2021), the accuracy of the CDL classification of croplands was 77% while the accuracy of method using radar data approached 96%. Efforts are also underway to further improve this approach by using a combination of SAR and crop phenology data.
Real time water quality data was collected at several USGS gage stations in the CRW and other LTAR locations to evaluate a prototype phosphorus probe. Additionally, analyses of water samples from the USDA Watershed Lag Time Project (WLTP), which is now part of the Legacy N project, and the USGS National Water Quality Assessment (NAWQA) network revealed that the complexity of watershed behave increases with the scale of the observation. Point-in-time samples were also collected to determine the concentrations of nitrate-N and the metolachlor degradation product (MESA, metolachlor ethane sulfonic acid) in the CRW and Monocacy River watersheds along with several other LTAR and CEAP sites.
The second Objective of the project is to advance remote sensing-based methods to assess crop condition, conservation practices, and nutrient use efficiency. A remote sensing data processing system capable of both generating operational field-level reports and mapping cover crop emergence and termination dates was built and tested on a local Linux system. An equivalent system that takes advantage of SCINet high performance computing (HPC) is also being evaluated over multiple states. The termination dates of winter cover crops were determined bi-weekly using the within-season termination (WIST) algorithm with the Harmonized Landsat and Sentinel-2 (HLS) datasets and delivered to Maryland Department of Agriculture's (MDA) Winter Cover Crop Incentive program as a part of an ongoing collaboration. The MDA compares the derived termination dates to those reported by growers.
A new algorithm for monitoring summer crop conditions was also developed. Unlike calendar-based approaches, the new crop emergence-adjusted vegetation index (CEAVI) accounts for interannual variations in crop phenology by incorporating crop emergence date derived from HLS datasets. Using a database of CEAVI over central Iowa generated with historical (2018-2022) HLS data, the new algorithm was evaluated against both traditional calendar-based approaches and NASS crop condition maps.
Daily time series of the Normalized Difference Vegetation Index (NDVI) and the 2-band Enhanced Vegetation Index (EVI2) were derived for the conterminous US (CONUS) for the period from 2018 to 2023 from HLS using the SCINet HPC. These datasets provide key information for improving the estimation of crop residue cover.
The final Objective of this project is to quantify the linkages between atmospheric, soil, and water processes within agricultural landscapes to identify the potential risks associated with pollutants, assess conservation and management practices, and develop remediation strategies. Research activities linked to atrazine and metolachlor emissions and deposition were hampered by the vacant the Support Chemist position. Instead, the research linked to measuring agrochemical fluxes in the field focused on nitrous oxide (N2O) and methane (CH4) emissions. In collaboration with scientists at the National Laboratory for Agriculture and the Environment (NLAE) and Los Gatos Research (LGR), this research evaluated the utility of the LGR gas analyzer for monitoring N2O and CH4 fluxes via eddy covariance (EC). This research quantified the measurement error and uncertainty due to the asynchronous measurements of wind speed and the gas concentration using multiple techniques such as temporal interpolation and oversampling the gas concentration. It investigated multiple modifications to both the sensor system hardware and software to improve the synchronicity of the measurements. This work increased the sampling rate of the gas measurements to 10 Hz which is sufficient for true EC flux measurements. Based on initial testing in the laboratory and a field site near Ames, Iowa, the synchronization issues appear to be resolved. Further studies are planned during the 2024 growing season to confirm this. At the same time, the accuracy of the N2O and CH4 flux measurement from the LGR system will be assessed by comparison with chamber measurements. Progress was also made investigating the feasibility of monitoring metolachlor flues via remote sensing using a modified version of the Two-Source Energy Balance (TSEB) model. A composite transfer function relating the metolachlor and moisture flux was developed. However, initial evaluations of the composite relationship showed there were substantial errors in the predicted fluxes due to the interannual and spatial variability in the relationship between the metolachlor and moisture fluxes. This suggests there are other factors that impact the metolachlor emissions that must be incorporated into the model.
Runoff and leaching can transport excess phosphorous that accumulates in agricultural fields into adjacent waterways where it can negatively impact water quality and environmental health. Building on previous work suggesting Miscanthus x giganteus (Miscanthus) may be useful for managing legacy phosphorous, data was also collected to quantify the capacity of Miscanthus to sequester phosphorous and its subsequent utility as poultry litter and fertilizer.
Two enhanced versions of the SWAT model were developed that better describe grass growth. The first of these, SWAT-GRASSD, integrates the DAYCENT grass growth algorithm into the SWAT model while the second version, SWAT-GRASSM, integrates a modified form of the same algorithm to explicitly consider the impact of leaf area on potential biomass production. Based on comparisons of the two models at eight sites in the Midwest US, the SWAT–GRASSM generally outperformed both SWAT and SWAT-GRASSD when simulating switchgrass biomass. Also, SWAT–GRASSM more realistically represented root development, which is key for allocating accumulated biomass and nutrients between above and below ground pools. These improvements are critical for assessing the agronomic and environmental impacts of growing perennial grasses for biomass production.
To evaluate the performance of winter cover crops (WCC) in agricultural landscapes, a study using the SWAT-C model was developed that considers six cover crop scenarios to understand the potential impacts of WCC in the Tuckahoe Creek Watershed (TCW) on Maryland’s Eastern Shore. In addition to corroborating the nitrate reduction benefits of WCC that have been reported previously, the results of this study also demonstrated a comparable reduction in sediment. The study also found that WCC can sequester 0.3 to 0.61 Mg C ha-1 yr-1 with the early planting of WCC resulting in up to 70% more carbon sequestration than late plantings. Extrapolating this to all Maryland croplands suggests that WCC have the potential to account for 1.4 to 2.9% of the state’s 2030 goal for reducing greenhouse gas emissions. However, it was also found that WCC can substantially increase evapotranspiration, thereby decreasing water yield and streamflow; this could adversely affect the health of aquatic ecosystems and the water supply.
Carbon accounting, which provides critical information for managing agroecosystems to mitigate greenhouse gas emissions, typically considers soil organic carbon and net ecosystem exchange but not lateral fluxes that transfer carbon from agricultural to aquatic systems. Therefore, a similar modeling study focusing on TCW were conducted with SWAT-C to assess the effect of lateral carbon fluxes on the carbon balance of agroecosystems. This study found lateral carbon fluxes account for 11% of net ecosystem exchange (NEE) and nearly 95% of net biome production. Overall, the results of this study illustrate the importance of accounting for both vertical and lateral carbon fluxes when developing effective agricultural practices to reduce greenhouse gas emissions.
Efforts continued to refine deep convolutional neural network models for mapping wetland connectivity within ditch networks in low relief landscapes. The models are being evaluated in the CRW along with several other watersheds in the central United States.
Accomplishments
1. Comparing spectral indices and classification techniques for improving crop residue cover mapping. Crop residue cover helps reduce erosion, increase soil organic carbon, and improve water quality. Estimating residue cover using remote sensing has been studied, yet it is still challenging due to the similarity of soil and crop residue signals at visible and near-infrared wavelengths. ARS scientists in Beltsville, Maryland, compared the accuracy of crop residue mapping using five spectral indices and six imagery classifications over four years in central Iowa. Results show that crop residue cover and soil tillage intensity can be mapped using the selected indices and classification methods. However, the accuracies obtained from spectral indices and classification methods vary across different years. The finding demonstrates that remote sensing provides a viable means to map crop residue cover on a large scale, thereby supporting agroecosystem monitoring.
2. Evaluation of cropland mapping with Sentinel data using the Agricultural Statistics Service (NASS) Cropland Data Layer (CDL). Crop monitoring data provides critical information to growers, policymakers, and scientists addressing a broad range of issues from understanding and ameliorating the effects of climate change to ensuring food security. However, the current methods for mapping crop production have drawbacks; for example, maps generated with optically based methods can be adversely by cloud cover and transient surface conditions. To overcome these limitations, ARS scientists in Beltsville, Maryland, focused on sites in the Lower Chesapeake Bay (LCB) Long Term Agricultural Research (LTAR) Network location to evaluate land cover maps derived from Sentinel-1 radar data against Agricultural Statistics Service (NASS) Cropland Data Layer (CDL). The radar-based approach substantially outperformed NASS CDL, particularly when classifying agricultural fields. Over the five-year (2017 to 2021) study period, the success rate for the CDL was 77% while radar-based approach correctly identified croplands 96% of the time. As demonstrated in this study, due to its effectiveness, relative ease of use and low computational cost, radar data is an invaluable tool for cropland mapping that can be used to provide high-quality data to growers and other stakeholders.
3. Mapping wetland hydrological connectivity. Depressional wetlands are often considered to be isolated from the surrounding landscape and remain vulnerable in the United States because their regulatory status is dependent on the Clean Water Act (CWA). Legal protection under the CWA is currently restricted to wetlands with demonstrated connection to downstream waters. ARS scientists in Beltsville, Maryland, used a new, nonlinear approach for establishing causal connections by detecting the flow of information from the wetland to downstream waters. The measurement of this information flow is difficult because of the nonlinear nature of hydrologic flows. This approach was successful in demonstrating a causal relationship between depressional wetlands under study and downstream waters, and provides an important new basis for identifying and protecting depressional wetlands.
Review Publications
Hively, D., Duiker, S., McCarty, G.W. 2015. Remote sensing to monitor cover crop adoption in southeastern Pennsylvania. Journal of Soil and Water Conservation. 70:340-352.
Starkenburg, D., Metzger, S., Fochesatto, G., Alfieri, J.G., Gens, R., Prakash, A., Cristobal, J. 2016. Assessment of de-spiking methods for turbulence data in micrometeorology. Journal of Geophysical Research Atmospheres. 33(9):2001-2013. https://doi.org/10.1175/JTECH-D-15-0154.1.
Prueger, J.H., Alfieri, J.G., Gish, T.J., Kustas, W.P., Daughtry, C.S., Hatfield, J.L., McKee, L.G. 2017. Multi-year measurements of field-scale metolachlor volatilization. Water, Air, and Soil Pollution. 228. Article 84. https://doi.org/10.1007/s11270-017-3258-z.
Lee, S., Lee, B., Lee, J., Song, J., McCarty, G.W. 2023. Detecting causal relationship of non-floodplain wetland hydrologic connectivity using convergent cross mapping. Scientific Reports. 13. Article e17220. https://doi.org/10.1038/s41598-023-44071-0.
Stern, A.J., Daughtry, C.S., Hunt Jr, E.R., Gao, F.N., Hively, W.D. 2023. Comparison of five spectral indices and six imagery classification techniques for assessment of crop residue cover using four years of Landsat imagery. Remote Sensing. Remote Sens. 2023, 15(18), 4596. https://doi.org/10.3390/rs15184596.
Luo, X., Risal, A., Qi, J., Lee, S., Zhang, X., Alfieri, J.G., McCarty, G.W. 2023. Modeling lateral carbon fluxes for agroecosystems in the Mid-Atlantic region: Control factors and importance for carbon budget. Science of the Total Environment. 912:169128. https://doi.org/10.1016/j.scitotenv.2023.169128.
Peng, Z., Zhao, T., Shi, J.C., Hu, L., Rodriguez-Fernandez, N., Wigneron, J., Jackson, T.J., Walker, J., Cosh, M.H., Yang, K., Lu, H., Bai, Y., Yao, P., Zheng, J., Wei, Z. 2023. First mapping of polarization-dependent vegetation optical depth and soil moisture from SMAP L-band radiometry. IEEE Transactions on Geoscience and Remote Sensing. 302. Article e113970. https://doi.org/10.1016/j.rse.2023.113970.
Mukundan, R., Gelda, R., Moknation, M., Zhang, X., Steenhuis, T. 2023. Watershed scale modeling of dissolved organic carbon export from variable source areas. Journal of Hydrology. 625(S1). Article e130052. https://doi.org/10.1016/j.jhydrol.2023.130052.
Chen, W., Zhou, Y., Stokes, E.C., Zhang, X. 2023. Large-scale urban building function mapping by integrating multi-source web-based geospatial data. Geo-spatial Information Science. 1-15. https://doi.org/10.1080/10095020.2023.2264342.