Location: Hydrology and Remote Sensing Laboratory2018 Annual Report
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.
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.
Fiscal year 2018 was the second year of a 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 the three components of National Program 212 in an interdisciplinary manner. Results of many of these activities have been summarized and published. The first objective concerns research within the Lower Chesapeake Bay Long Term Agroecological Research (LCB-LTAR) site. The two main areas of the LCB-LTAR project are the Beltsville Agricultural Research Center (BARC) and the Choptank River watershed (located on the Delmarva peninsula). Landsat 4, 5, 7, and 8 data and WorldView-3 data of the study sites were acquired through USGS. Research was also conducted in collaboration within the Upper Mississippi Basin (UMB) LTAR site in the South Fork Tributary of the Iowa River watershed. Most landscape scale process-based models do not adequately represent wetland functions. A more comprehensive approach is needed that is capable of depicting more-detailed wetland processes and evaluating the overall effects of natural restored and historical wetlands on water quality. The Soil and Water Assessment Tool (SWAT) and Agricultural Policy/Environmental eXtender (APEX) combination model were considered for conducting wetland assessments. Wetlands and other aquatic related ecosystems, such as flood plains, are known to affect the water quality in adjacent streams, but are typically represented in these models as impoundments without the unique biogeophysical attributes of the wetlands and ecostystems. We found the representation of wetland processes in APEX model found to be overly simplistic. Therefore, the SWAT model was integrated with a well-known, yet improved, riparian wetland module along with the use of inundation maps derived from remotely-sensed data. This spatial data-model integrated framework is undergoing testing and validation with field observations. This study, when completed, will illustrate how spatially distributed information is necessary to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place. Rainfall-runoff data from small agricultural watersheds spanning the mid-1930s to the present were assembled to inform a broad portfolio of hydrologic research contributing to the LTAR network research. This database containing over 14,000 station-years of observations from 21 states was added to the National Agricultural Library’s Ag Data Commons and included rainfall, runoff, temperature, and other ancillary information. A longstanding group of hydrologic methods and models developed by the USDA depend centrally on the concept of a curve number that characterizes the likelihood of a parcel of land to produce runoff as a function of its soils and land use. However, recent research has suggested a different relationship between curve numbers, storage, and the rainfall fraction that is withheld from runoff production at the onset of a storm event. Both the current and proposed relationships were examined for storage and initial abstraction in the context of runoff production, peak discharge, and detention storage needs as they vary geographically with rainfall magnitude, frequency, and storm type. When completed, this study will provide a quantitative accounting of how estimated runoff and peak flows differ when comparing the current and proposed relationships. This will have implications relating to potential changes in drainage design, flood control, and sediment transport. The research of second objective assesses the use of remote sensing to measure biophysical variables related to agricultural production, nitrogen use efficiency, and environmental assessment. The major site for this research is the 20-year experiment, Optimizing Productivity for Economic and Environmental Enhancement (OPE3), located in BARC. Using the integrated Landsat-MODIS data fusion and phenology package, data fusion results were generated and evaluated over the Choptank River watershed. The new Sentinel-2A and -2B data (2016-2017) from European Space Agency were included in data fusion in the South Fork site of the UMB-LTAR. The fully-integrated Landsat-MODIS-Sentinel-2 multi-sensor data fusion system was built. The feasibility of using the ARS SCINet computing facility for data fusion system is under investigation. Data fusion and phenology analysis for OPE3 has been postponed due to the change of acquisition plan from the Venus mission. A French-Israel satellite has been acquiring data every two days at 5m resolution since 2018and the OPE3 site will be observed on 2019. Comparison of different data fusion approaches were performed at different spatial resolutions including Landsat-MODIS data fusion (30m) and Planet-Worldview data fusion (2m). The selection of optimal input image pairs for data fusion were analyzed. A real-time monitoring of crop phenology using coarse resolution (500m) MODIS and VIIRS observations were analyzed. NASA and the European Space Agency have harmonized Landsat-8 and Sentinel-2 images to a common calibration and format which should provide additional opportunities to assess soil tillage intensity and crop growth and condition. Although the download capacity of Sentinel-2 was limited in 2016 and 2017, sufficient harmonized images were acquired to monitor soil tillage intensity in a portion of the South Fork watershed (UMB-LTAR). Crop residue cover was measured in over 45 fields shortly after planting in both years. WorldView-3 images were also acquired which included additional shortwave infrared bands not available on Landsat-8 or Sentinel-2. Residue cover indices that used the relatively narrow spectral bands of WorldView-3 were more robust to variations in moisture conditions than residue indices that used the broad spectral bands of Landsat-8 or Sentinel-2. Methods to calibrate Landsat-8 residue cover indices using WorldView-3 indices were explored. Data fusion tools for combining Landsat and MODIS visible and near infrared bands to assess crop conditions were tested. Multispectral sensors mounted on small unmanned aircraft have been be considered for detection of nitrogen deficiency in crops, but research showed that the spectral vegetation indices may not have sufficient sensitivity for predicting side-dress nitrogen requirements. Oblique sensor orientations may be more sensitive to nitrogen deficiency. To this end, the radiative transfer model PROSAIL is being revised for more efficient input and output options. The new multispectral sensors have up-looking, incident light sensors to calculate reflectances from image data; however, the roll, pitch, and yaw (attitude) of the aircraft will affect the amount of light incident on the up-looking sensor. Reflectances over the same crop fields were found to differ with respect to aircraft attitude from an inertial measurement unit. Variation of incident light on the up-looking sensor is being investigated as a possible cause for the different reflectances. Eighteen years of maize leaf and canopy reflectances measured by field-portable spectrometers at the OPE3 fields are being organized for analysis. Data quality varied within and between datasets, which needs to be factored into the reanalysis. The thrust of Objective 3 is to measure and model landscape processes that influence the fate and transport of agriculturally-important chemicals. Due to the practical constraints of the relaxed eddy accumulation (REA) approach, along with several unanticipated engineering challenges, it became necessary to: (a) conduct additional field trials to refine several operational parameters (flow rate, valve speed, measurement period, etc.) of the sensor system; and (b) modify the design of the REA system to ensure it will accurately measure the fluxes of pesticides, such as metolachlor and atrazine. These activities were conducted in lieu of the pesticide volatilization experiment; the REA system is currently being built. While the design requirements of the REA system lie at the limit of commercially available components, progress has been made in confirming the viability of the sampling regime (5 L/min flow rate and 30-minute sampling period), redesigning the REA system so that this sampling regime is feasible, identifying system components such as fast-acting solenoid valves that can meet the design specifications, and fabricating the sensor package, which is currently ongoing. A new Gaussian plume model was developed that simulates the atmospheric emissions from poultry house tunnel fans. The model was validated and then used to evaluate the effectiveness of vegetative buffers in controlling ammonia and particulate matter released from the poultry house. Instruments to be deployed in the Choptank River watershed to measure ammonia flux were damaged in shipment and will be deployed next year.
1. Proposed changes by the Natural Resources Conservation Service (NRCS) to the rainfall-runoff curve numbers used in models will change assessment of flood abatement. Longstanding hydrologic methods and models developed by the NRCS have depended on the curve number, which is quantifies the runoff produced by a parcel of land with a mixture of land use types. Engineers, developers, and land use policy makers use curve numbers for estimating flood risk and sewer capacity. Resulting changes to modeled runoff volumes and flood peaks were examined by ARS scientists in Beltsville, Maryland, for various geographic regions and rainfall patterns. Results using the proposed changes showed some areas with increased curve numbers and others with decreased curve numbers. Flood abatement costs increased or decreased accordingly. These new findings will help with assessing hydrologic risks of land use change and mitigation throughout the United States.
2. Practical USDA pollutant-transport tool is more accurate for surface runoff compared to soil leaching. The Soil Vulnerability Index (SVI), recently developed by USDA, is a practical tool to identify crop fields that have a high risk of pollutant transport through surface runoff and leaching. ARS scientists from Beltsville, Maryland, tested the suitability of the SVI method on the Choptank River (Maryland) watershed. Outputs from a computer simulation model, the Soil and Water Assessment Tool (SWAT), were compared to the SVI classification scheme. Comparison results indicated that the SVI method was less accurate for identifying nitrate vulnerability caused by leaching. The SVI index was more accurate in estimating surface runoff of organic nitrogen. Based on the pollutant type of interest, state National Resources Conservation Service (NRCS) offices and other local agricultural management agencies can use the SVI method to classify crop fields vulnerable to surface runoff and leaching processes.
3. Combining vegetation monitoring data from multiple satellites will provide farmers with information for better agronomic practices. Precision farming uses variation within a field to determine the application rates of fertilizers and agrochemicals, but farmers need reliable information sources about variation. Frequent imagery at high-spatial resolution is ideal for monitoring vegetation and crop conditions at the sub-field scale. Currently, the Worldview satellites can provide 2-m resolution images; however, the revisit time is more than seven days. The new Planet satellites can provide daily images, but at a coarser resolution. Data fusion techniques provide feasible ways to combine Worldview and Planet satellite images for producing daily 2-m resolution images. Three different data fusion approaches were compared and showed that all three approaches can generate high-quality images and have comparable performance over different landscapes. Generation of daily, high resolution images at the sub-field scale will help individual farmers better manage their crops and enhance both production and environmental quality.
4. Remotely-sensed residue cover indices are more accurate after correction for surface moisture. Leaving residue to cover the ground after harvest is an important conservation practice for enhancing the soil and preventing erosion. However, variations in moisture conditions within fields and across landscapes increases the uncertainty of remotely-sensed estimates of crop residue cover. Adjusting the reflectance in each spectral band for variations in moisture conditions gave rise to more accurate residue cover estimates. Residue cover indices that use the relatively narrow spectral bands of WorldView-3 were more robust to variations in moisture conditions than residue indices that use the broad spectral bands of Landsat-8 or Sentinel-2. These techniques provide tools for government agencies and local soil conservation districts to monitor the spatial and temporal changes in crop residue cover and identify where additional conservations practices may be required.
5. New best management practices are developed for using small unmanned aircraft in precision agriculture. Remote sensing from small unmanned aircraft systems, or drones, is a new technology that may help individual farmers produce food and fiber more efficiently. However, confusion continues concerning best management practices for using unmanned systems in agriculture, which had led to farmers paying extra for unnecessary services. ARS scientists in Beltsville, Maryland, developed guidelines for using drones based on farmer objectives. This research will benefit drone makers, service providers, and farmers by outlining the different requirements for remotely-sensed data and showing how those requirements can be met.
6. Tree height measurements increase accuracy of soil moisture estimates derived from satellites. Soil moisture content is monitored from space using satellite microwave radiometers to increase knowledge about the amount and extent of drought worldwide. However, a major limitation to the accuracy of estimating soil moisture is the amount of moisture in the vegetation above the soil, particularly in the woody stems of forests and woodlands. ARS scientist in Beltsville, Maryland, and Naval Research Laboratory scientists used overlapping high resolution aerial images to measure the heights of woodlands and crops. Established forestry equations were used to calculate the water in woody stems from tree height, which is then subtracted from the total satellite signal to obtain more-accurate, soil-moisture content. These results will be used directly by weather and space agencies to improve estimates of soil moisture content and will provide needed information on drought, food security, and crop risk assessments.
7. Using winter cover crops may enhance water quality under climatic change. Winter cover crops are used as a best management practice (BMP) for their effectiveness in reducing nitrate loads from agricultural lands into streams in the Chesapeake Bay watershed. There is concern that cover crops may not be effective enhancing water quality in the future with climatic change. ARS scientists from Beltsville, Maryland, used the SWAT model to assess three possible climate scenarios and six agricultural systems with winter cover crops. Under climatic change, the biomass of cover crops increased, which increased nitrate uptake, potentially improving water quality. This information will be useful to non-governmental agencies such as the Chesapeake Bay Foundation.
8. Mapping denitrification potentials for agricultural landscapes. Denitrification is the primary process that removes applied nitrate fertilizer before either plant uptake or soil leaching. ARS scientists in Beltsville, Maryland, measured denitrification potential of soils at three sites within the Choptank River (Maryland) watershed. Topography explained the greatest amount of variation in denitrification potential across the three sites. Therefore, topographic metrics derived from Light Detection and Ranging (LiDAR) data have the potential to improve understanding of denitrification variability at the landscape scale. The improved information on denitrification at field and landscape scales will advance implementation of precision agriculture technologies for minimizing impacts crop production on water quality.
9. Mapping distribution of soil organic carbon using topographic metrics. Soil organic carbon (SOC) is an important soil property and a key factor affecting soil quality. SOC is lost by multiple processes, including soil aggregate disruption, soil erosion and redistribution, and SOC mineralization. ARS scientists in Beltsville, Maryland, and Ames, Iowa, took advantage of the radioactive cesium–137 fallout produced from atmospheric testing of atomic bombs to determine patterns of SOC distribution across 560 sampling locations at two field sites within the Walnut Creek watershed in Iowa. Topography alone explained 62% variability in SOC content and soil redistribution. This study indicates that models based on topography will be effective tools for scaling of SOC content and soil redistribution from field to watershed scales.
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