Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes
Hydrology and Remote Sensing Laboratory
2013 Annual Report
1a. Objectives (from AD-416):
Objective 1: Develop and verify new observational tools (both remote sensing- and ground observation-based) and scaling techniques for characterizing water balance components, from plot (~10 m) to regional scales (~100 km). Objective 2: Develop remote sensing and modeling approaches for monitoring the magnitude of agricultural drought and its subsequent impact on agricultural crop condition and yield. Objective 3: Develop remote sensing and modeling approaches for characterizing the multi-scale impacts of conservation practices on water quality variables.
1b. Approach (from AD-416):
Ground measurements, remote sensing observations, and modeling each provide a partial description of hydrologic variables required at different spatial scales for agricultural applications. This project seeks to integrate these various sources of information into true multi-scale assessments and leverage their mutual strengths.
3. Progress Report:
First year progress was dominated by data collection/processing activities and the development of suitable evaluation strategies for benchmarking anticipated future improvements in remote sensing, modeling and data assimilation approaches. Examples include the completion of an extensive validation exercise for satellite-based surface energy flux estimates and a detailed examination of ground-based instrumentation required for the validation of satellite-based surface soil moisture retrievals. In addition, new verification techniques were developed for evaluating remote sensing retrievals over data-poor areas lacking extensive ground instrumentation. These milestones represent important prerequisites for follow-on activities aimed at improving our ability to track the storage and flux of water through agricultural landscapes.
Agricultural drought monitoring is another key component of the project. In particular, there is an emphasis on the development of improved drought-monitoring approaches which integrate visible, near-infrared, thermal, and microwave remote-sensing resources. Such integrative research requires the acquisition and processing of multiple (large-scale) satellite- and ground-based data sets. To address this need, a number of important data acquisition and processing tasks were completed over the past year. Once in place, these data sets will allow us to look for better ways to integrate disparate remote sensing products into a unified drought product.
The same is true for projects focused on improving water quality monitoring. In particular, significant time and resources have been dedicated to collecting datasets required to benchmark expected future improvements in combined modeling/remote sensing approaches for monitoring water quality and the impact of conservation practices within the Chesapeake Bay Watershed.
1. A satellite-based early warning index for flash drought. The flash drought event of 2012 was a rapid onset event fueled both by below-normal precipitation levels and a lingering heat-wave, which served to essentially “bake” moisture reserves from the soil profile. ARS scientists at Beltsville, Maryland have developed a satellite-based drought product called the Evaporative Stress Index (ESI) that provided early warning of deteriorating crop and moisture conditions occurring in 2012 over the heart of the U.S. Cornbelt, preceding signals of increasing drought severity recorded in the U.S. Drought Monitor and many other standard drought indicators by several weeks. The ESI depicts areas of anomalously low water use and availability, derived from measurements of evapotranspiration (ET) generated with thermal infrared satellite imaging systems. Robust early warning of impending drought provides growers additional time to adjust cropping and marketing strategies during the growing season. ARS scientists are also working with researchers at the National Agricultural Statistics Service (NASS) to establish utility of ESI records of seasonal crop stress for improving estimates of at-harvest yield. With minimal reliance on ground-based observations, the ESI shows good potential in global monitoring for food and water security.
2. Using remote sensing to improve tracking of agricultural conservation practices. The planting of winter cover crops is a critical agricultural conservation practice in the Chesapeake Bay watershed, and cover crops have been shown to be effective in reducing nutrient and sediment losses from farmland. However, the success of wide-scale conservation programs to promote winter cover crops has been hampered by the difficulty of tracking winter cover crop performance over large spatial areas. ARS scientists at Beltsville, Maryland have been working in partnership with the United States Geological Survey (USGS) and the Maryland Department of Agriculture (MDA) to map and report winter cover crop performance on working farms. Data integration is used to match satellite-based measurements of biomass, cover crop management, and crop type to evaluate the on-farm cover crop performance at the watershed scale. Results of a five-year analysis of winter cover crop performance in Talbot County, Maryland demonstrate significant improvement in wintertime groundcover linked to increased farmer participation in conservation cost-share programs, and can be used to isolate successful cover cropping strategies. Scaling this work throughout the State of Maryland, and neighboring Chesapeake Bay jurisdictions, will provide information that can be used by farmers and conservationists to support tracking and adaptive management of agricultural conservation practices for water quality protection.
3. Space-based mapping of daily water use at field scales. ARS scientists at Beltsville, Maryland have developed a novel technique for combining information from multiple satellite platforms to map daily water use at fine spatial scales, resolving individual farm fields. While no single satellite system currently in orbit provides both the spatial and temporal resolution required to support farm-level water management, data fusion techniques can be employed to optimally combine the spatial assets of some satellite-based imaging systems (resolving features down to 30 meters in size) with the frequent temporal sampling of other satellites (hourly to daily). Fused multi-sensor estimates of daily evapotranspiration (ET), mapped at sub-field scales, have been compared with data collected in rain-fed and irrigated crops in Iowa, Texas and Nebraska, showing good agreement with observations of daily and seasonal water use. These data fusion techniques have the potential to both supply critical real-time crop stress and water use information needed for farm management and regional yield estimation, and to significantly enhance the value of the existing U.S. satellite sensor fleet through synergistic integration of individual data streams.
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Kustas, W.P., Alfieri, J.G., Anderson, M.C., Colaizzi, P.D., Prueger, J.H., Evett, S.R., Neale, C.M., French, A.N., Hipps, L.E., Chavez, J.L., Copeland, K.S., Howell, T.A. 2012. Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area. Advances in Water Resources. 50:120-133.
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Heathman, G.C., Cosh, M.H., Merwade, V., Han, E. 2012. Multi-scale temporal stability analysis of surface and subsurface soil moisture within the Upper Cedar Creek Watershed, Indiana. Catena. 95:91-103.
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Cammalleri, C., Anderson, M.C., Ciraolo, G., D'Urso, G., Kustas, W.P., La Loggia, G., Minacapilli, M. 2012. Practical applications of the remote sensing-based two-source algorithm for mapping surface energy fluxes without in-situ air temperature observations. Remote Sensing of Environment. 124:502-515.
Chen, F., Crow, W.T., Holmes, T.R. 2012. Improving long-term, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART). Journal of Applied Remote Sensing (JARS). 6(1):603-604.
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Yilmaz, M.T., Crow, W.T. 2013. The optimality of potential rescaling approaches in land data assimilation. Journal of Hydrometeorology. 14:650-660.
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Bolten, J., Crow, W.T. 2012. Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture. Geophysical Research Letters. 39(19):L19406.
Hain, C., Crow, W.T., Anderson, M.C., Mecikalski, J. 2012. An EnKF dual assimilation of thermal-infrared and microwave satellite observations of soil moisture into the Noah land surface model. Water Resources Research. DOI: 10.1029/2011WR011268.
Scanlon, T.M., Kustas, W.P. 2012. Partitioning evapotranspiration using an eddy covariance-based technique: Improved assessment of soil moisture and land-atmosphere exchange dynamics. Vadose Zone Journal. 11:3.
Heathman, G.C., Cosh, M.H., Han, E., Jackson, T.J., McKee, L.G., McAfee, S.J. 2012. Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks. Geoderma. 170:195-205.
Agam, N., Kustas, W.P., Evett, S.R., Colaizzi, P.D., Cosh, M.H., McKee, L.G. 2012. Soil heat flux variability influenced by row direction in irrigated cotton. Advances in Water Resources. 50:31-40.
Evett, S.R., Kustas, W.P., Gowda, P., Anderson, M.C., Prueger, J.H., Howell, T.A. 2012. Overview of the Bushland Evapotranspiration and Agricultural Remote sensing experiment 2008 (BEAREX08): A field experiment evaluating methods for quantifying ET at multiple scales. Advances in Water Resources. 50:4-19. http://dx.doi.org/10.1016/j.advwatres.2012.03.010.
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Nearing, G.S., Crow, W.T., Thorp, K.R., Moran, M.S., Reichle, R., Gupta, H.V. 2012. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resources Research. 48 W05525.
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