2010 Annual Report
1a.Objectives (from AD-416)
Develop new remote sensing, modeling and data assimilation techniques to improve the monitoring of hydrologic fluxes and agricultural pollutant pathways at the field, watershed and regional scale.
1b.Approach (from AD-416)
Effective management of these watersheds requires detailed process-level understanding concerning the complex hydrologic and constitutive flux pathways that govern: the availability of root-zone soil moisture, the delivery of agricultural pollutants to surface water bodies and feedbacks operating along the soil-plant-atmosphere continuum. The inability to measure the relative magnitude of these pathways hampers the development of effective water management strategies at watershed- and regional-scales. This project attempts to develop novel remote sensing and modeling tools to better characterize key hydrologic and constitutive flux pathways operating within agricultural watersheds. An overarching theme of the project is that the integration of remote sensing products into models can enhance the utility of models for critical agricultural applications.
This project attempts to develop novel remote sensing and modeling tools to better characterize key hydrologic and constitutive flux pathways operating within agricultural watersheds. Objectives in the fourth year of the project were aimed at the development and implementation of new tools for such characterizations. Examples of such tools include a novel remote sensing-based method for capturing the impact of near-surface air temperature variations on evapotranspiration within agricultural landscapes and the development of a retrospective (2000-present) satellite-based agricultural drought index for the United States. Other work focused on the development of agricultural applications for the expected 2014 launch of the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. If properly prepared for, SMAP should represent a significant improvement in our ability to detect agricultural drought worldwide. Example of vital preparation work include the inter-comparison of large-scale, satellite-based estimates of vegetation water content (required for SMAP to accurately estimate surface soil moisture) and the establishment of a ground instrumentation test-bed for SMAP validation activities. Related work has focused on the development of innovative remote sensing data analysis and modeling techniques for water quality conservation practice assessment within the Chesapeake Bay watershed. The only unmet 2009 milestone is attributable to discontinued use of the Annualized AGricultural Non-Point Source (AnnAGNPS) for water quality monitoring in the Choptank Watershed. This change required a re-direction in the research and is based on replacing AnnAGNPS with a comparable.
The operational implementation of a satellite-based drought product for the contiguous U.S. Satellite-based drought indices can be used to supplement coarser resolution data from weather and precipitation networks to assess drought conditions across the United States. Because land-surface temperature is strongly modulated by evaporation, thermal infrared (TIR) remote sensing data carry valuable information regarding surface moisture availability and therefore have been widely used to map drought and vegetation stress. Using Geostationary Operational Environmental Satellite TIR imagery, a fully automated inverse model of Atmosphere-Land Exchange has been used to model hourly evapotranspiration (ET) over a 10-km resolution grid covering the contiguous United States. From these ET estimates, we have developed and evaluated an Evaporative Stress Index, reflecting temporal anomalies in the actual-to-potential ET ratio. Quantitative comparisons with standard drought metrics have been conducted to demonstrate that the remote sensing ET index captures spatiotemporal variability in rainfall patterns without requiring precipitation data as input. This index is therefore well-suited for applications in data-sparse parts of the globe where food and water security are of significant issue. The model processing infrastructure has been revised to utilize standard satellite and meteorological data fields generated by the National Oceanic and Atmospheric Administration (NOAA), and operational processing is being transitioned to NOAA for incorporation into the Climate Prediction Center’s North American Drought Briefing. Products are also being evaluated by the National Drought Mitigation Center.
Use of NEXRAD improves streams flow calibrations in SWAT model: A better precipitation data-source for accurate water balance in watershed models. Hydrologic and water quality models are very sensitive to input parameter values, especially precipitation input data. We compared several sources of rainfall data with the next generation radar (NEXRAD) rainfall data to examine the impact of such sources on Soil and Water Assessment Tool (SWAT) model streamflow calibrations for a watershed located in the coastal plain of Maryland. Model simulation results indicated that distance and location of the rain gauges located outside the watershed boundary have a significant impact in simulating hydrologic and water quality response of the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced more accurate estimates of streamflow than using single gage data. This study concludes that one has to be mindful of the source and methods of interpolation of rainfall data for input into hydrologic and water quality models if high –quality simulations are desired.
Improving precipitation measurements using remote sensing and data assimilation. Measurements of short-term (< 1 week) rainfall accumulations are widely used for a large number of agricultural management applications (e.g. yield prediction, drought detection and irrigation scheduling). However, over many agricultural-producing regions of the world, insufficient numbers of ground-based rain gauges are available to accurately measure such accumulations. Satellite-based rainfall retrievals offer an alternative, but are prone to large error over land areas. This research describes and implements an alternative rainfall measurement technique based on observing temporal variations in surface soil moisture observations made from a satellite microwave radiometer. These soil moisture variations can be merged with a surface water balance model (forced by the satellite-based rainfall retrievals) to yield temporally dynamic corrections to the rainfall forcing. Global application of this technique - called the Soil Moisture Analysis Rainfall Tool - has demonstrated its potential for significantly improving our ability to measure 3-day rainfall accumulations over areas of the world (e.g. North Africa) with significant food security issues associated with agricultural drought.
Geospatial tools for Conservation Effects Assessment in the Chesapeake Bay Region. Choptank River Conservation Effects Assessment Project has completed five years of field work to support the development and implementation of geospatial adaptive management tools for cover crop management. Developed in collaboration with the United States Geologic Survey and the Maryland Department of Agriculture, the tools combine field-specific conservation program enrollment data with satellite imagery analysis for rapid quantification, verification and certification of cover crop performance. Now that the methodologies have been successfully developed, Maryland is currently beta-testing technology transfer of the geospatial cover crop management toolkits at the Talbot County, Maryland Soil Conservation District. Part of our strategy is to automate the aggregation of the analytical output (site-specific conservation program performance data) to match watershed and county boundaries, so as to provide useful, rapid appraisal of cover crop success in protecting water quality, while also maintaining farm data privacy as required in Section 1619 of the Farm Bill. By continuing the work conducted in collaboration among the Maryland Department of Agriculture, the United States Department of Agriculture, the United States Geologic Survey, the National Fish and Wildlife Agency, and other partners, we hope to substantially improve the effectiveness of winter cover crop nutrient uptake in the Chesapeake Bay Region.
Using a Large Eddy Simulation model with remote sensing to account for variability in atmospheric properties and potential errors in flux estimation. Land surface fluxes and in particular evapotranspiration (ET) are strongly affected by variability in land surface properties (soil texture and land cover) and states (soil moisture and surface temperature) as well as overlying atmospheric conditions. The Large Eddy Simulation (LES) model has been fully coupled with a remote sensing-based land surface model for simulating the influence of land surface states and properties on exchange of heat, water vapor and momentum (wind energy) with the lower atmosphere. The model was applied to an agricultural study region in the Southern Great Plains for investigating in detail the spatial relationships between surface fluxes and near-surface atmospheric properties, and the potential errors in flux estimation due to homogeneous atmospheric inputs over heterogeneous landscapes. If air properties from a single weather station in a non-representative location within the landscape are applied uniformly over the domain, significant differences in surface flux estimation with respect to the LES output are observed. The spatial correlations of atmospheric properties with the fluxes, the land cover properties, and surface states were examined and the spatial scaling of these fields is analyzed using a two-dimensional wavelet technique. The results indicate a significant local correlation of the spatial distributions of the air temperature Ta with the sensible heat flux H, the specific humidity q with ET or the latent heat flux LE, and the wind speed U with the surface roughness z0. A simple yet practical method has been proposed using remotely sensed observations and the land surface scheme, based on general linear expressions derived between Ta and H, q and LE/ET, and U and z0. This approach is recommended when only local weather station observations are available.
Developing a remote sensing-based thermal sharpening technique for monitoring evapotranspiration and moisture stress over agricultural landscapes. Operational monitoring of daily evapotranspiration at field scales is possible using land surface temperature maps developed from thermal band remote sensing at high spatiotemporal resolution (hourly information with 100m pixel size or finer). Unfortunately, no single satellite system affords both high spatial and high temporal resolution thermal imaging. Therefore, we have developed a technique for fusing information from multiple satellites with different revisit cycles and pixel sizes to produce the input data required to map daily evapotranspiration at 30m resolution. This strategy uses information from the geostationary satellites (hourly imagery at 10km resolution), the Moderate Resolution Imaging Spectroradiometer instrument (daily at 1km) and Landsat (16-day revisit at 60-120m). We have also finalized development of a thermal sharpening strategy that further improves Landsat thermal imagery down to the 30m resolution of the Landsat visible bands. This sharpening algorithm has been tested over several landscapes in different climatic regions, and refined to accommodate sharpening of surface water bodies. The technique has been automated and integrated into our routine evapotranspiration evaluations.
Estimating vegetation water content from the moderate resolution imaging spectroradiometer. Microwave retrievals of soil moisture depend on estimates of vegetation water content. If vegetation water content can be independently estimated with remote sensing using shortwave infrared wavelengths, then microwave retrievals of soil moisture will be more accurate. Vegetation water contents estimated from both Moderate Resolution Imaging Spectroradiometer and WindSat sensors were equal over Iowa. The relationship between Moderate Resolution Imaging Spectroradiometer shortwave infrared indices (normalized difference infrared index) and vegetation water content was tested during the 2005 Soil Moisture field Experiment. This shows that new algorithms for microwave sensors can provide good estimates of vegetation water content, implying that soil moisture estimates are also accurate. With the expected launch of the Soil Moisture Active Passive satellite by the National Aeronautics and Space Administration, soil moisture data products will be important for assessing the impact of drought on agriculture.
Successful installation of the soil moisture active passive In Situ Sensor Testbed (ISST). As part of the Soil Moisture Active Passive (SMAP) satellite validation program, an in situ soil moisture sensor testbed was developed and installed in Marena, OK. This testbed includes all of the major soil moisture technologies currently used by national and international soil moisture networks. Also, included are several new and emerging technologies, such as the Cosmic ray Soil Moisture Observing System and the Global Positioning System Reflectometry project which will be deploying large scale networks in the near future. In addition, to in situ instrumentation, this effort will be supported by regular gravimetric sampling and vegetation sampling which is critical to satellite validation. This testbed will be ongoing through the launch of the SMAP satellite and will be a part of the validation program. This testbed has international cooperation with scientists from Japan and The Netherlands participating in sensor deployment and analysis.
Combined passive active soil moisture observations to simulate satellite systems. A National Aeronautics and Space Administration satellite currently under development, the Soil Moisture Active Passive (SMAP) mission, will attempt to combine two techniques (microwave radiometry and radar) to produce a global high accuracy and improved spatial resolution soil moisture product with high temporal frequency. In order to explore the algorithm concepts that might be implemented for SMAP, an experiment was conducted using an aircraft-based prototype of SMAP that was embedded in the Cloud Land Atmospheric Interaction Campaign conducted over the Southern Great Plains in June 2007. The results demonstrated the complementary information that each sensor can provide and indicated the path that further development should take. These analyses will contribute to the refinement of the SMAP mission design. SMAP will produce a wide range of scientific and societal benefits that include agricultural hydrology, weather and climate forecasts.
Estimation of forest canopy effects on radar sensing of soil moisture. For the remote sensing of soil moisture, the presence of trees causes attenuation to the soil emission underneath. For accurate soil moisture retrievals through a forest canopy, the correction for the vegetation effects is needed. A new technique for determining the canopy attenuation using the measured stepped frequency radar backscatter response was proposed. The technique was applied to truck based radar data collected over stands of deciduous Paulownia trees under various physical conditions located at a Maryland test site. Using this procedure, analysis for various cases was carried out in order to understand the backscattering sources within a forest canopy and their effects on the transient response of the backscattered field. The technique developed here can be adapted to the airborne or spaceborne systems using similar instruments. Having the forest attenuation information on a global basis will help to extend accurate soil moisture retrievals from global microwave mission to more areas of Earth’s surface than are currently feasible.
WindSat global soil moisture retrieval and validation. A physically-based six-channel land information algorithm was developed for simultaneously retrieving global soil moisture, vegetation water content and land surface temperature using data from the Department of Defense WindSat multiple channel and polarization satellite instrument. The WindSat instrument is the first spaceborne polarimetric microwave radiometer. The algorithm was validated for a range of temporal and spatial scales using soil moisture climatology, ground in situ network data, precipitation patterns, and green vegetation fraction data. Despite the diverse set of climatic and biome regions included in the validation studies, the comparison between in situ and retrieval data show consistently good performance for all the sites. At the global scale, the soil moisture and vegetation retrievals vary spatially according to the climate zones. The overall validation results suggest that the WindSat land algorithm is able to separate reasonably well the soil moisture and vegetation effects in the microwave data; and the retrievals can capture land parameter variations in the hydrological processes. It is expected that the validated WindSat products will be utilized in numerical weather prediction and climate models that provide important decision information to agricultural hydrology.
Successfully modeling of chemical transport with model abstraction. Successful understanding and modeling of chemical transport in soils and groundwater is a precondition of risk-informed predictions of subsurface contaminant transport. Present day water quality models, though complex are typically poor predictors of chemical transit times. However, model abstraction is an emerging methodology for reducing the complexity of a simulation model while maintaining the validity of the simulation. The objective of this work was to use model abstraction techniques to characterize and understand flow and transport in soils in the presence of shallow groundwater. We developed two case studies by carrying out two types of field tracer experiments at the USDA Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) Beltsville field site, and applying a sequence of model simplifications based on existing hydrologic models. Soil moisture, soil water potential, tracer concentrations in groundwater, groundwater levels, and weather data, along with ground-penetration radar surveys, electric resistivity monitoring, and dilution tests complemented borehole log data and laboratory hydraulic measurements to characterize soil heterogeneity. The invoked series of model abstractions showed the important role of subsurface heterogeneity in the vadose zone and groundwater, and substantial improved the conceptualization of the subsurface.
Validation of advanced microwave scanning radiometer soil moisture products. Global soil moisture products are now being operationally generated by space agencies in the U.S. and Japan. Validating these products is critical to the potential users who must know whether they are accurate and reliable. The disparity of spatial scales between what we are able to measure on the ground to the large domains that the satellite observes makes this a challenging task. In this study, soil moisture products were compared to in situ observations derived from networks developed specifically for this purpose. The results indicate that each of the products evaluated had different performance statistics that depend upon the site conditions and that there is much room for improvement in some products. A positive outcome of the analysis is that it appears that the approaches do perform within reasonable error bounds. Soil moisture products from satellite sensors have the potential to dramatically improve the accuracy and timeliness of weather, climate, and agricultural assessments and forecasts used by the United States Department of Agriculture, the National Oceanic and Atmospheric Administration, and other agencies.
Houborg, R.M., Anderson, M.C., Norman, J.M., Wilson, T., Meyers, T. 2009. Intercomparison of a 'Bottom-up' and 'Top-down' modeling paradigm for estimating carbon and latent heat fluxes over a variety of vegetative regimes across the U.S. Agricultural and Forest Meteorology. 149:1875-1895.
Lang, M.W., McCarty, G.W. 2009. LiDar intensity for improved detection of inundation below the forest canopy. Wetlands. 29:1166-1178.
Agam, N., Kustas, W.P., Anderson, M.C., Norman, J.M., Colaizzi, P.D., Howell, T.A., Prueger, J.H., Meyers, T.P., Wilson, T.B. 2010. Application of the Priestley-Taylor Approach in a Two-Source Surface Energy Balance Model. Journal of Hydrometeorology. 11:185-198.
Gonzalez-Dugo, M.P., Neale, C., Mateos, L., Kustas, W.P., Prueger, J.H., Anderson, M.C., Li, F. 2009. A comparison of operational remote sensing-based models for estimating crop evapotranspiration. Agricultural and Forest Meteorology. 149:1843-1853.
Kustas, W.P., Anderson, M.C. 2009. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology. 149:2071-2081.
Cosh, M.H., Tao, J., Jackson, T.J., McKee, L.G., O'Neill, P. 2010. Vegetation water content mapping in a diverse agricultural landscape: The National Airborne Field Experiment 2006. Journal of Applied Remote Sensing (JARS). 4(043532):1-12.
Cosh, M.H., Kabela, A., Hornbuckle, B., Gleason, M.L., Jackson, T.J., Prueger, J.H. 2009. Observations of dew amount using in-situ and satellite measurements in an agricultural landscape. Agricultural and Forest Meteorology. 149:1082-1086.
Crow, W.T., Reichle, R. 2008. Comparison of adaptive filtering techniques for land surface data assimilation. Water Resources Research. 44(W08423):100-112.
Huang, J., Chen, D., Cosh, M.H. 2009. Sub-pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites. International Journal of Remote Sensing. 30(8):2075-2104.
Crow, W.T., Ryu, D. 2009. A new data assimilation approach for improving hydrologic prediction using remotely-sensed soil moisture retrievals. Hydrology and Earth Systems Sciences. 13:1-16.
Schaefer, G., Cosh, M., Jackson, T. 2007. The USDA Natural Resource Conservation Service Soil Analysis Network (SCAN). Journal of Atmospheric and Ocean Technology. 24:2073-2077.
Scanlon, T.M., Kustas, W.P. 2009. Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agricultural and Forest Meteorology. 150:89-99.
Choi, M., Kustas, W.P., Anderson, M.C., Allen, R.G., Li, F., Kjaersgaard, J.H. 2009. An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX. Agricultural and Forest Meteorology. 149:2082-2097.
Li, F., Crow, W.T., Kustas, W.P. 2010. Estimating root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Advances in Water Resources. 33:201-214.
Ryu, D., Crow, W.T., Zhan, X., Jackson, T.J. 2009. Correcting unintended perturbation biases in hydrologic data assimilation using Ensemble Kalman filter. Journal of Hydrometeorology. 10(3):734-750.
Bolten, J.D., Crow, W.T., Jackson, T.J., Zhan, X., Reynolds, C.A. 2010. Continental-Scale evaluation of assimilated soil moisture retrievals from the advanced microwave scanning radiometer. Geoscience and Remote Sensing Letters. 3:57-66.
Karnieli, A., Agam, N., Pinker, R.T., Anderson, M.C., Imhoff, M.L., Gutman, G.G., Panov, N., Goldberg, A. 2010. Use of NDVI and land surface temperature for assessing vegetation health: merits and limitations. Journal of Climate. 23:618-633.
Das, N., Mohanty, B., Cosh, M.H., Jackson, T.J. 2008. Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04. Remote Sensing of Environment. 112:415-429.
Kabela E., Hornbuckle, B., Cosh, M.H., Anderson, M.C., Gleason, M.L. 2009. Dew frequency, duration, amount, and distribution in corn and soybean during SMEX05. Agriculture Forest Meteorology. 149(1):11-24.
Kurum, M., Lang, R., O'Neill, P., Joseph, A., Jackson, T.J., Cosh, M.H. 2009. Estimation of forest canopy attenuation at L-band by a time-domain analysis of radar backscatter response. IEEE Transactions on Geoscience and Remote Sensing. 47:3026-3040.
Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Peipmeier, J.R., Koster, R.D., McDonald, K.C., Moghaddam, M., Moran, M.S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W. 2009. The Soil Moisture Active and Passive (SMAP) Mission. Proceedings of the IEEE. 98(5):704-716.