Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes
Hydrology and Remote Sensing Laboratory
2012 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:
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. To reach this goal, required research topics include: developing improved observational methods that exploit advances in both ground and satellite measurement methodologies, combining remote sensing retrievals derived from multiple satellite sensors, linking local measurements acquired from ground-based instrumentation to large-scale areal averages, and using remote sensing and modeling to scale-up the impact of local management practices to the watershed scale. Work on year 1 project objectives begun only recently (in February 2012). Nevertheless good progress has been made on preliminary analysis and early data collection tasks required to meet later project milestones.
1. Defining effective strategies for improving global agricultural drought monitoring. Monitoring global agricultural drought requires the accurate estimation of root-zone soil moisture availability in agricultural areas. Such estimates are commonly obtained through water balance modeling based on observations of meteorological variables (e.g., rainfall and air temperature). However, these meteorological observations are not available over large portions of the globe. As a result, international soil moisture predictions obtained from such models are known to be highly inaccurate. In response, a number of different strategies for improving these models have been advanced. These include the integration of satellite soil moisture retrievals into existing models and/or the application of new, more complex models. Unfortunately, little is known about which strategy is more effective. Using a novel model evaluation strategy, recent research has assessed the quality of root-zone soil moisture retrievals obtained from global water balance models and evaluated the impact of both increased model complexity and the assimilation of satellite soil moisture retrievals. Results clearly show that the assimilation of satellite-based soil moisture retrievals is a much more effective strategy for enhancing the quality of model-based soil moisture estimates. These results are highly relevant to multiple federal agencies (e.g., NOAA, USAID, and USDA FAS) currently investing significant resources to improve their capability to globally monitor agricultural drought.
2. Vegetation water content estimated using radar. Vegetation water content is an important biophysical parameter which plays a significant role in the retrieval of soil moisture using microwave remote sensing as well as in forest fire fuel assessment and agricultural yield prediction. In this study, a new remote sensing approach called the Radar Vegetation Index (RVI) was tested for estimating vegetation water content. The analysis utilized a data set obtained with a ground-based radar system through the growth cycle of cultivated rice and soybean. Prediction equations for the estimation of biophysical variables from the RVI were developed. Results indicated that it was possible to estimate vegetation water content with much greater accuracy than other techniques currently used. These results demonstrate that valuable new information can be extracted from current and future radar satellite systems on the vegetation condition of important crop types grown world-wide.
3. Detection of wetland-stream connectivity using LIDAR. The connectivity between wetlands and streams impacts the ecological function and regulatory status of wetlands. However, such connectivity is difficult to assess using existing measurement techniques. In response, we investigated the utility of airborne Light Detection And Ranging (LIDAR) measurements and derived digital elevation models for improved mapping of stream channels and a better assessment of wetland/stream connectivity. The LIDAR observations lead to a substantial improvement in the accuracy of stream maps compared to standard stream map products based on lower resolution elevation data. Assessments of wetland connectivity were greatly altered by this improved accuracy and a general increase in estimated connectivity was found. The geospatial and remote sensing tools developed in this research will improve our ability to effectively regulate wetlands within agricultural landscapes.
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Crow, W.T., Berg, A., Cosh, M.H., Loew, A., Mohanty, B., Panciera, R., De Rosnay, P., Ryu, D., Walker, J. 2012. Upscaling sparse ground-based soil moisture observations for the validation of satellite surface soil moisture products. Review of Geophysics. DOI: 10.1029/2011RG000372.
Chen, F., Crow, W.T., Starks, P.J., Moriasi, D.N. 2011. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Advances in Water Resources. 34:526-536.
Yost, R., McCarty, G.W., Doumbia, M. 2011. Loss on ignition: Measuring soil organic carbon in soils of the Sahel, West Africa. African Journal of Agricultural Research. 5(22):3088-3095.
Brunsell, N., Anderson, M.C. 2011. Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory. Biogeosciences. 8:2269-2280.
Mirrales, D., Crow, W.T., Cosh, M.H. 2010. A technique for estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations. Journal of Hydrometeorology. 11:1423-1429.
Hain, C.R., Crow, W.T., Mecikalski, J.R., Anderson, M.C., Holmes, T.R. 2011. An intercomparison of available soil moisture estimates from thermal-infrared and passive microwave remote sensing and land-surface modeling. Journal of Geophysical Research Atmospheres. 116:D15107.
Kim, Y., Jackson, T.J., Bindlish, R., Lee, H., Hong, S. 2012. Radar vegetation indices for estimating the vegetation water content of rice and soybean. Geoscience and Remote Sensing Letters. 9:564-568.
Holmes, T.R., Jackson, T.J., Reichle, R., Basara, J. 2012. An assessment of numerical weather prediction (NWP) models surface soil temperature products using ground-based measurements. Water Resources Research. DOI: 10.1029/2011WR010538.
Choi, M., Tae, W.K., Kustas, W.P. 2011. Reliable estimation of evapotranspiration on agricultural fields predicted by Priestley-Taylor model using soil moisture data from ground and remote sensing observations compared with Common Land Model. International Journal of Remote Sensing. 32(16):4571-4587.
Tang, R., Jia, Y., Li, C., Sun, X., Kustas, W.P., Anderson, M.C. 2011. An intercomparison of three remote sensing-based energy balance models using large aperture scintillometer measurements over a wheat-corn production region. Remote Sensing of Environment. 115:3187-3202.
Mladenova, I., Lakshmi, V., Jackson, T.J., Walker, J.P., Merlin, O., De Jeu, R.A. 2011. Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006 (NAFE'06). Remote Sensing of Environment. 115:2096-2103.
Brunsell, N., Mechem, D.B., Anderson, M.C. 2011. Surface heterogeneity impacts on boundary layer dynamics via energy balance partitioning. Atmospheric Chemistry and Physics. 11:3403-3416.
Parinussa, R., Holmes, T.R., Crow, W.T. 2011. The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations. Hydrology and Earth System Sciences. 15(10):3135-3151.
Liu, Q., Reichle, R., Bindlish, R., Cosh, M.H., Crow, W.T., De Jeu, R., De Lannoy, G., Huffman, G., Jackson, T.J. 2011. The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. Journal of Hydrometeorology. 12:750-765.
Narvekar, P., Heygster, G., Tonboe, R., Jackson, T.J. 2011. Analysis of windsat 3rd and 4th stokes components over Arct Sea ice. IEEE Transactions on Geoscience and Remote Sensing. 49:1627-1636.
Mehmet, K., O'Neill, P.E., Lang, R.H., Cosh, M.H., Joseph, A.T., Jackson, T.J. 2011. Impact of conifer forest litter on microwave emission at L-band. IEEE Transactions on Geoscience and Remote Sensing. 50(4):1071-1084.
Kurum, M., Lang, R.H., O'Neill, P.E., Joseph, A.T., Jackson, T.J., Cosh, M.H. 2011. A first-order radiative transfer model for microwave radiometry of forest canopies at L-band. IEEE Transactions on Geoscience and Remote Sensing. 49:3167-3179.