2006 Annual Report 1.What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? Why does it matter? Agriculture must be able to monitor and model land-atmosphere processes in order to respond, plan, and predict for relatively short term hydrologic extremes (floods and droughts) as well as more persistent climate events as a result of global warming. The key in developing such capabilities is understanding the hydrologic cycle and its connections to the energy and the carbon balance. Shifts in the spatial and temporal distributions of water will have dramatic impacts on energy and carbon budgets, which in turn affect weather on daily, seasonal, annual, and longer time scales. One of the key advances in integrated earth system sciences is the realization that predicting changes of the hydrologic cycle resulting from climatic change requires understanding the effects of climatic change on other biogeochemical cycles. Changes in the carbon and nitrogen cycle will affect vegetation biomass (leaf area index), which in turn will affect the rates of land-surface transpiration and evaporation. Furthermore, organic carbon sequestered in the soil will affect the soils' water holding capacity, which in turn increases the potential productivity of an area. Understanding the interaction of the land surface and atmospheric circulation is a complex problem due to the high degree of variability in landscape properties. Observing technologies, both ground and remote sensing, must be developed and implemented in conjunction with appropriate land-atmosphere models in order to understand surface-air state coupling. Each approach offers unique capabilities that should be optimized in seeking a global product. This requires methodologies for scaling up our understanding of point level physics to watershed, regional and ultimately global scales. The main objective of this research project is to develop process-based land surface algorithms and models using remote sensing technology and evaluate their utility for mapping surface states (i.e., soil moisture, surface temperature, vegetation cover, landscape roughness, soil erosion distribution, etc.) and water, energy and carbon fluxes from field and farm to watershed, regional and ultimately global scales. Specific objectives include: 1.1. Develop soil moisture retrieval algorithms for satellite sensors. 1.2. Evaluate surface temperature/emissivity retrieval algorithm using ASTER satellite data. 1.3. Evaluate techniques to use airborne lidar data to measure land surface parameters. 1.4. Evaluate techniques for integrating remotely sensed crop parameters in crop yield models. 2.1. Investigate the effects of aggregation/ disaggregation methods using remote sensing. 2.2. Evaluate energy balance models using multifrequency remote sensing data. 2.3. Develop drought assessment from remote sensing and models. 3. Investigate land surface-atmosphere coupling and feedbacks using LES-remote sensing modeling framework. 4.1. Combining micrometeorological fluxes with remotely sensed vegetation indices. 4.2. Assessment of soil carbon sequestration in crop lands. To address these objectives ground-truth data covering a range of scales from point to field (for evaluating the processes), to watershed and regional scales (to explore scaling relationships) commensurate with remotely sensed observations that cover a similar range in spatial scales are being collected and processed. In addition, models evaluating exchange rates or fluxes of water, energy and carbon across the land-atmosphere interface that vary in complexity will be developed depending on the objectives, spatial and temporal scales, and the availability of input data. An array of such physically-based remote sensing models will be implemented for testing remote sensing algorithms and aggregation/ disaggregation techniques, while a Large Eddy Simulation model simulating atmospheric turbulent processes will be linked to remotely sensed boundary conditions for evaluating the feedbacks between surface states and the atmosphere. For quantifying regional scale carbon budgets from daily to seasonal time scales, the utility of simple schemes will be explored given the limited data available for more sophisticated approaches. This will involve integrating measurements of carbon exchange into regional models of ecosystem processes, which are driven by remotely sensed vegetation indices. 2.List by year the currently approved milestones (indicators of research progress) FY2002 Milestones 1. Implement physical models in a retrieval algorithm for the TMI and AMSR satellite instruments (Objective 1.1); 2. Collect ground truth data from study/validation sites under different climatic and landscape conditions ((Objective 1.2); 3. Develop algorithms for measuring landscape roughness and vegetation cover with lidar (Objective 1.3); 4. Develop algorithms for assessing crop growth and development from new satellite sensors (Objective 1.4); 5. Develop mathematical tools and procedures for aggregation and disaggregation of landscape properties (Objective 2.1); 6.Create spatial data sets combining remote sensing, meteorological and ground truth data for the different experimental sites to perform model validation (Objective 2.2); 7. Review models that can be integrated with remote sensing data (Objective 2.3).Create spatial data sets combining remote sensing, meteorological and ground truth data for the different experimental sites to perform model validation (Objective 3); 8. Analysis of flux data for different regions and different years for interannual variability (Objective 4.1); 9. Development of a data base of parameters for soils, crops and meteorological inputs for models (Objective 4.2). FY2004 Milestones 1. Validate soil moisture algorithms using insitu and aircraft observations and implement results by refining algorithms (Objective 1.1); 2. Process ASTER data and evaluate algorithms with ground truth data and make necessary revisions and refinements to the algorithms (Objective 1.2); 3. Evaluate lidar algorithms with ground measurements and as input to land surface models (Objective 1.3); 3. Evaluate the integration of biophysical parameters retrieved from remotely sensed data in crop yield models (Objective 1.4); 4. Evaluate affects of scale using remote sensing and ground truth data from field experiments (Objective 2.1); 5. Run the assorted model versions that use different surface schemes and remotely sensed boundary conditions and compare to surface energy balance observations (Objective 2.2); 6. Set up study areas with collaborators and complete evaluation of methods (Objective 2.3); 7. Evaluate LES-remote sensing model simulations of the heat fluxes and mean atmospheric properties using observations. Make necessary revisions to LES land surface scheme to achieve satisfactory agreement with the observations (Objective 3); 8. Ingest of gridded climate layers and remotely sensed data; analysis of FPAR/NDVI relationships for shrublands and grasslands (Objective 4.1); 9. Set up the EPIC-CENTURY model for application in the crop areas of the US Midwest. Evaluate remote sensing techniques for scaling up soil carbon sequestration (Objective 4.2). Note: For FY2006+ milestones for original Objective 1.2 and revised Objective 1.2 (Evaluate Landsat TM and ASTER visible and near-infrared spectral bands) have been fully or substantially met. This coupled with retirement of an SY (Tom Schmugge) has resulted in no new milestones being proposed. For FY2005+ milestones for Objective 1.3 have been fully met, and no new milestones related to lidar technologies will be proposed. For FY2005+ Objective 4.1 has been modified because SY substantially met the 2004-2006 milestones. Revised Objective 4.1: Estimation of vegetation water content from remote sensing. FY2005 Milestones 1. Develop and demonstrate new technologies for achieving higher resolution and improved accuracy soil moisture mapping (Objective 1.1); 2. Evaluate algorithms with ground truth data from field sites and assess uncertainty between TM and ASTER (Objective 1.2); 3. Apply newly developed technologies for regional crop yield assessment (Objective 1.4); 4. Develop methodologies using remote sensing that account for effects of sensor resolution (Objective 2.1); 5. Evaluate sensitivity of the various models to errors in remote sensing and ancillary data inputs. Assess uncertainty in energy balance estimation due differences in model surface schemes (Objective 2.2); 6. Optimize parameters, for minimum data inputs (Objective 2.3); 7. Increase the variance in key spatial inputs over the landscape (i.e, surface temperature) and evaluate the impact on surface-atmosphere coupling and exchange rates and mean air properties (Objective 3); 8. Estimation of vegetation water content from remote sensing using shortwave infrared wavelengths for AVIRIS sensor. (Objective 4.1); 9. Assessment of baseline soil carbon sequestered in crop land and develop scenarios for optimizing sequestration (Objective 4.2). FY2006 Milestones 1. Provide a robust soil moisture retrieval algorithm to NASA and international agencies and a long term record of soil moisture to user groups (Objective 1.1); 2. Technology transfer for operational use by FAS and NASS (Objective 1.4); 3. Apply aggregation/ disaggregation techniques using operational satellite data and evaluate procedures at selected validation sites (Objective 2.1); 4. Develop software codes that can permit the models to be implemented with the NASA Terra and other satellite systems for monitoring regional energy and water balance (Objective 2.2); 5. Complete validation and transfer technology to user community (Objective 2.3); 6. Derive surface variability indicators that can be estimated from remote sensing that indicate possible thresholds of surface contrast initiating secondary circulations in transporting fluxes (Objective 3); 7. Estimation of vegetation water content from remote sensing using shortwave infrared wavelengths for MODIS sensor(Objective 4.1); 8. Transfer of technology for assessment of regional soil carbon sequestration for current and alternate management practices (Objective 4.2). 4a.List the single most significant research accomplishment during FY 2006. Monitoring Drought at Continental Scales with Geostationary Satellites. Data from the Geostationary Operational Environmental Satellites (GOES), operated by NOAA to support weather forecasting, were used in a model to map evapotranspiration and moisture stress across the continental United States for 2002-2004. The resulting stress maps correlate well with maps of antecedent precipitation and with standard Palmer drought indices. However, these GOES-based stress maps are at significantly higher spatial resolution (5-10km) and do not depend on precipitation data, making this technology promising for developing countries lacking intensive precipitation measurement networks. The algorithm and data ingestion infrastructure has been automated, paving the way for daily operational moisture stress assessments across the U.S., which could be incorporated into the national drought monitoring program. This project is identified with National Program 201 Component Agricultural Watershed Management and Irrigation and Drainage Management; research problem areas Watershed Management, Water Availability, and Ecosystem Restoration and Irrigation Water Management and Security. 4b.List other significant research accomplishment(s), if any. Operational Crop Yield Assessment Tool for the U.S. Cornbelt. The National Agricultural Statistics Service (NASS) is responsible for developing the domestic crop production estimates, and produces a crop classification map for a major portion of the U.S. Corn Belt. Research lead to developing of operational algorithms for crop yield estimates using MODIS imagery instead of Landsat used by NASS which reduces the opportunity to acquire cloud free imagery. The limitation of sensor resolution (250 m) to some extent is compensated by the frequency of daily coverage. The automated algorithm tracks changes in the normalized difference vegetation index for specific periods during the crop season to develop the crop-specific classification. This algorithm was tested for the 2005 growing season in Iowa with an 85 % level of accuracy compared with the NASS Landsat classification. Results suggest that this classification algorithm using MODIS imagery can potentially be used for operational assessment of crop yields for NASS. This project is identified with National Program 201 Component Irrigation and Drainage Management; research problem area Irrigation Water Management and Security. Designing the Next Generation of Operational Satellites for Soil Moisture Mapping. Soil Moisture Experiments 2005 were conducted to address algorithm development and validation related to current and planned soil moisture satellite systems. Major activities are underway throughout the world to develop operational soil moisture remote sensing, integrate such measurements with conventional methods and models, and to understand the role of this variable in weather and climate, carbon cycling, as well as global security. The next generation sensor called the Conical Scanning Microwave Imager/Sounder (CMIS) has unique fully polarimetric capabilities, which may help in soil moisture algorithm design. In SMEX05, a series of controlled aircraft based experiments were conducted in conjunction with ground and satellite observations to understand the polarimetric response of soil moisture. Soil moisture is a lynchpin environmental variable in environment assessment and prediction. Timely and effective knowledge of soil moisture at regional spatial scales is critical to improved weather and climate forecasting. This project is identified with National Program 201 Component Agricultural Watershed Management; research problem area Watershed Management, Water Availability, and Ecosystem Restoration. Developed and demonstrated data assimilation approach for the integration of thermal-based remote sensing into a water balance model. Water balance models typically attempt to observed precipitation into runoff, infiltration, evapotranspiration, drainage and change of soil column storage components. These balance calculations - typically found at the core of water quality, irrigation scheduling, drought monitoring, and rainfall/runoff models - are inherently uncertain due to the impact of incorrect model forcings, parameter values, and inadequate physical representations of hydrologic processes. However, if properly exploited, remote sensing observations provide a valuable additional constraint for water balance calculations, which may improve their accuracy and utility for agricultural applications. In 2006, research demonstrated the potential of improving the performance of a water balance model through the assimilation of independent estimates of root-zone soil water content obtained from thermal remote sensing. Results suggest a potential role of thermal remote sensing in efforts to improve water quality and quantity modeling within agricultural watersheds. Follow up research will attempt to harness this potential. This project is identified with National Program 201 Component Agricultural Watershed Management; research problem area Watershed Management, Water Availability, and Ecosystem Restoration. Investigating the Effect of Soil Moisture and Vegetation Moisture on Polarimetric Signals. A large scale field experiment, the Soil Moisture Experiment in 2005 (SMEX05)/Polarimetry Land Experiment (POLEX), was conducted in central Iowa to investigate the effect of various land surface parameters on the polarimetric information from a microwave radiometer. This work impacts the interpretation and understanding of polarimetric information from satellites such as Windsat and CMIS, as well as the interpretation of land surface parameters for the SMOS, Aqua, and future NPOESS satellites. This project is identified with National Program 201 Component Agricultural Watershed Management; research problem area Watershed Management, Water Availability, and Ecosystem Restoration. Comparing ground (ASD), aircraft (MASTER) and Satellite (ASTER) reflectance data for assessing desert vegetation communities. ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), MASTER (MODIS/ASTER airborne simulator), and ASD (Analytical Spectral Devices Spectroradiometer-ground based) reflectance measurements were compared for the 21 MASTER and 9 ASTER visible and near infrared bandwidths for three dominate vegetation communities (grass, transition (grass to shrub), and shrub-mesquite) for a semiarid rangeland. A strong positive correlation with a slope near one between the measurements indicated that the three sensors were measuring similar absolute values from the three vegetation communities. Reflectance was highest from the shrub community with large areas of exposed soil and lowest from the grass community with the shrub-grass transition being intermediate. This has implications for the energy and water budgets of the Jornada where shrub communities are invading and replacing grass communities. This project is identified with National Program 201 Component Agricultural Watershed Management; research problem area Watershed Management, Water Availability, and Ecosystem Restoration. Leaf spectral reflectances used to develop potential algorithms for retrieval of vegetation water content from satellites. Field work was conducted during the Soil Moisture Experiments 2004 and 2005 in Arizona, Mexico (SMEX04) and Iowa (SMEX05), for comparison with satellite and airborne data in order to test these algorithms. There is no significant difference in the relationships during SMEX04 and SMEX05 between the vegetation water content and the normalized difference infrared index, which uses bands in the shortwave infrared region. Having a single, robust relationship is promising for the development of a operational algorithm for determining vegetation water content. With independent estimates of vegetation water content, soil moisture estimates from microwave remote sensing will be more accurate, and potentially crop water stress could be monitored globally. This project is identified with National Program 201 Component Agricultural Watershed Management; research problem area Watershed Management, Water Availability, and Ecosystem Restoration. 4c.List significant activities that support special target populations. None 4d.Progress report. Research results from the SMEX02 and SMACEX studies conducted in near Ames, Iowa, a corn and soybean production region have been published as a Special Issue in the Journal of Hydrometerology. The unique data consisting of in-situ and aircraft measurements of atmospheric, vegetation and soil properties and fluxes allows for detailed and rigorous analysis and validation of surface states and fluxes diagnosed using remote sensing methods at various scales. Research results presented in the special issue have illuminated on the potential of satellite remote sensing algorithms for soil moisture retrieval, land surface flux estimation, and in the assimilation of surface states and diagnostically modeled fluxes into prognostic land surface models. Results of this research will lead to a greater understanding of the role of soil moisture and vegetation conditions on planetary boundary layer dynamics for this region, and an assessment of the utility of remote sensing data for improving coupled land-atmosphere models. This in turn will lead to more reliable weather forecasting and regional climate predictions. More specifically, the data and research results will assist in substantiating a growing body of evidence from model simulations that suggest agricultural practices can modify local and regional climate. 5.Describe the major accomplishments to date and their predicted or actual impact. These project accomplishments address prior National Program (NP) action plans in NP201- Water Quality and Management and NP204-Global Change as well as the new NP201 Component Agricultural Watershed Management, Irrigation and Drainage Management and Water Quality Protection and Management. ARS research initiatives include the development of means for managing and conserving the nation's soil and water resources for a stable and productive agriculture. Critical to this problem is assessment and monitoring of these resources at scales ranging from individual farms to the entire U.S. It is recognized that the current methods that are available for assessments are slow, expensive, and often inadequate. Remote sensing and process-based models developed to use this technology have the potential to improve the information available for these assessments because this data/modeling framework can provide information for assessing soil, water, carbon, plant-quality, and quantity over large areas rapidly and cost-effectively. Significant progress has been made in the areas of soil moisture, evapotranspiration, carbon sequestration and landscape parameter measurement and assessment. New techniques were developed that may facilitate the wider implementation of satellite data in research and applications. The new scientific knowledge and results are being used by researchers in other government agencies, namely NASA, NOAA and USGS. Some of the algorithms and techniques are being adopted operationally by NOAA, FAS, NASS and NRCS. Analyses of microwave aircraft and satellite data from large scale field experiments (Monsoon90, SGP97, SGP99, SMEX02, SMEX03, SMEX04 and SMEX05) covering a wide variety of landscapes demonstrated the potential for large area soil moisture assessment, and are now being implemented with the new generations of satellites (AMSR and ADEOS-II). The impact of this research is the development of a global soil moisture monitoring system for improved weather and hydrologic forecasts. This accomplishment directly contributes to the milestones under objective 1.1 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Validation of large-scale (> 50-km) soil moisture retrievals from spaceborne sensors remains a serious challenge for improving the accuracy of global soil moisture monitoring systems. A high-resolution (90-m) land surface modeling of the SMEX02 domain was used to develop strategies for upscaling local in situ soil moisture observations to spaceborne footprint scales using distributed hydrologic modeling. The impact of this research is better representation of the large scale hydrologic processes. This accomplishment directly contributes to the milestones under objectives 1.1 and 2.1 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Research has demonstrated that the assimilation of remotely sensed soil moisture (from the NASA TRMM satellite) into simple water balance models can improve the model’s ability to forecast runoff from subsequent storm events. Knowledge of soil moisture conditions provides an important source of skill for short-term hydrologic forecasting of runoff and forecasting. However, operational efforts to exploit this skill are typically hampered by a lack of reliable soil moisture information. This research demonstrated that, if properly assimilated into land surface models, remotely sensed soil moisture can address observational shortcomings that currently hamper our ability to track and forecast the flow of water through agricultural landscapes. This accomplishment directly contributes to the milestones under objectives 1.1 and 2.1 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. A multi-scale soil-vegetation-energy-water balance regional modeling framework has been developed which can use remotely sensed surface temperature from operational weather satellite to predict vegetation and soil water use and stress without the need of air temperature observations and that minimizes measurement errors. In addition, this regional output can be disaggregated to field scale with high resolution satellite remote sensing data. This framework is being implemented in a climate forecasting model and being integrated as part of NASA's Earth Observing System EOS-Terra/Aqua satellite data products. This research will improve weather forecasts and crop yield predictions critical to maintaining agricultural competitiveness. This accomplishment directly contributes to the milestones under objectives 1.2, 2.1,and 2.2 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. High resolution flux maps generated by a multi-resolution remote sensing-based energy balance model are used to evaluate the flux distribution and spatial correlations with land surface flux variability measured from a tower-based network and aircraft transects. This analysis indicates that a network of tower observations will not typically represent the flux distribution over a landscape and that the source-area for heat and water vapor differ significantly for the aircraft-based measurements. These research findings have major implications in interpreting and validating land surface model output with tower and aircraft measurements. Improved capabilities in identifying the area of landscape influencing flux observations will lead to the development of more robust land surface schemes used in hydrologic modeling and weather and climate forecasting. This in turn will lead to more accurate water and agricultural management decision tools. This accomplishment directly contributes to the milestones under objectives 1.2, 2.1,and 2.2 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Airborne laser altimeters have been shown to provide measurements of landscape roughness over large areas quickly and easily that will improve our ability to manage natural and agricultural landscapes. The algorithms are now available for operational implementation by private and government agencies. The result will be the production of more reliable roughness maps over different landscapes for improved modeling of ecosystem health. This accomplishment directly contributes to the milestones under objective 1.3 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Remote sensing field experiments were conducted by the USDA-ARS Hydrology and Remote Sensing Laboratory in collaboration with other ARS facilities involving the collection of remotely sensed data over arid, semiarid, subhumid, and humid climates. Satellite observations have potential in providing information critical in monitoring and modeling the impact of land use changes on water, energy and carbon fluxes at regional scales for natural and agricultural ecosystems. The remotely sensed data from the different platforms were correlated to variations in vegetation cover and condition and energy balance models were used for computing surface fluxes. This research will lead better understanding of the relationships between vegetation patterns, spectral properties, and energy exchange rates affecting vegetation-climate interactions. This accomplishment directly contributes to the milestones under objectives 1.2, 2.1, 2.2 and 2.3 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. A Large Eddy Simulation model has been extended to work with remotely sensed data. The Large Eddy Simulation (LES) model was used for investigating the effect of land surface heterogeneity on land surface-atmosphere coupling. An understanding of the coupling between land surface and atmosphere is essential for understanding the degree to which vegetation changes affect local and regional climate and weather. A suite of LES simulations over surface fields obtained from recent field experiments have begun. Preliminary analyses from the simulations indicate the strength of surface contrasts has a significant affect on surface-air coupling resulting in potentially large errors in flux computations that assume uniform atmospheric forcing. This research enables a greater understanding of the effects of land cover heterogeneity on regional scale land-atmosphere flux interactions. This accomplishment directly contributes to the milestones under objective 3 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. The performance of data assimilation techniques was examined over a range of land covers within the south-central United States to assess their robustness and value relative to existing approaches. Data assimilation approaches are powerful mathematical tools being used in climate modeling for more accurate weather forecasts. They are now being utilized in hydrologic models for improving predictions and have recently been developed to utilize remotely sensed surface temperature observations to solve the surface energy balance and predict surface energy fluxes. The impact of this research is more accurate forecast models. This accomplishment directly contributes to the milestones under objective 2.2 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nation’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Evaluated the potential use of the EPIC- Century biogeochemical model for simulating the accumulation of organic carbon in the soil under various soil and crop management practices. Estimation of regional carbon storage in the soil is critical assessing agricultural impact on the global carbon budget and potential consequences on global climate change. A data-base was set up to run the EPIC-Century model in central Iowa and south western Mali. Preliminary runs have been completed and the database is being populated with additional detailed soil and land cover information required for model simulations. The impact of this research is that it could be set up to predict the rate of changes in soil carbon with different tillage and residue management practices at regional scales. This accomplishment directly contributes to the milestones under objective 4.2 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nations’ Natural Resources Base and Environment and the ARS National Programs 201 and 204. Remote sensing data was used to map soil carbon sequestration at regional scales. The model simulations were conducted for a period of 50 years beginning in 1970, using historic databases on management practices and modeling potential sequestration rates for scenarios that optimize soil management practices. Optimizing soil and crop management practices is an important goal for NRCS and USDA as an agency interested in minimizing soil erosion, improving soil quality and mitigating atmospheric carbon dioxide while improving crop production. Modeling soil carbon sequestration at three levels of tillage practices that included residue management was conducted over a selected area (50 x 100 km) in central Iowa. The NASA – Carbon Management Program, funded the project and the laboratories involved in this project are the Hydrology and Remote Sensing Laboratory, Beltsville, MD and the Soil Tilth Laboratory, Ames, IA. The results were presented to the NRI (National Resources Inventory) division of NRCS who will collaborate in developing a decision support system for optimizing soil management at the National Level. This accomplishment directly contributes to the milestones under objective 4.2 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nations’ Natural Resources Base and Environment and the ARS National Programs 201 and 204. Flux data were combined for two sites in Southeastern Wyoming with AVHRR NDVI data to estimate primary production. Management of natural resources for sustainability requires accurate estimates of vegetation production. These data were extrapoltaed for rangelands over the entire state of Wyoming and compared the data, 1x1 km pixel, with estimates of productivity from USDA NRCS geographic information layers. Productivity can be used to estimate the allowable grazing by livestock, so this research will lead to better estimates of stocking rates by state and federal agencies. This accomplishment directly contributes to the milestones under objective 4.1 of the project plan which is related to the ARS Strategic Plan Goal #5: Protect and Enhance the Nations’s Natural Resources Base and Environment and the ARS National Programs 201 and 204. Research results from the above accomplishments will lead to the development of technologies that can ultimately provide more timely and cost-effective assessments of soil and water resources. From these assessments, farmers, water managers and researchers should be able to better manage and conserve these resources and develop strategies for managing water supplies for agriculture and municipalities from improved short-term weather forecasts and more reliable longer term climate forecasts. 6.What science and/or technologies have been transferred and to whom? When is the science and/or technology likely to become available to the end-user (industry, farmer, other scientists)? What are the constraints, if known, to the adoption and durability of the technology products? Technologies fall into three categories: data, algorithms and research results. Data collected in field experiments over many years are provided with substantial documentation at a web site (http://hydrolab/). Historic data sets collected at Beltsville in the late 1970s and early 1980s were recovered in a digital format and added to laboratory website access. The data collected in all large scale experiments (SMEX and SMACEX) have been processed and prepared for public dissemination. The channel for this is the NASA Distributed Active Archive Center, which is a public database. This is widely accessed by scientists, technical specialists, students, as well as industry. This effort required extensive documentation to insure transferability. Several significant algorithms for the translation of remotely sensed data to hydrologic variables have been developed. Some of these have been adopted as operational tools by NOAA and others will be implemented in future satellite programs for monitoring evapotranspiration by NASA and Japan. A prototype soil moisture retrieval algorithm for use with satellite sensors launched in 2002 was provided to NASA and the Japanese space agency to produce daily soil moisture products for public distribution. Transferred a spring wheat yield model to PECAD-FAS for operational assessment of crop yields in northern Kazakhstan. The science of sequestering atmospheric carbon dioxide in the soil through increased biomass, residue management and tillage practices is currently being evaluated. This will eventually be transferred to industry and farmers in several years when all aspects of the project are evaluated. The National Agricultural Statistics Service (NASS) produces an annual Landsat crop classification map over most of the crop area in the U.S. mid-west. With the failure and limited functionality of Landsat sensors, there is a critical need for evaluating the use of alternative U.S. satellite sensors for developing crop classifications. The feasibility of potentially using MODIS imagery was tested and presented to the NASS research program. ARS and NASS will evaluate the classification algorithm jointly for the 2006 crop season. Initiated collaborative project with FAS to transfer ARS soil moisture remote sensing and data assimilation technologies into the FAS crop yield forecasting system. Airborne lidar technology is now widely available in the commercial arena for a wide variety of applications, allowing the landscape roughness algorithm to be implemented operationally. 7.List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below). Towards Integrated Global Soil Moisture Observation. GEWEX News. 15(3):8-9, 2005. Advanced Microwave Scanning Radiometer Joint U.S. and Japan Science Team Meeting, Honolulu, HI, September 2005. 9th International Symposium on Physical Measurements, Signatures and Remote Sensing and Panel Member on Remote Sensing Systems, Beijing, China, October 2005. Microrad 2006, San Juan, PR, March 2006. Soil Moisture Mission Working Group, Beltsville, MD, March 2006. International Soil Moisture Workshop, Noordwijk, NL, March 2006. Joint Federal Interagency Hydrologic Modeling Workshop, Reno, NV, April 2006. Soil Moisture Ocean Salinity Mission Workshop, Copenhagen, Denmark, May 2006. Satellite data to aid yield forecasts. by Kay Shipman, FarmWeek, (Illinois Farm Bureau), June 20, 2005, p.11. Presentations on the operational use of MODIS imagery for classification of corn and soybean crop in the U.S. Corn Belt to NASS. MODIS Team Meetings, Baltimore, MD, January 4-6, 2006. Research featured on public interest article posted to NASA.gov website http://www.nasa.gov/centers/goddard/news/topstory/2005/trmm_runoff.html Review Publications Anderson, M.C., Norman, J.M., Kustas, W.P., Li, F., Prueger, J.H., Mecikalski, J.R. 2005. Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX. Journal of Hydrometeorology. 6:892-909. Crow, W.T., Kustas, W.P., Li, F. 2005. Intercomparison of spatially disturbed models for predicting surface energy flux patterns during SMACEX. Journal of Hydrometerorology. 6(6):941-954. Jackson, T.J., Hurkmans, R., Hsu, A., Cosh, M. 2005. Soil moisture algorithm validation using data from the Advanced Microwave Scanning Radiometer (AMSR-E) in Mongolia. Italian Journal of Remote Sensing. 30(31):23-32. Li, F., Kustas, W., Prueger, J.H., Neale, C., Jackson, T. 2005. Utility of remote sensing based two-source energy balance model under low and high vegetation cover conditions. Journal of Hydrometeorology. 6:878-891. Crow, W.T. 2006. Impact of incorrect hydrologic model error assumptions on the sequential assimilation of remotely sensed surface soil moisture. Journal of Hydrometeorology. 8(3):421-431. Jackson, T.J., Bindlish, R., Gasiewski, A.J., Stankov, B., Klein, M., Njoku, E.G., Bosch, D., Coleman, T.L., Laymon, C., Starks, P. 2005. Polarimetric Scanning Radiometer C and X band microwave observations during SMEX03. IEEE Transactions on Geoscience and Remote Sensing. 43:2418-2430. Kustas, W.P., Prueger, J.H., MacPherson, I., Wolde, M., Li, F. 2005. Effects of landuse and meteorological conditions on local and regional momentum transport and roughness for midwestern cropping systems. Journal of Hydrometeorology. 6:825-839. Cosh, M.H., Jackson, T.J., Starks, P.J., Heathman, G. 2006. Temporal stability of surface soil moisture in the Little Washita River Watershed and its applications in satellite soil moisture product validation. Journal of Hydrology. 323(1-4):168-177. Crow, W.T., Bindlish, R., Jackson, T.J. 2005. The added value of spaceborne passive microwave retrievals for runoff ratio forecasting. Geophysical Research Letters. (32)L18401, doi: 10.1029/2005GL023543. Kustas, W.P., Anderson, M.C., French, A.N., Vickers, D. 2006. Using a remote sensing field experiment to investigate flux-footprint relations and flux sampling distributions for tower and aircraft-based observations. Advances in Water Resources. 29:355-368. Hunt, E.R., Miyake, B.A. 2006. Comparison of stocking rates from remote sensing and geospatial data. Rangeland Ecology & Management. 59:11-18. Crow, W.T., Koster, R.D., Reichle, R., Sharif, H. 2005. Relevance of time-varying and time-invariant retrieval error sources on the utility of spaceborne soil moisture. Geophysical Research Letters. (32)l24405, doi: 10.1029/2005GL24889. Li, F., Kustas, W.P., Anderson, M.C., Jackson, T.J., Bindlish, R., Prueger, J.H. 2006. Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape. Remote Sensing of Environment. 101:315-328. Wen, J., Jackson, T., Bindlish, R., Hsu, A., Su, Z. 2005. Retrieval of soil moisture and vegetation water content using SSM/I data over a corn and soybean region. Journal of Hydrometeorology. 6:854-863. Otkin, J., Anderson, M.A., Mecikalski, J.R., Diak, G.R. 2005. Validation of GOES-Based insolation estimates using data from the United States Climate Reference Network. Journal of Hydrometerorology. 6:475-640. Santanello, J.A., Friedl, M.A., Kustas, W.P. 2005. An empirical investigation of convective planetary boundary layer evolution and its relationship with the land surface. Journal of Applied Meteorology. 44:917-932. Chen, D., Huang, J., Jackson, T.J. 2005. Vegetation water content estimation for corn and soybean using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment. 98:225-236. French, A.N., Jacob, F., Anderson, M.C., Kustas, W.P., Timmermans, W., Gieske, A., Su, B., Su, H., McCabe, M.F., Li, F., Prueger, J.H., Brunsell, N. 2005. Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sensing of Environment. 99:55-65. Corrigendum 99:471. Hatfield, J.L. 2005. Review of writing and presenting scientific papers. Crop Science. 45:809. Gao, H., Wood, E.F., Jackson, T.J., Drusch, M., Bindlish, R. 2006. Using TRMM/TMI to retrieve soil moisture over the southern United States from 1998 to 2002. Journal of Hydrometeorology. 7:23-38. Eichinger, W.E., Cooper, D.I., Hipps, L.E., Kustas, W.P., Neale, C.M., Prueger, J.H. 2006. Spatial and temporal variation in evapotranspiration using Raman Lidar. Advances in Water Resources. 29:369¿381. Eichinger, W.E., Holder, H.E., Cooper, D.I., Hipps, L.E., Knight, R., Kustas, W.P., Nichols, J., Prueger, J.H. 2005. Lidar measurement of boundary layer evolution to determine sensible heat fluxes. Journal of Hydrometeorology. 6:840-853. Hanson, P.J., Amthor, J.S., Wullschleger, S.D., Wilson, K.B., Grant, R.F., Hartley, A., Hui, D., Hunt, E.R., Johnson, D.W., Kimball, J.S., King, A.W., Luo, Y., McNulty, S.G., Sun, G., Thornton, P.E., Wang, S.S., Williams, M., Cushman, R.M. 2004. Carbon and water cycle simulations for an upland oak forest using 13 stand-level models: Intermodel comparisons and evaluations against independent measurements. Ecological Monographs. 74(3):443-489. Jackson, T.J. 2005. Hydrological application of remote sensing: Surface states-surface soil moisture. Encyclopedia of Hydrological Sciences. 54:799-810. Ritchie, J.C., Zimba, P.V. 2005. Hydrological application of remote sensing: Water quality suspended sediment and algae. Encyclopedia of Hydrological Sciences, Volume II. London, United Kingdom: John Wiley & Sons. p. 939-949. Jackson, T.J. 2005. Satellite soil moisture remote sensing. In: Aswathanarayana, U., editor. Advances in Water Resources Technologies. A. A. Balkema Publishers. Rotterdam, The Netherlands. p. 91-96. Crow, W.T., Entekahabi, D., Reichle, R., Koster, R., 2006. Multiple spaceborne water cycle observations would aid modeling. Earth Observing Systems (EOS). 87(15). Jackson, T.J., Entekabi, D., van Oevelen, P., Kerr, Y. 2005. Towards intergrated global soil moisture observation. Global Energy and Water Cycle Experiment (GEWEX) News. 15(3):8-9. Crow, W.T.,Entekhabi, D.,O¿Neill P.E., Njoku, E.,Chan, T., Shi, J.C. 2005. An observing system simulation experiment for hydros radiometer-only soil moisture and freeze-thaw products. In: Proceedings of the 2005 International Geoscience and Remote Sensing Symposium, July 25-29, 2005, Seoul, South Korea. 4:2737. Jackson, T.J., Bindlish, R., Cosh, M.H., Gasiewski, A., Stankov, B., Klein, M., Weber, B., Zavorotny, V. 2005. Soil Moisture Experiments 2004 (SMEX04) polarimetric scanning radiometer, AMSR-E and heterogeneous landscapes. In: Proceedings of the International Geoscience and Remote Sensing Symposium, July 25-29, 2005, Seoul, Korea. 2:1114-1117. Crow, W.T., Bindlish, R., Jackson, T.J. 2005. The marginal value of spaceborne passive microwave soil moisture observations for runoff ratio forecasting. In: Proceedings of the ACTIF/Floodman/Floodrelief Conference on International Flood Forecasting, October 17-19, 2005, Tromso, Norway. p.34 Ritchie, J.C., Schmugge, T.J., Rango, A., Schiebe, F.R. 2006. Jornada Experimental Range and Sevilleta LTER: Unique arid rangelands to study changing vegetation cover using remote sensing systems. In: Proceedings of the 14th International Soil Conservation in Semi-Arid Environments, May 14-19, 2006, Marrakech, Morocco. 2006 CDROM. Crow, W.T., Bindlish, R., Jackson, T.J. 2006. Improving hydrological forecasting using spaceborne soil moisture retrievals. In: Proceedings of the 2006 American Meteorological Society Meeting, January 26-February 2, 2006, Atlanta, Georgia. 2006 CDROM. Velde, R., Jackson, T.J., Joseph, A., Hsu, A. 2004. ERS-2 SAR data applied to watershed soil moisture maping with corn and soybeans. In: Proceedings of the 2004 Envisat/ERS Symposium, September 6-10, 2004, Salzburg, Austria. European Space Agency SP-572. Paper No. 4P05-03. 2004 CDROM. Shi, J., Chen, K.S., Kim, Y., Van Zyl, J., Njoku, E., Sun, G., O'Neill, P., Jackson, T.J., Entekhabi, D. 2004. Estimation of soil moisture with L-band multi-polarization radar. In: Proceedings of the International Geoscience and Remote Sensing Symposium, September 20-24, 2004, Anchorage, Alaska. IEEE Cat. No. 04CH37612C, II: 815-818. Ritchie, J.C., Schmugge, T.J., Hsu, A. 2006. Comparison of ASTER, MASTER, Landsat, and ground-based radiance measurements [abstract]. Geophysical Research Abstracts. 8(10460):SRef-ID 1607-7962/gra/EGU06-A-10460. Hunt, E.R., Yilmaz, T., Jackson, T.J. 2006. Vegetation water content from Landsat 5 thematic mapper during the Soil Moisture Experiment 2004 in Arizona and Sonora [abstract]. American Society for Photogrammetry and Remote Sensing Proceedings. 2006 CDROM. Crow, W.T., Kustas, W.P. 2006. Assimilation of thermal remote sensing-based soil moisture proxy into a root-zone water balance model [abstract]. EOS Transactions, American Geophysical Union. 87(36) Joint Meeting Supplement, Abstract, H31A-01. Jackson, T.J., Entekhabi, D., Njoku, E. 2005. The Hydros mission and validation of soil moisture retrievals [abstract]. European Geosciences Union General Assembly. Abstract 7:00434. Ritchie, J.C., Rango, A., Schmugge, T.J. 2006. JORNEX: A long-term experiment to study arid rangelands using remote sensing techniques [abstract]. American Geophysical Union. 87(36):H43B-02. Hsu, A., Jackson, T.J. 2005. Evaluating MODIS vegetation indices for soil moisture retrieval from microwave data [abstract]. EOS Transactions, American Geophysical Union. 86(18), Joint Assembly Supplements, Abstract H33C-05. Hunt, E.R., Ustin, S., Vanderbilt, V., Huete, A. 2006. Soil moisture experiments 2004 and 2005 for evaluation of vegetation water content with MODIS [abstract]. MODIS Team Meetings. 2006 CDROM. Bolten, J.D., Jackson, T.J., Lakshmi, V., Cosh, M.H., Drusch, M. 2005. Long-term evaluation of AMSR-E soil moisture product over the Walnut Gulch Watershed, AZ [abstract]. EOS Transactions, American Geophysical Union. 86(52), Fall Meeting Supplements, Abstract H13J-04. Zhan, X., Kumar, S.V., Crow, W.T., Arsenault, K., Houser, P., Peters-Lidard, C. 2006. Implementation and application of the Kalman Filter data assimilation approaches in NASA's Land Information System Infrastructure [abstract]. EOS Transactions, American Geophysical Union. 87(36), Joint Meeting Supplement, Abstract H31A-01. Lakshmi, V., Jackson, T.J., Njoku, E.G., Bolten, J.D., Guijarro, L.N. 2005. Validation of AMSR-Derived soil moisture: Lessons from SMEX02, SMEX03 and SMEX04 [abstract]. EOS Transactions, American Geophysical Union. 86(52) Fall Meeting Supplements, Abstract H23H-04. Van Oevelen, P., Jackson, T.J., Entekhabi, D., Kerr, Y. 2005. A strategy for a global in-situ soil moisture network [abstract]. Eos Transactions, American Geophysical Union. 86(52), Fall Meeting Supplements, Abstract H11B-1261. Choi, M., Jacobs, J.M., Cosh, M.H., Ray, R.L. 2005. Soil moisture structure for different soil depths from field to watershed scale during the Soil Moisture Experiment 2005 (SMEX05) [abstract]. EOS Transactions, American Geophysical Union. 86(52), Fall meeting Supplements, Abstract H23H-02.