Project : USDA ARS

ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #431325

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

2019 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
Instruments for monitoring ecosystem processes were tested and deployed at different locations in the Lower Chesapeake Bay Long-Term Agroecological Research (LCB-LTAR) site. Continuous in-situ monitoring of streamflow was performed for the headwater basins of Tuckahoe, Greensboro, and Upper Pocomoke creeks. These basins constitute the portions of Hydrological Unit Code 10 watersheds and are defined by the drainage areas sampled at USGS gauging stations. Water quality monitoring instrument packages containing ultraviolet-visible (200 to 700 nm) spectrophotometer probes for in-situ monitoring of turbidity, nitrate, total organic carbon (TOC), and dissolved organic carbon (DOC) were co-located at the USGS gauging stations. Two eddy-covariance micrometeorological towers were established at the Optimizing Production inputs for Economic and Environmental Enhancement (OPE3) research site and in the Choptank Watershed, which were identically equipped with sonic anemometers and infrared gas analyzers to determine heat, water vapor, and carbon dioxide fluxes. Additional above-ground measurements include four components of the radiation budget, atmospheric pressure, air temperature, humidity, and precipitation. The soil measurements include the ground heat flux, temperature, and moisture content. Ten soil moisture/meteorological monitoring stations (including tipping bucket rain gauges) are installed on private lands distributed throughout the Choptank Watershed. Volumetric soil moisture is collected at four depths in the soil profile along with air temperature, rainfall, and photosynthetically active radiation (PAR). These data will aid parameterization of distributed process models. Two models, Soil Water Assessment Tool (SWAT) and Agricultural Policy/Environmental eXtender (APEX), were used to study the hydrology of depressional wetlands under restored and natural conditions. A paired wetland approach has been adopted for comparing hydrology of restored and natural depressional wetlands within agricultural landscapes. Two pairs of wetlands are involved in the study. The observation network was established for measurement of volumetric soil moisture at four depths. Soil moisture measurements are made along transects in topographic gradient from upland toward the center of the wetland. Ground water dynamics and influence on wetland hydroperiod are being determined by piezometers screened in the shallow groundwater aquifer below the wetland and a fully-screened well installed in the wetland sediment with the screen extending above ground to monitor above-ground surface inundation. Remote sensing methods are needed to assess crop conditions, conservation practices, and nutrient use efficiency. Surface reflectance (SR), vegetation index (VI), and evapotranspiration (ET) data cubes from multiple satellite sensors were generated for the LCB-LTAR and other study areas. Daily VI at 30-m resolution were produced by fusing Landsat-8, Sentinel-2, and MODerate resolution Imaging Spectroradiometer (MODIS) data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Crop green-up dates were extracted using those data cubes and are being compared to PhenoCam observations and crop green-up dates extracted from an experimental satellite mission (VENUS, 2-day repeat in 5-10m spatial resolution). The SR and VI data cubes include the period from 2016-2018 and comparison focuses on 2018 when Vegetation and Environmental monitoring on a New micro Satellite (VENµS) data become available. Daily 30-m resolution ET for the Choptank River Watershed from 2013-2017 were used to compare ET estimation from the SWAT model. NASA’s Landsat algorithm for estimating Leaf Area Index (LAI) was assessed using various sampling strategies. Field observations of LAI at the Beltsville Agricultural Research Center (BARC) area were started in early 2019 and will be used for algorithm validation. An approach to monitoring crop condition based on crop growth stages rather than calendar dates was developed. Crop residue cover and crop development stage were measured in 40-60 fields per site using the line-point transect method. When combined Landsat and Sentinel-2 data provided more cloud-free images and more frequent data for assessing soil tillage intensity and crop develop progress than were possible using either satellite alone due clouds. A MicrosoftWindows-10 graphical user interface was programmed using the current versions of the Scattering by Arbitrarily Inclined Leaves (SAIL) and Prospect models (collectively called PROSAIL). The graphical user interface allows easy access to advanced, new features of the PROSAIL model for calculation of canopy reflectance at different LAI and view angles. The model generated predictions of maize canopy reflectance at oblique view angles for testing with imagery acquired by small Unmanned Aircraft Systems (sUAS). Multiple remote sensing technologies were used to map wetland hydroperiod and surface water flow pathways. Light Detection and Ranging (LiDAR) and land-cover datasets were compiled for the Choptank River watershed. LiDAR-derived digital elevation models were used to extract relevant topographic metrics and improve the accuracy of hydrologic flow predictions. Channelized and non-channelized flow were mapped using flow accumulation and other algorithms within Geographic Information System (GIS) packages. The USDA Farm Service Agency provided the location of riparian-buffer conservation practices. The digital elevation data were used to model the location of pathways entering the buffer and assess degree of channelized flow that bypass the buffer to determine the amount of flow intercepted by buffer practices. Significant progress was made toward the understanding of nutrient and pesticide losses from agricultural fields. As a part of this work, the recently developed standard operating procedures for extracting and measuring metalochlor was validated with a field trial with a low flow rate (5 l/min) and 30-minute sampling period. A revised design for the relaxed eddy accumulation system was developed and a prototype unit was built for measuring volatilization of pesticides after application. In conjunction with the National Laboratory for Agriculture and Environment and the University of Iowa, a research plan was developed to study pesticide volatilization and transport at an additional site near Ames, Iowa. This new work greatly augments the work conducted at OPE3 by elucidating the mechanisms controlling pesticide loss under a broader range of environmental conditions. Modified Gaussian plume transport models were evaluated and used to characterize the impact of riparian buffers on pollutant transport. The results suggest agrochemical flux models based on the Relaxed Eddy Accumulation method are impractical when stable atmospheric conditions are present (such as at night). Therefore, the current plan will be refocused towards data collection to better understand the unique factors influencing volatilization overnight. Excessive ammonia emissions from poultry production can serve as a nitrogen source to the Chesapeake Bay via aquatic transport and atmospheric deposition. An open path ammonia laser was setup to collect data on ammonia concentrations along a transect extending across the main channel of the Choptank River to assess the potential for ammonia uptake by the river estuary. Water quality models like SWAT provide important information on effectiveness of conservation practices such as winter cover crops by predicting effects on water quality parameters. Wetland hydroperiod is an important driver of ecosystem biogeochemistry and is expected to have strong influence on the capacity for soil carbon storage with ecosystems. This project expanded collection of soil carbon data to include a broad range of depressional wetlands.

Accomplishments
1. Near real-time mapping of crop phenology. Crop progress information can benefit farmers in scheduling irrigation, fertilization and harvest operations. Satellite remote sensing data are used to map crop phenology and growth stage. However, near real-time monitoring at the early stages of crop development during spring is still challenging due to the lack of cloud-free satellite observations. A new approach was developed that combines historical Moderate Resolution Imaging Spectroradiometer (MODIS) data and the recently-launched Visible Infrared Imaging Radiometer Suite (VIIRS). Results show that VIIRS imagery captures spatial variability of crop emergence resulting from different planting dates. Furthermore, estimated crop growth stage is correlated with phenology data reported by National Agricultural Statistics Service (NASS). The date that plants emerge from the soil is better than the planting date for predicting crop yield and will eventually increase the accuracy of NASS yield forecasts.

2. Estimation of peak flow from failed earthen dams. A flood is most dangerous and destructive at the time of its peak flow. Predicting flood peaks originating from earthen dam failures is difficult because of the sparse nature of observations from this kind of rare event. ARS scientists at Beltsville, Maryland, created a substantial database from the aggregation of several small datasets relating to dam failures, which was used to compare various approaches for predicting peak flow. Traditional (regression-based) approaches were examined along with more innovative approaches based on machine learning algorithms. Calibration methods were examined noting the importance of prediction being limited to within the convex hull of observations. This research enhances public safety by the synthesis of data from the infrequent earthen dam failures and by predictions of peak flow which may be used to develop emergency plans in case of an earthen dam failure.

3. Remote sensing for conservation tillage practices. Remote sensing indices are used to estimate soil tillage intensity based on the crop residue cover remaining in the field after planting. Soil moisture conditions affect the estimation of crop residue, and therefore affect estimates of tillage intensity. However, the long revisit intervals of Landsat (i.e., 16 days), spring rains, and clouds have made monitoring soil tillage intensity challenging. Imagery from Landsat-8 and the European satellites Sentinel-2A and 2B were combined to create a high-temporal-resolution dataset at 30 m spatial resolution for the 6- to 12-week period when most crops are planted. A remote sensing algorithm was developed that mitigated the uncertainty caused by variable soil moisture conditions and significantly improved estimates of crop residue cover. These new techniques may be used to monitor the spatial and temporal changes in soil tillage intensity across landscapes and to identify where additional conservation practices may be required.

4. Effectiveness of forest riparian buffer zones depends on water flow pathway. Establishment and maintenance of riparian forest buffers is an important best management practice to reduce surface water pollution from agriculture. Riparian buffers are believed to be the most effective when surface runoff enters the riparian zone as dispersed sheet flow. However, channelized surface pathways into the riparian zone limit the effectiveness of forest buffers. Using high-resolution digital elevation data, ARS scientists at Beltsville, Maryland, compared two methods, topographic openness and flow accumulation, for evaluating the impact of channelized pathways on riparian buffer capacity. The flow accumulation technique was better in areas of medium to high relief such as those in the Piedmont and Appalachian Ridge and Valley physiographic provinces, whereas the topographic openness method was better in areas of low relief, such as the coastal plain. Conservation managers will obtain more accurate estimates of forest riparian buffer capacity by applying the appropriate method for a given landscape. Therefore, when designing and maintaining riparian buffers, occurrence of channelized flow pathways may be mitigated to protect stream water quality.

5. Isotopic signatures measure soil erosion rates for corn and soybean cropping systems. Soil is the largest carbon sink or reservoir in the biosphere with approximately 60% of the carbon stored as organic matter. A major unknown of the carbon cycle is the effect of erosion on soil organic carbon. There are two photosynthetic pathways by which plants initially fix atmospheric carbon dioxide, the C3 pathway (e.g. soybeans) and the C4 pathway (e.g. corn), which result in different stable carbon isotope (13-C) ratios in organic matter. In addition, stable cesium isotope (137-Cs) concentrations indicate the amount of soil erosion; therefore, differences in 13-C and 137-Cs ratios in the soil reflect a soil’s recent history. To better understand the combined impacts of tillage and crop type on soil erosion, we analyzed 13-C and 137-Cs ratios, historic aerial photographs, and digital elevation data to quantify carbon redistribution. We developed a topographic model which captured more than 60% of the variance in soil 137-Cs and 13-C. Our results indicate that cesium and carbon isotopes may be used to assess differences in soil erosion rates resulting from different tillage practices for corn and soybeans.

U.S. DEPARTMENT OF AGRICULTURE

Hydrology and Remote Sensing Laboratory: Beltsville, MD