2009 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. 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. Early portions (FY 2007 to 2009) of the project plan generally focused on the development and implementation of modeling and/or remote sensing techniques in isolation. Examples of this include successfully completed 2009 milestones aimed at the development of improved remote sensing technologies (see Objectives 1.1, 1.2 and 3.1), ground-based validation technologies (Objective 1.3), and land modeling techniques (Objectives 2.1 and 3.2). Successful completion of these tasks set the stage for more integrative (modeling + remote sensing) project milestones during the last two years of the project. An early example of such integrative activities is the successful implementation of data assimilation techniques to incorporate remotely-sensed surface soil moisture retrievals into the Soil Water Assessment Tool (SWAT) (Objective 2.3). These accomplishments address NP211 Problem Areas I Effectiveness of Conservation Practices, II Irrigation and Water Management, and IV Watershed Management, Water Availability, and Ecosystem Restoration. In particular, they represent key steps in on-going efforts to more effectively apply remote sensing technologies to the large-scale evaluation of environmental factors impacting - and impacted by – agriculture management. The only unmet 2009 milestone (pertaining to Objective 2.2) is attributable to a change in modeling framework from the Annualized AGricultural Non-Point Source (AnnAGNPS) model to SWAT. This change represents only a minor temporary setback that rendered the relative 2009 milestone irrelevant but should not impact future Objective 2.2 milestones. All other 2009 milestones in the project plan were fully met and the project remains in well-positioned to fully meet ambitious 2010 milestones.
Preparation for the first soil moisture mapping satellite. Remote sensing technology has a huge potential for improving water management and hydrologic prediction by providing soil moisture observations. Solutions to challenging technology problems are now feasible and the first dedicated soil moisture satellite, the Soil Moisture and Ocean Salinity (SMOS) mission, is scheduled for launch in late 2009. In order to prepare for this new data resource, a targeted field experiment was conducted in 2006 called the National Airborne Field Experiment (NAFE) in the Murrumbidgee catchment (Australia) to provide simulated SMOS observations supported by ground measurement of soil moisture and other relevant ground data. This effort was designed to contribute to the development of the SMOS retrieval algorithms to estimate surface soil moisture from satellite sensors. SMOS-type data were collected using a Polarimetric L-band Multi-beam Radiometer (PLMR). Flights included 1km resolution passive microwave data across the main 40km x 55km study area every 2-3 days, verification of downscaling techniques and assimilation, and a transect twice a week to provide both 500m multi-angular passive microwave data for algorithm development and 50m resolution passive microwave data for algorithm verification. These investigations will allow point measurements from existing monitoring stations to be extrapolated to the footprint scale and so address scaling issues. Knowing the soil moisture and its distribution across this site could greatly help farming operations by allowing farmers to predict vegetative growth, crop rotations and stocking rates during an era of climate uncertainty. The SMOS mission will significantly improve our global assessments and provide improved information for weather and climate forecast models.
Successful integrated application of radar and lidar to monitor wetlands in agricultural landscapes. Wetland restoration is an important component in water quality improvement strategies in the Choptank River and for the Chesapeake Bay. Wetlands have great potential for mitigating agricultural pollution but managing agricultural landscapes to maximize their effectiveness requires detailed information hydrology of wetlands and their connection to the larger landscape. Radar and lidar are two remote sensing approaches involving active sensors that show great promise for providing spatial information for mapping and hydroperiod characterization of the wetland. ARS research has shown great synergy of information can be gained by the combined use of these active sensors. Radar can provide detailed temporal information on hydroperiod and lidar can provide detailed surface elevation maps and instantaneous inundation maps useful for mapping wetland. This synergy of information improves understanding of ecological services provided by wetlands and their connection to the larger landscape which will have bearing on management and conservation of wetland ecosystems within agricultural landscapes.
Development of a remote sensing-based thermal sharpening technique for monitoring evapotranspiration for rain-fed and irrigated agricultural landscapes. Surface energy balance models are currently available for operationally deriving spatially-distributed evapotranspiration (ET) maps over landscapes by using satellite remote sensing in the visible–near-infrared for estimating fractional vegetation cover, and the thermal infrared bands for estimating land surface temperature (LST). A thermal sharpening strategy was developed that utilizes the functional relationship between spaceborne-derived LST and vegetation indices to sharpen course thermal infrared imagery to a resolution sufficient for generating ET maps at the field scale. The utility of the thermal sharpening was examined for a rain-fed corn and soybean production region in central Iowa, and an irrigated agricultural area in the Texas High Plains. In the absence of sub-field scale resolution thermal data, the sharpening algorithm provides an important tool for routine monitoring ET over rain-fed agricultural areas using satellite observations. In contrast, over irrigated regions, the thermal sharpening approach is unable to provide as accurate fine resolution ET due to significant sub-pixel soil moisture variations that are not captured in the sharpening procedure. However, this thermal sharpening algorithm is relatively simple and fairly robust for many landscapes and is being implemented routinely with a regional two-source energy balance model using coarse resolution satellite data over targeted agricultural areas. This will be a very useful tool for monitoring crop water use and stress in water-limited regions.
Successful testing of a data assimilation system to integrate remotely-sensed surface soil moisture retrievals into the operational USDA FAS drought monitoring system. The USDA Foreign Agricultural Service (FAS) is tasked with globally monitoring agricultural drought and predicting its impact on world wide food production. To enhance their ability to provide such monitoring, a data assimilation system has been developed which integrates remotely-sensed surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument aboard the NASA Aqua satellite into the operational FAS soil water balance model. To test the effectiveness of this system and to assess the added value of satellite soil moisture data for drought monitoring, a data denial study has completed over the continental United States. This study tested the data assimilation system by intentionally degrading the quality of rainfall forcing data feed into model and then attempting to correct for the impact of this degradation through the assimilation of AMSR-E surface soil moisture products. Results demonstrated that, for large areas in the central and western United States, assimilation of the AMSR-E product was able to correct for a significant portion of root-zone soil moisture error attributed to the degraded rainfall. Since uncertainty in rainfall inputs is the primary source of error in USDA FAS agricultural drought predictions, these results demonstrate that data assimilation of soil moisture into soil water balance models can substantially improve our ability to globally monitor large-scale variations in agricultural productivity related to agricultural drought. The full operational implementation of this data assimilation at USDA FAS is currently nearing completion.
Using a Large Eddy Simulation model with remote sensing to account for variability in air properties and the impact on spatially distributed fluxes. Land surface fluxes and evapotranspiration (ET) are 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 simulates the turbulent two-way exchange of heat, water vapor and momentum (wind energy) between the land surface and lower atmosphere. The LES has been fully coupled with a remote sensing-based land surface model and applied to agricultural study regions in the Texas High Plains, Southern Great Plains, and the Desert Southwest. Analyses over several landscapes indicate surface contrasts in temperature, canopy cover and moisture have a significant affect on surface-air coupling and the resulting spatial distribution of near-surface air properties (wind speed, air temperature and humidity). This has a significant impact on flux computations and ET over the landscape. Atmospheric properties were derived under four typical surface and atmospheric conditions over agricultural landscapes to evaluate the impact on flux estimation. It was found that for irrigated crops and mature stressed crops had the largest deviations (> 30%) in the estimation of area averaged heat fluxes and ET. Therefore, spatial variability of air properties needs to be considered for reliable ET estimation, particularly irrigated areas where accurate assessment of water use is critical for water resource management.
Development of an all-weather satellite monitoring system for vegetation. Information on global vegetation conditions is important in agricultural assessments and yield forecasts. Conventional vegetation monitoring is performed using indices that are developed using satellite-based visible-infrared sensors. These indices can be limited by the presence of clouds and can be observed only during the day. Many regions of the world are plagued with almost constant cloud cover. Microwave observations allow all weather monitoring. A new set of microwave vegetation indices was developed that uses operational satellite observations. Results were compared with conventional vegetation indices and analyses indicated that the microwave vegetation indices can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy, in many cases the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring agricultural crops at global scales and seasonal phenology from space. This information would be very useful for improving the timeliness and reliability of crop condition assessments and yield forecasts by the USDA-Foreign Agricultural Service and other agencies for the U.S. and world-wide.
Successful completion of the Soil Moisture Active Passive Validation Experiment 2008. As a part of the Soil Moisture Active Passive satellite mission, a validation experiment was designed and conducted to collect a data set for use in the development of active passive microwave remote sensing algorithms. This experiment took place on the Delmarva Peninsula in Maryland and Delaware during October 2008. Land cover and vegetation water content information was collected and used to generate critical parameters for modeling and interpreting the numerous remote sensing datasets. The dominate land cover was soybean and corn fields with a significant amount of forested areas. This work will lead ultimately to a better understanding of scaling mixed agricultural landscapes up to the remote sensing footprint.
Spatial and Temporal Field-Scale Chemical Transport Determined. A field site had been characterized with soil cores, ground-penetrating radar (GPR), water table height observations, and other geo-physical tools. Field-scale chemical transport through soil was investigated by applying three mobile solute tracers (chloride and two unique florobenzoic acid tracers) to a 100 m2 area and subsequently observing transit times to 12 observations wells that were in two transects, each transect perpendicular to the down-slope gradient from the treated area. Chemical transit times to the wells varied dramatically, from several days to several months. The shortest (fastest) travel times occurred in wells that were located closest to GPR-identified subsurface flow pathways and were dominated by preferential flow. On the other hand, chemical transit time to wells that occurred on the order of months was dominated by matrix flow and were located far from GPR-identified flow pathways. All chemical breakthrough curve data indicate that at least some preferential flow was present and will need to be incorporated into the field-scale chemical transport models. Furthermore, this research demonstrates the complex nature of field scale chemical transport and the need for model abstraction techniques as no single model available today will adequately simulate field observations.
Application of data assimilation tools to the Soil Water Assessment Tool. Within the past decade, a large number of high efficient data assimilation tools have been developed for the optimal integration of remote sensing observations into land surface and hydrology models. However, to date these tools have not been applied to model applied to natural resource assessment and conservation problems (e.g. water quality monitoring). This lack of integration between water quality modeling and state-of-the-art data assimilation procedures could potentially retard the effective integration of remote-sensing measurement in water quality models. In order to bridge this gap, a simple data assimilation system has been designed and applied to the water balance portion of the Soil Water Assessment Tool (SWAT) model. The system is designed to update SWAT soil water predictions based on coincident microwave remote sensing retrievals of surface soil moisture. Preliminary testing of the approach within the Fort Cobb basin in Western Oklahoma has been aimed at examining the degree to within the system can improve the prediction of surface runoff and therefore the watershed-scale transport of pathogens, sediment and nutrients to the stream channel system.
Estimates of surface soil moisture derived from satellite observations have value for a range of agricultural applications including: drought monitoring, crop yield forecasting, and long-term precipitation forecasting using numerical weather prediction models. Unfortunately, a severe lack of accurate ground-based soil moisture observations has hindered the thorough testing of surface soil moisture retrieval algorithms. Without such testing, it is very difficult to objectively compare two competing retrieval approaches and learn what approach works best. This uncertainty has - in turn - limited the development of key agricultural applications for this data. To address this need a novel evaluation approach (based on a data assimilation mathematical framework and ancillary rainfall observations) has been developed that allows for the objective inter-comparison of competing soil moisture retrieval strategies over a much wider spatial and temporal domain than was previously possible. Comparison of the approach with satellite soil moisture validation results in isolated areas of the United States with extremely dense ground-based soil moisture sampling has demonstrated that it can accurately mimic validation results obtained in these highly data-rich sites, and can therefore be widely applied with confidence. Preliminary global-scale evaluation of remotely-sensed soil moisture products obtained from the Advance Microwave Scanning Radiometer (AMSR-E) sensor aboard the NASA Aqua satellite has been completed. Global application of the approach will also make easier to objectively evaluated remotely-sensed surface soil moisture products obtained from the upcoming NASA Soil Moisture Active/Passive (SMAP) mission and aid in the development of key water-resource applications for SMAP observations.
A drought monitoring product for the contiguous United States. Satellite-based drought indices can be used to supplement coarser resolution data from weather and precipitation networks to assess drought conditions across the U.S. Because land-surface temperature (LST) 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 GOES (Geostationary Operational Environmental Satellite) TIR imagery, a fully automated inverse model of Atmosphere-Land Exchange (ALEXI) has been used to model hourly evapotranspiration (ET) and surface moisture stress over a 10-km resolution grid covering the contiguous United States. From these ET estimates, we have developed and evaluated an Evaporative Stress Index (ESI), given by 1-ET/PET, where ET is the actual evapotranspiration determined by ALEXI. We have demonstrated that this thermal-based stress index shows good correspondence with standard drought metrics and with patterns of antecedent precipitation, but at significantly higher spatial resolution due to limited reliance on ground observations. The TIR inputs detect drought conditions even under the dense forest cover along the East Coast of the U.S., where microwave soil moisture retrievals typically lose sensitivity. This satellite-based drought product will be distributed to the drought community (e.g., the National Drought Mitigation Center) for use in generating weekly U.S. Drought Monitor Reports.
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Anderson, M.C., Kustas, W.P. 2008. Thermal remote sensing of drought and evapotranspiration. EOS Transactions, American Geophysical Union. 89(26):233-234.
Jackson, T.J., Bindlish, R., Cosh, M.H. 2009. Validation of AMSR-E soil moisture products using in situ observations. Journal of the Remote Sensing Society of Japan. 29:263-270.
Crow, W.T., Huffman, G.J., Bindlish, R., Jackson, T.J. 2009. Improving satellite-based rainfall estimates over land using spaceborne surface soil moisture retrievals. Journal of Hydrometeorology. 10(1):199-212.
Jackson, T.J. 2008. Passive microwave remote sensing for land applications. In: Liang, S., editor. Advances in Land Remote Sensing: System, Modeling, Inversion and Application. New York, NY: Springer. p. 9-18.
McCarty, G.W., McConnell, L.L., Sadeghi, A.M., Hapeman, C.J., Graff, C., Hively, W.D., Lang, M.W., Fisher, T.R., Jordan, T., Rice, C., Whitall, D., Lynn, A., Keppler, J., Fogel, M.L. 2008. Overview of the Choptank River watershed conservation effectiveness assessment project. Journal of Soil and Water Conservation. 63:461-474.
Wang, P., Sadeghi, A., Linker, L., Arnold, J., Shenk, G., Wu, J. 2008. Simulated soil water content effect on plant nitrogen uptake and export for watershed management. In: Ma, L., Ahuja, L.R., Brusselma, T., editors. Quantifying and Understanding Plant Nitrogen Uptake for Systems Modeling. Boca Raton, FL: Taylor & Francis Group, LLC. p. 277-304.
Bertoldi, G., Kustas, W.P., Albertson, J.D. 2008. Estimating spatial veriability in atmospheric properties over remotely sensed land-surface conditions. Journal of Applied Meteorology. 47:2147-2165.
Serbin, G., Daughtry, C.S., Hunt, E.R., Reeves, J.B. 2009. Effects of soil composition and mineralogy on remote sensing of crop residue cover. Remote Sensing of Environment. 113:224-238.
Guber, A.K., Gish, T.J., Pachepsky, Y.A., Van Genuchten, M.T., Daughtry, C.S., Nicholson, T., Cady, R. 2008. Temporal stability of estimated soil water flux patterns across agricultural fields. International Agrophysics. 22:209-214.
Bolten, J.D., Crow, W.T., Zhan, X., Reynolds, C.A., Jackson, T.J. 2009. Assimilation of a satellite-based soil moisture product into a two-layer water balance model for a global crop production decision support system. In: Pard, S.K., editor. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. London, United Kingdom: Springer-Verlang. p. 449-464.
Liang, S., Schaepman, M., Jackson, T.J., Jupp, d., Li., X., Liu, J., Liu, R., Strahler, A., Townshend, T., Wickland, D. 2008. Emerging issues in land remote sensing. In: Liang, S., editor. Advances in Land Remote Sensing: System, Modeling, Inversion and Application. New York, NY: Springer. p. 485-494.
Shi, J., Jackson, T.J., Tao, J., Du, J., Bindlish, R., Lu, L., Chen, K.S. 2008. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sensing of Environment. 112:4285-4300.
Choi, M., Jacobs, J.M., Kustas, W.P. 2008. Assessment of clear and cloudy sky parameterizations from daily downwelling longwave radiation over different land surfaces in Florida, USA. Geophysical Research Letters. 35:L20402.
Codling, E.E., Faucette, L.B., Cardoso-Gendreau, F.A., Sadeghi, A.M., Pachepsky, Y.A., Shelton, D.R. 2009. Storm Water Pollution Removal Performance of Compost Filter Socks. Journal of Soil and Water Conservation. 38:1233-1239.
Famiglietti, J.S., Ryu, D., Berg, A.A., Rodell, M., Jackson, T.J. 2008. Field observations of soil moisture variability across scales. Water Resources Research. 44: W01423. http://dx.doi.org/10.1029/2006WR005804.
Kumar, S.V., Reichle, R.H., Peters-Lidard, C.D., Koster, R.D., Zhan, X., Crow, W.T., Eylander, J.B., Houser, P.R. 2008. A land surface data assimilation framework using the land information system: Description and application. Advances in Water Resources. 31:1419-1432.
Merlin, O., Walker, J.P., Kalma, J.D., Kim, E.J., Hacker, J., Panciera, R., Young, R., Summerell, G., Hornbuckle, J., Hafeez, M., Jackson, T.J. 2008. The NAFE'06 data set: Towards soil moisture retrieval at intermediate resolution. Advances in Water Resources. 31:1444-1455.