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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

2015 Annual Report


Objectives
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.


Approach
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. During the third year of our project, significant progress was made in the development of new remote sensing retrieval techniques. For instance, a high-resolution thermal remote sensing data fusion scheme has been developed and validated over rain-fed and irrigated croplands, vineyards and a managed pine plantation. The data fusion technique is better able to capture the effects of rainfall and irrigation on changes in daily field-scale evapotranspiration compared with standard interpolation methods using only high resolution remote sensing data collected infrequently from Landsat. As a result, seasonal and annual water use estimates are observed to be more reliable when compared to observations using the data fusion methodology. In addition, significant progress in microwave remote sensing culminated in the January 2015 launch of the NASA Soil Moisture Active Passive (SMAP) mission which utilizes new instrument technologies and algorithm approaches. Over the past year, the SMAP soil moisture retrieval algorithms have been successfully adapted and tested. Validation resources and techniques have also been developed to evaluate SMAP soil moisture products via comparisons with ground-based observations. New SMAP soil moisture products with improved temporal and spatial resolution will improve our ability to assess, forecast, and adapt to hydrologic aspects of weather and climate. As described above, a key aspect of our project is the integration of new remote sensing technologies (described above) multi-scale drought assessment and monitoring tools. For example, daily time-series vegetation index (VI) at a 30-m pixel resolution was generated by fusing data from multiple satellite sensors data in Iowa. Field-scale crop phenology was then extracted from the VI time series and compared to the in-situ observations and National Agricultural Statistics Service (NASS) weekly crop progress report for the selected years from 2001 to 2014. The data fusion algorithm has been greatly improved in computing efficiency (~15 times faster). Parallel computing technique was implemented for a multi-processor system. The data fusion package has been extended to use Moderate Resolution Spectroradiometer (MODIS) surface reflectance data at both 250m and 500m pixel resolution. The new improvements greatly enhance our ability to monitor crop progress and condition at a field scale resolution over a large regional area and effectively respond to variations in crop progress associated with agricultural drought. Likewise, early warning lead time provided by the Evaporative Stress Index (ESI) was investigated for several flash drought events that occurred in the continental U.S. over the past 15 years. It was demonstrated that rapid changes in the ESI, indicative of rapid increases in moisture stress, preceded the introduction of severe-to-exceptional drought in the U.S. Drought Monitor (USDM) by more than 4 weeks. Based on demonstrated early warning capacity, the ESI is being integrated into a prototype multi-index composite known as QuickDRI (Quick Drought Response Index) co-developed by the University of Nebraska Lincoln and U.S. Geological Survey (USGS) to improve response time under flash drought conditions. Enhancements to microwave-based drought products were also achieved. In particular, during May 2014, the USDA Foreign Agricultural Service started the real-near-time operation of a global drought monitoring system developed at ARS Beltsville as part of this product. The system integrates a global soil water balance models with remotely-sensed surface soil moisture retrievals acquired from satellites to provide optimal estimates of root-zone soil water availability. Finally, comparable scaling, modeling and remote sensing tools were applied to examine key outstanding issues in catchment-scale water quality monitoring. In particular, the Soil and Water Assesment Tool (SWAT) model was applied to both the Tuckahoe and the Greensboro sub-watersheds within the larger Choptank River Watershed on the Maryland Eastern Shore. Despite the fact that the two, side-by-side, sub-watersheds are similar in size, ongoing monitoring data have shown different behavior in terms of their nutrient export patters. Since the installations of two in situ optical sensors at the outlets of the two sub-watersheds, we have collected more detailed stream flow and nitrate information and we were able to do a much better flow and nitrate calibrations of the SWAT model at these two watersheds. Our model simulations revealed that for improving water quality at these sub-watersheds, other conservation strategies (or best management practices) besides cover crops are also needed to improve water quality degradation. In addition to this water quality modeling work, remote sensing work focused on the (highly-challenging) task of mapping temporal dynamics of the inundation pattern wetlands within forested ecosystems. A method was developed to assess wetland hydro-period using the long-term Landsat record (1985 to present) and the highly accurate detection of inundation by lidar for calibration. This method gives long-term information on wetland hydro-period and can be used to isolate trends in wetland hydrology associated with climate change.


Accomplishments
1. In January 2015 the National Aeronautics and Space Administration (NASA) launched a new satellite called SMAP (Soil Moisture Active-Passive). ARS scientists in Beltsville, Maryland played key roles in the design and implementation of SMAP—an orbiting observatory that measures the amount of water in the top layer of the soil globally. Over 30 years of ARS research provided the basis for the satellite research mission. SMAP is the best satellite soil moisture sensor ever deployed due to its resolution, accuracy, global coverage and repeat time. It is currently collecting valuable soil moisture data that will help track diseases and famine; predict weather and climate patterns; assist emergency workers’ response to natural disasters; and let farmers know what crops to plant. Plans are already in place to integrate SMAP soil moisture products into agricultural forecast and monitoring systems operating at the USDA Foreign Agricultural Service (FAS) and National Agricultural Statistics Service (NASS).


Review Publications
Kumar, S.V., Peters-Lidard, C., Mocko, D., Reichle, R., Liu, Y., Arsenault, K.R., Xia, Y., Ek, M., Riggs, G., Livneh, B., Cosh, M.H. 2014. Assimilation of passive microwave-based soil moisture and snow depth retrievals for drought estimation. Journal of Hydrometeorology. 15:2446-2469.
Djamaï, N., Magagi, R., Goita, K., Hosseini, M., Cosh, M.H., Berg, A., Toth, B. 2015. Evaluation of SMOS soil moisture products over the CanEx-SM10 area. Journal of Hydrology. 520:254-267.
Coopersmith, E.J., Cosh, M.H., Daughtry, C.S. 2014. Field-scale moisture estimates using COSMOS sensors: A validation study with temporary networks and leaf-area-indices. Journal of Hydrology. 519:637-643.
Kustas, W.P., Alfieri, J.G., Evett, S.R., Agam, N. 2015. Quantifying variability in field scale evapotranspiration measurements in an irrigated agricultural region under advection. Irrigation Science. DOI: 10.107/S00271-015-0469-1.
Reuter, D., Richardson, C., Pellerano, F., Irons, J., Allen, R., Anderson, M.C., Jhabvala, M., Lunsford, A., Montanaro, M., Smith, R., Tesfaye, Z. 2015. The Thermal Infrared Sensor (TIRS) on Landsat 8: Design overview and pre-launch characterization. Remote Sensing. 7:1135-1153.
Roy, D., Wulder, M., Loveland, T., Woodcock, C., Allen, R., Anderson, M.C., Helder, D., Irons, J., Johnson, D., Kennedy, R., Scambos, T., Schott, J., Sheng, Y., Vermote, E., Belward, A., Bindschadler, R., Cohen, W., Gao, F.N., Hipple, J., Hostert, P., Huntington, J., Justice, C., Kilic, A., Kovalskyy, V., Lee, Z., Lymburner, L., Masek, J., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R., Zhu, Z. 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment. 145:154-172.
Yilmaz, M.T., Anderson, M.C., Zaitchik, B.F., Hain, C., Crow, W.T., Ozodogan, M., Chun, J.A., Evans, J. 2014. Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin. Water Resources Research. 50(1):386-408.
Ma, Y., Bao, S., Yang, T., Hu, J., Quan, J., He, Y., Wang, X., Wan, Y., Sun, X., Jiang, J., Gong, C., Zong, X. 2013. Genetic linkage map of Chinese native variety faba bean (Vicia faba L.) based on simple sequence repeat markers. Plant Breeding. doi.10.11/pbr.12074.
Parinussa, R.M., Yilmaz, M.T., Anderson, M.C., Hain, C., De Jeu, R. 2013. An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian penisula. Journal of Hydrological Processes. 28(18):4829–4988.
Otkin, J., Anderson, M.C., Hain, C., Mladenova, I., Basara, J., Svoboda, M. 2013. Examining rapid onset drought development using the thermal infrared based evaporative stress index. Journal of Hydrometeorology. 14(4):1057-1074.
Starkenburg, D., Fochesatto, G., Cristobal, J., Prakash, A., Gens, R., Alfieri, J.G., Nagano, H., Harazono, Y., Iwata, H., Kane, D. 2015. Temperature regimes and turbulent heat fluxes across a heterogeneous canopy in an Alaskan boreal forest. Journal of Geophysical Research Atmospheres. 120(4):1348-1360. DOI: 10.1002/2014JD022338.
Lee, S., In Young, Y., Sadeghi, A.M., McCarty, G.W., Hively, D.W. 2013. Assessing effectiveness of winter cover crops to improve water quality. Journal of Hydrology and Earth System Sciences. 10:14229-14263.
Cosh, M.H., Starks, P.J., Guzman Jaimes, J.A., Moriasi, D.N. 2014. Upper Washita River experimental watersheds: Multiyear stability of soil water content profiles. Journal of Environmental Quality. 43(4):1328-1333. DOI: 10.2134/jeq2013.08.0318.
Coopersmith, E.J., Cosh, M.H., Petersen, W., Prueger, J.H., Niemeier, J. 2015. Multi-scale soil moisture model calibration and validation: An ARS Watershed on the South Fork of the Iowa River. Journal of Hydrometeorology. 16(3):1087-1101. DOI:10.1175/JHM-D-14-0145.1
Srivastava, P., O'Neill, P., Cosh, M.H., Lang, R., Joseph, A. 2014. Evaluation of dielectric mixing models for microwave soil moisture retrieval using data from the Combined Radar/Radiometer (ComRAD) ground-based SMAP simulator. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 99:1-10. DOI: 10.1109/JSTARS.2014.2372031.
Coopersmith, E.J., Bell, J., Cosh, M.H. 2015. Improving the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN). Advances in Water Resources. 79:80-90. DOI:10.1016/j.advwatres.2015.02.006.
Jackson, T.J., Cosh, M.H., Crow, W.T. 2014. Some issues in validating satellite-based soil moisture retrievals with in situ observations and their Impact on SMAP validation. In: Lakshmi, V. Remote Sensing of the Terrestrial Water Cycle. American Geophysical Union Geophysical Monograph 206. Washington, DC: John Wiley and Sons. p. 247-254.
Bindlish, R., Jackson, T.J., Cosh, M.H., Zhao, T., O'Neill, P.E. 2015. Global soil moisture from the aquarius satellite: Description and initial assessment. Geoscience and Remote Sensing Letters. 12:923-927.
Kim, Y., Jackson, T.J., Bindlish, R., Hong, S., Jung, G., Lee, H. 2014. Retrieval of wheat growth parameters with radar vegetation indices. Geoscience and Remote Sensing Letters. 11:808-812.
Mcnairn, H., Jackson, T.J., Wiseman, G., Belair, S., Bullock, P., Colliander, A., Cosh, M.H., Magagi, R., Moghaddam, M., Adams, J., Berg, A., Homayouni, S., Ojo, E., Rowlandson, T., Shang, J., Goita, Hosseini, M. 2015. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Pre-launch calibration and validation of the SMAP satellite. IEEE Transactions on Geoscience and Remote Sensing. 53:2784-2801.
Colliander, A., Jackson, T.J., McNairn, H., Chaznoff, S., Dinardo, S., Latham, B., O'Dwyer, I., Chun, W., Yueh, S., Njoku, E. 2015. Comparison of airborne Passive and Active L-band System (PALS) brightness temperature measurements to SMAP observations during the SMAP Validation Experiment 2012 (SMAPVEX12). Geoscience and Remote Sensing Letters. 12:801-805.
Mierneckia, M., Wigneron, J., Lopez-Baeza, E., Kerr, Y., De Jeu, R., De Lannoy, G., Jackson, T.J., O’Neill, P., Moran, R., Bircher, S., Lawrence, H., Mialon, A., Al Bitard, A., Richaume, P. 2014. Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field. Remote Sensing of Environment. 154:89-101.
Zhao, T., Shi, J., Bindlish, R., Jackson, T.J., Kerr, Y., Cosh, M.H., Cui, Q., Li, Y., Xiong, C., Che, T. 2015. Refinement of SMOS multi-angular brightness temperature toward soil moisture retrieval and its analysis over reference targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8:589-603.
Liang, L., Schwartz, M., Wang, Z., Gao, F.N., Schaaf, C., Tan, B., Morisette, J., Zhang, X. 2014. Validation of spatiotemporally dense springtime land surface phenology with intensive and upscale in situ. Remote Sensing of Environment. 52:7513-7526.
Gao, F.N., He, T., Wang, Z., Ghimire, B., Shuai, Y., Masek, J., Schaaf, C., Williams, C. 2014. Generating multi-scale albedo look-up maps using MODIS BRDF/Albedo products and landsat imagery. Geoscience and Remote Sensing Letters. 8(1) 083532 DOI: 10.1117/1.JRS.8.083532
Ghimire, B., Williams, C., Masek, J., Gao, F.N., Wang, Z., Schaaf, C., He, T. 2014. Global albedo change and radiative cooling from anthropogenic land-cover change, 1700 to 2005 based on MODIS, land-use harmonization and radiative kernels. Geophysical Research Letters. DOI: 10.1002/2014GL061671.
Gao, F.N., He, T., Masek, J., Shuai, Y., Schaaf, C., Wang, Z. 2014. Angular effects and correction on medium resolution sensors for crop monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 99:1-10.
Weng, Q., Fu, P., Gao, F.N. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment. 145:55-67.
Shuai, Y., Masek, J., Gao, F.N., Schaaf, C., Tao, H. 2014. An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment. 152:467-479.
Yilmaz, M.T., Crow, W.T. 2014. Evaluation of assumptions in soil moisture triple collocation analysis. Journal of Hydrometeorology. 15:1293-1302. DOI:10.1175/JHM-D-0158.1
Han, E., Crow, W.T., Holmes, T.R., Bolten, J. 2014. Benchmarking a soil moisture data assimilation system for agricultural drought monitoring. Journal of Hydrometeorology. 15:1117-1134. DOI: 10.1175/JHM-D-13-0125.1.
Yu, X., Duffy, C., Kaye, J. , Crow, W.T., Bhatt, G., Shi, Y. 2013. Reanalysis of water and carbon cycle models at a Critical Zone Observatory. In: Lakshmi, V., Alsdorf, D., Anderson, M., Biancamaria, S., Cosh, M. H., Entin, J., Huffman, G., Kustas, W., van Oevelen, P., Painter, T., Parajka, J., Rodell, M., Rudiger, C. Remote Sensing of the Terrestrial Water Cycle. Hoboken, NJ: John Wiley & Sons, Inc. p.493-510.
Bruscantini, C., Crow, W.T., Gringis, F., Perna, P., Maas, M., Karszenbaum, H. 2014. An observing system simulation experiment (OSSE) for the aquarius/SAC-D soil moisture product. IEEE Transactions on Geoscience and Remote Sensing. 50(10):6086-6094. DOI:10.1109/TGARS.2013.2294915.
Nearing, G.S., Gupta, H.V., Crow, W.T., Gong, W. 2013. An approach to quantifying the efficiency of a Bayesian filter. Water Resources Research. 49(4):2164-2173. DOI: 10.1002/wrcr.20177.
Nearing, G.S., Gupta, H.V., Crow, W.T. 2013. Information loss in approximately bayesian data assimilation: A comparison of generative and discriminative approaches to estimating agricultural yield. Journal of Hydrology. 507:163-173.
Crow, W.T., Yilmaz, M.T. 2014. The auto-tuned land data assimilation system (ATLAS). Water Resources Research. 50(1):371-384. DOI:10.1002/2013WR0145502014
Qiu, J., Crow, W.T., Mo, X., Liu, S. 2014. The impact of vertical measurement depth on the information content of soil moisture times series data. Geophysical Research Letters. 41(14):4997-5004. DOI: 10.1002/2014GL060017.
Qui, J., Crow, W.T., Mo, X., Liu, S. 2014. The impact of temporal auto-correlation mismatch on the assimilation of satellite-derived surface soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(8):3534-3542. DOI: 10.1109/JSTARS.2014.2349354.
Su, C., Ryu, D., Crow, W.T., Western, A. 2014. Beyond triple collocation: Applications to satellite soil moisture. Journal of Geophysical Research Atmospheres. 119(11):6419-6439. DOI: 10.1102/2013JD021043.
Chen, F., Crow, W.T., Ryu, D. 2014. Simultaneous state and forcing data correction for improved rainfall-runoff modeling. Journal of Hydrometeorology. 15(5):1832-1848. DOI: 10.1175/JHM-D-14-0002.1.
Holmes, T., Crow, W.T., De Jeu, R. 2014. Leveraging microwave polarization information for calibration of a land data assimilation system. Geophysical Research Letters. 41(24):8878-8886. DOI: 10.1002/2014GL061991.
Su, C., Ryu, D., Crow, W.T., Western, A. 2014. Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method. Remote Sensing of Environment. 154:115-126. DOI: 10.1016/j.rse.2014.08.014.
Alvarez, C., Ryu, D., Western, A., Crow, W.T., Robertson, D. 2014. The impacts of assimilating satellite soil moisture into a rainfall-runoff model in a semi-arid catchment. Journal of Hydrology. 519(D):2763-2774. DOI:10.1016/j.jhydrol.2014.07.041.
Li, Y., Ryu, D., Western, A., Wang, Q.L., Robertson, D., Crow, W.T. 2014. An integrated error estimation and lag-aware data assimilation scheme for real-time flood forecasting. Journal of Hydrology. 519(D):2722-2736. DOI: 10.1016/j.jhydrol.2014.08.009.
Holmes, T.R., Crow, W.T., Hain, C., Anderson, M.C., Kustas, W.P. 2015. Amplitude of the diurnal temperature cycle as observed by thermal infrared and microwave radiometers. Remote Sensing of Environment. 158:110-125.