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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #422787

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

2014 Annual Report

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.

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.

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 disparate sources of information into true multi-scale assessments and leverage their mutual strengths. Required research topics to meet this goal include: 1) developing improved observational methods that exploit advances in both ground and satellite measurement methodologies, 2) combining remote sensing retrievals derived from multiple satellite sensors, 3) linking local measurements acquired from ground-based instrumentation to large-scale areal averages, and 4) using remote sensing and modeling to scale-up the impact of local management practices to the watershed scale. Early (i.e., 12-month) milestones in this project plan focused primarily on preliminary analysis and early data set collection activities required to meet later milestones. However, during FY14 project plans research progressed to address more advanced 24-month milestones which required the higher-level application of data sets to enhance specific remote-sensing, spatial scaling and modeling methodologies. Despite the ambitious progression between 12-month and 24-month milestones, the project remains on schedule. For example, Sub-objective 1.3 has moved beyond simply establishing ground-based soil moisture networks to examining statistical approaches for up-scaling ground-based soil moisture to a capture soil moisture variations at a watershed (~10 x 10 km2) spatial scale, and flux tower observations collected as part of Sub-objective 1.4 were analyzed to quantify how variations in measured fluxes are linked to seasonal variations in environmental conditions. In addition, key remote sensing sub-objectives are moving into novel new applications. Meeting the FY14 milestone for Sub-objective 1.1, for example, required new research to extend existing thermal remote sensing approach to previously-unexamined environments (i.e., snow-covered surfaces and highly spatially-clumped vegetative canopies found in vineyards). Likewise, the FY14 milestone for Sub-objective 1.2 required successfully extending an existing remote sensing algorithm to a completely new satellite platform. FY14 milestones for Objective 2 focus primarily on deploying new remote sensing approaches to improve our ability to characterize drought over large geographic regions at high spatial resolution. The FY14 milestone in Sub-objective 2.1 is particularly important in this regard as it entailed the merging of two-separate satellite sensors (within the STARFM algorithm) to acquire field-scale vegetation on a daily scale. Information at such scales is critical for a number of targeted agricultural applications. In addition, extended multi-year time-series of evapotranspiration generated with the continental-scale ALEXI surface energy balance model have been compared with tower-based flux and soil moisture information at several flux tower sites within the U.S., as well as in Spain and North Africa. As a result of this spatially-expanded analysis, revised continental-scale evapotranspiration fluxes are now being iteratively compared with moisture variables collected at a full set of ground-based monitoring sites to ensure optimal response over large continental regions. Likewise, 24-month FY14 milestones listed in Objective 3 focus on the expansion of existing modeling and measurement approaches to broader geographic areas. During FY14, project scientists demonstrated that satellite imagery made available by the USGS LEDAPS project can be applied to estimate the nutrient benefits of winter crop cover. This, in turn, can be used to expand the geographic extent of current crop cover monitoring efforts. Likewise, in order to meet the modeling FY14 milestone for Sub-objective 3.2, the application of the SWAT model was spatially extended to two significantly larger sub-watersheds (i.e., the Tuckahoe and Greensboro) within the Choptank watershed (located in Maryland’s Eastern Shore). Although these two adjacent sub-watersheds are similar in size, ongoing monitoring data has shown markedly different behavior in terms of their nutrient export patterns.

1. A global soil moisture product from the Aquarius satellite. Soil moisture is a key player in Earth’s water cycle. It can affect the planet’s energy flux, is essential for plant life, and influences weather and climate. Satellite measurements of soil moisture have the potential of proving this information globally on a frequent basis. ARS scientists working with data from NASA’s new Aquarius satellite instrument have developed a method for retrieving soil moisture from the sensor and have released new worldwide maps of soil moisture, revealing how the wetness of the land fluctuates with the seasons and weather phenomena. These maps are being provided operationally to the public and will support a wide range of agricultural and hydrologic applications, from advancing climate models and weather forecasts to improving flood-warning systems.

2. Operational implementation of a Global Root-Zone Soil Moisture Monitoring System. Globally monitoring the availability of root-zone soil moisture is critical for forecasting variations in agricultural productivity (due to e.g., drought) which impact global food prices and availability. By combining surface soil moisture retrievals obtained from a satellite with independent soil moisture estimates derived from rainfall observations, ARS scientists have designed an optimized system for globally estimating the availability of root-zone soil moisture. As of April 2014, predictions made by this system are being used by USDA Foreign Agricultural Service analysts to improve their operational forecasts of global agricultural yield and productivity. These forecasts are of critical importance for efforts to provide commodity markets with a source of unbiased information and provide decision makers with critical crop production information for food deficit countries that may require food aid assistance during severe drought.

Review Publications
Kustas, W.P., Agam, N., Soil evaporation. Encyclopedia of Natural Resources. DOI: 10.1081/E-ENRL-120049129.
Kongoli, C., Kustas, W.P., Anderson, M.C., Norman, J.M., Alfieri, J.G., Flerchinger, G.N., Marks, D.G. 2014. Evaluation of a two source snow-vegetation energy balance model for estimating surface energy fluxes in a rangeland ecosystem. Journal of Hydrometeorology. 15(1):143-158.
Panciera, R., Walker, J.P., Jackson, T.J., Ryu, D., Gray, D., Monerris, A., Yardley, H., Tanase, M., Rudiger, C., Wu, X., Gao, Y., Hacker, J. 2014. The soil moisture active passive experiments (SMAPEx): Towards soil moisture retrieval from the SMAP mission. IEEE Transactions on Geoscience and Remote Sensing. 52:490-507.
Leroux, D., Kerr, Y., Sahoo, A., Wood, E., Bindllish, R., Jackson, T.J. 2014. An approach to constructing a homogeneous time series of soil mositure using SMOS. IEEE Transactions on Geoscience and Remote Sensing. 52:393-405.
McCabe, M., Kustas, W.P., Anderson, M.C., Kiongoli, C., Ershadi, A., Hain, C. 2014. Global scale estimation of land surface heat fluxes from space: Current status, opportunities and future directions. In: Petropoulos, G.P., editor. Remote Sensing of Land Surface Turbulent Fluxes and Soil Moisture. Boca Raton, FL: Taylor & Francis, CRC Press. p. 506.
Leroux, D., Kerr, Y., Bitar, A., Gruhier, C., Bindlish, R., Jackson, T.J., Berthelotd, B., Portet, G. 2014. Comparison between SMOS, VUA, ASCAT, and ECMWF soil moisture products over four watersheds in the U.S. IEEE Transactions on Geoscience and Remote Sensing. 52:1562-1571.
Kim, Y., Jackson, T.J., Bindlish, R., Lee, H., Hong, S. 2013. Monitoring soybean growth using L, C, X-band scatterometer data. International Journal of Remote Sensing. 11:4069-4082.
McCarty, G.W., Hapeman, C.J., Rice, C., Hively, W.D., McConnell, L.L., Sadeghi, A.M., Lang, M.W., Whitall, D.R., Bialek Kalinski, K.M., Downey, P.M. 2014. Metolachlor metabolite (MESA) reveals agricultural nitrate-N fate and transport in Choptank River watershed. Science of the Total Environment. 473-474:473-482.
Crow, W.T. 2013. Estimating model and observation error covariance information for land data assimilation systems. In: S. Liang, X. Li and X. Xie, editors. Land Surface Observation, Modeling and Data Assimilation. Washington, DC: World Scientific Publishing Company. p. 171-205.
Gao, F.N., Kustas, W.P., Anderson, M.C. 2012. A data mining approach for sharpening satellite thermal imagery over land. Remote Sensing. 4:3287-3319.
Cosh, M.H., Jackson, T.J., Smith, C., Toth, B., Berg, A. 2013. Validating the BERMS in situ soil water content data record with a large scale temporary network. Vadose Zone Journal. DOI: 10.2136/vzj2012.0151.
Gao, F.N., Anderson, M.C., Kustas, W.P., Wang, Y. 2012. A simple method for retrieving leaf area index from landsat using MODIS LAI products as reference. Journal of Applied Remote Sensing (JARS). 6(1):063554.
Gao, F.N., Anderson, M.C., Kustas, W.P., Houborg, R. 2013. Retrieving leaf area index from landsat using MODIS LAI products and field measurements. Geoscience and Remote Sensing Letters. 11:773-777.
Wang, P., Gao, F.N., Masek, J. 2014. Operational data fusion framework for building frequent Landsat-like imagery in a cloudy region. IEEE Transactions on Geoscience and Remote Sensing. 52(11):7353-7365.
Beeson, P.C., Sadeghi, A.M., Lang, M.W., Tomer, M.D. 2013. Evaluating the effect of digital elevation model resolution on sediment prediction in water quality models. Journal of Environmental Quality. 43(1):26-36.
Mladenova, I., Jackson, T.J., Njoku, E., Bindlish, R., Chan, S., Cosh, M.H., Holmes, T.R., De Jeu, R., Jones, L., Kimball, J., Paloscia, S., Santi, E. 2014. Remote monitoring of soil moisture using passive microwave-based technologies – theoretical basic and overview of selected algorithms for AMSR-E. Remote Sensing of Environment. 197-213.
Holmes, T.R., Crow, W.T., Hain, C. 2013. Timing of the diurmal temperature cycle in remote sensing and model. Hydrology and Earth System Sciences. 17:3695-3706.
Ochsner, T., Cosh, M.H., Cuenca, R., Dorigo, W.A., Draper, C., Hagimoto, Y., Kerr, Y., Larson, K., Njoku, E., Small, E., Zreda, M. 2013. State of the art in large-scale soil moisture monitoring. Soil Science Society of America Journal. 77(6):1888-1919.
Walker, J., Beurs, K.M., Wynne, R.H., Gao, F.N. 2012. Evaluation of landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sensing of Environment. 117:381-393.
Bruscantini, C., Perna, P., Ferrazoli, P., Gringis, F., Karszenbaum, H., Crow, W.T. 2014. Effect of forward/inverse model asymmetries over retrieved soil moisture assessed with an OSSE for the Aquarius/SAC-D. Journal of Applied Remote Sensing (JARS). 50(1):371-375.
Rowlandson, T., Berg, A., Bullock, P., Ojo, E.R., McNairn, H., Wiseman, G., Cosh, M.H. 2013. Evaluation of several calibration procedures for a portable soil moisture sensor. Journal of Hydrology. 498:335-344.