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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

2016 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 water resource applications (including water quality management and drought monitoring). 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 fourth year of this project, significant progress was made in the evaluation and implementation of new remote sensing retrieval techniques. Integral to the project is the implementation and validation of soil moisture retrieval algorithms for estimating surface soil moisture levels based on satellite-based passive microwave (MW) observations. Following the 2015 launch of a new NASA satellite called the Soil Moisture Active Passive (SMAP) mission, a rigorous validation of the mission’s primary soil moisture product was successfully completed. These new SMAP MW remote sensing products provide improved temporal and spatial resolution and will improve our ability to assess, forecast, and adapt to hydrologic aspects of weather and climate. In addition to MW-based product, rigorous evaluation approaches were also applied to both thermal infrared (TIR) and visible/near -infrared (VIS/NIR) remote sensing products. For example, for the case of TIR remotes sensing, regional evapotranspiration (ET) products were compared with other remote sensing ET datasets in several regions within the US (New Mexico, Nebraska, Arizona) and globally (Nile River and Red River basins). These ET products compared well, and in many cases outperformed other models as evaluated through surface flux observations and regional water balance assessments. Also, TIR-based soil moisture predictions were successfully evaluated against MW satellite based SM products and land surface model simulations. Vegetation phenology products generated from VIS/NIR remote sensing were integrated into remote sensing data fusion system for automated processing. Crop phenology products generated in central Iowa were compared to the crop growth stages reported by USDA NASS, and crop growth stages were mapped at field scale. The results of this comparison indicated that the data fusion approach substantially improves our ability to monitor in-season crop phenology and development over regional agricultural landscapes. Phenology products produced by the approach were delivered to USDA NASS and used for estimating crop yield. The simultaneous availability of validated MW, TIR, and VIS/NIR observations also spurred the development of new integrative data assimilation techniques which fuse all three sources of data for enhanced agricultural drought monitoring. In particular, new integrative techniques were developed which allowed for the improved characterization of root-zone soil moisture anomalies derived from an ensemble of land surface model predictions and the robust calculation of coupling coefficients between surface soil moisture and latent heat flux. In addition, analyses were conducted comparing a wide range of MW, TIR and VIS/NIR remote sensing products to gridded USDA NASS crop condition and yield reports. Results illustrated the ability of these products to forecast variations in crop yield. Taken as a whole, the results of these studies significantly improve our ability to characterize the onset, evaluation and eventual (crop and forage production) impact of agricultural drought. As noted above, the development of ground-based observations for these satellite validation efforts is also a major project priority. Micrometeorological and surface flux data were collected over natural and both irrigated and rain-fed agricultural ecosystems. These datasets, along with complementary data collected over several Ameriflux sites, were used as the basis of analyses inter-comparing micrometeorological measurement techniques and exploring the temporal persistence of surface fluxes, especially ET. These analyses suggest that the degree of temporal variability exhibited by the surface fluxes is strongly influenced by water availability and vegetation density. In addition to hydrologic analysis of water quantity, the project also focuses on enhancing our ability to transition water quality management from the field to watershed scale. In particular, substantial progress was made in the application of SWAT hydrology model for quantifying the watershed-scale impact of winter cover crop (WCCs) best management practices. SWAT was applied to two sub-watersheds that were similar in size, but had shown large differences in nitrate load exports. Modeling results showed that the difference in nitrate loads was mostly attributed to the differences in the soil characteristics in terms of their drainage potentials. Furthermore, model simulation results revealed that with the current WCCs BMPs/practices, the amount of nitrates in watersheds is not expected to decrease adequately and other conservation strategies or BMPs are required to adequately cope with climate-driven water quality degradation. Wetland function has been shown to be a particular challenge when implementing water quality management at a watershed scale. Therefore, the development of wetland mapping tools has emerged as a major project priority. With regards to such mapping, we found that there is clear evidence that the connection of wetlands to the local stream network influences their structure and function. In response, we have developed new LiDAR (Light Detection and Ranging) remote sensing approaches to quantitatively assess the level of this connectivity.

1. Integrated remote sensing system for mapping crop phenology at field scales. Crop progress information can benefit farmers in scheduling irrigation, fertilization and harvest operations. The USDA National Agricultural Statistics Service (NASS) reports crop progress and condition supplied by local farmers weekly at the state and district levels; however, ground data collection is time consuming, highly localized, and produces inconsistent data quality. While remote sensing can provide timely and consistent large-area coverage, standard single-sensor products lack either the spatial or temporal resolution needed for robust monitoring at field scale; therefore, ARS scientists at Beltsville, Maryland, have developed an integrated remote sensing system to map crop phenology using remote sensing data from multiple satellite platforms. Near-daily vegetation indices at 30-m spatial resolution can be produced for monitoring crop progress and condition; further, using these time-series datasets, remote sensing phenology were extracted and used to deduce key crop growth stages (e.g., emergence, maximum green-up, and harvest) over agricultural landscapes. Such information is critical for monitoring the growth and development of agricultural crops and will be used by USDA NASS to provide improved in-season monitoring of domestic agricultural production.

2. Development and validation of the Soil Moisture Active Passive (SMAP) Satellite. The grand challenge to soil moisture remote sensing has been to provide highly accurate soil moisture information globally at a spatial resolution that supports a wide range of agricultural, hydrologic, and climate applications. Over the past five years a satellite instrument (Soil Moisture Active Passive-SMAP) was developed that solved this problem by combining active and passive microwave observations. The satellite was successfully launched by NASA in January 2015 and following initial system calibration, an intensive validation program was undertaken that utilized a variety of ground-based soil moisture monitoring networks operated by USDA, as well as international collaborators. The results of these analyses indicate that the sensors and algorithms are performing better than the pre-launch goal. Soil moisture products from satellite sensors have the potential to dramatically improve the accuracy and timeliness of weather, climate, and agricultural assessments and forecasts used by the United States Department of Agriculture, the National Oceanic and Atmospheric Administration, and other agencies.

3. The GOES Evapotranspiration and Drought (GET-D) Product System. The GOES (Geostationary Operational Environmental Satellite) Evapotranspiration and Drought (GET-D) Product System passed Operational Readiness Review by the NOAA’s Office of Satellite and Produce Operations (OSPO) in January 2016. The GET-D system based on remote sensing approaches initially developed by ARS scientists in Beltsville, Maryland, will operationally produce daily maps of evapotranspiration (ET) and an associated Evaporative Stress Index (ESI) over North America at 8-km spatial resolution. These routine ET and ESI products will be used within NOAA’s drought monitoring and land-surface modeling programs, and additionally disseminated through the National Integrated Drought Information System (NIDIS). This project is an example of multi-agency collaboration, with ARS models transitioned to operational production at NOAA, funded in part by NASA’s Applied Sciences program and it became operational at NOAA as of June 6, 2016. See

4. Improved constraint of land atmosphere coupling using remote sensing. Evapotranspiration (ET) from the land surface is sensitive to a wide variety of environmental factors which include the availability of sufficient root-zone soil moisture for crops and vegetation to actively transpire. Therefore, a key factor for determining the onset of agricultural drought is the limiting relationship between soil moisture and ET; further, despite its importance (and recent advances in the remote sensing of both soil moisture and ET), relatively little is known about the actual strength of this relationship over agricultural region. The key obstacle is that remotely-sensed estimates of ET and soil moisture are inevitably impacted by mutually-independent random errors which spuriously reduce the strength of ET/soil moisture correlations sampled from observations. USDA ARS scientists in Beltsville, Maryland, have recently developed a new data assimilation technique which corrects for the impact of this random error on sampled estimates of ET/soil moisture coupling strength. As a result, the approach can be used to provide (for the first time) reliable large-scale estimates of soil moisture/ET coupling strength over agricultural regions and will also be used to improve our ability to model (and thus predict) the onset and severity of agricultural drought events.

5. The Soil Moisture Active Passive Validation Experiment in 2015 (SMAPVEX15). The first validation experiment for the NASA Soil Moisture Active Passive satellite mission was completed in 2015 in southeastern Arizona. The satellite mission is the first mission to use both active and passive L-band sensors to monitor surface soil moisture at a high resolution. A ground and aircraft validation campaign was conducted in and around the Walnut Gulch Experimental Watershed near Tombstone, Arizona, to provide a valuable ground truth dataset to verify the accuracy of the mission product. This was conducted during August of 2015, which coincided with the North American Monsoon System in the region, providing the most dynamic range of soil moisture to study. The results of this experiment are providing datasets for the revision of algorithms for monitoring soil moisture in semi-arid domains, such as the southwestern U.S. This will ultimately improve the understanding of climate and weather dynamics in a drought prone region which is of significant interest to the U.S. Department of Agriculture and the National Oceanic and Atmospheric Administration.


Review Publications
Zhu, X., Lefsky, M., Helmer, E., Liu, D., Chen, J., Gao, F.N. 2015. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment. 172:165-177.
Sheng, Y., Song, C., Wang, J., Lyons, E., Knox, B., Cox, J., Gao, F.N. 2016. Comprehensive lake dynamics mapping at continental scales using Landsat 8. Remote Sensing of Environment. doi:10.1016/j.rse.2015.12.041.
Dong, G., Ochsner, T., Zreda, M., Cosh, M.H., Zou, C. 2014. Calibration and validation of the COSMOS rover for surface soil moisture. Vadose Zone Journal.13:4. doi: 10.2136/vzj2013.08.0148.
Temimi, M., Lakhankar, T., Zhan, X., Cosh, M.H., Krakauer, N., Kelly, V., Kumissi, L. 2014. A ground based L-band radiometer for the monitoring of soil moisture in the region of Millbrook, New York, USA. Vadose Zone Journal. 13:3. doi: 10.2136/vzj2013.06.0101.
Otkin, J., Anderson, M.C., Hain, C., Svoboda, M. 2015. Using temporal changes in drought indices to generate probabilistic drought intensification forecasts. Journal of Hydrometeorology. 16:88-105.
Rondinelli, W., Hornbuckle, B., Patton, J., Cosh, M.H., Walker, V., Carr, B., Logsdon, S.D. 2015. Different rates of soil dyring after rainfall are observed by the SMOS satellite and the South Fork In Situ Soil Moisture Network. Journal of Hydrometeorology. 16(2):889-903. doi:10.1175/JHM-D-14-0137.1.
Martens, B., Lievens, H., Colliander, A., Jackson, T.J., Verhoest, N. 2015. Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12. IEEE Transactions on Geoscience and Remote Sensing. 63:4091-4203.
Gao, Y., Walker, J., Allahmoradi, M., Monerris, A., Ryu, D., Jackson, T.J. 2015. Optical sensing of vegetation water content: A synthesis study. IEEE Journal of Selected Topics in Applied Remote Sensing. 8:1456-1464.
Timmermans, W., Kustas, W.P., Andreu, A. 2015. Utility of an automated thermal-based approach for monitoring evapotranspiration. Acta Geophysica. doi:10.1515/acgeo-2015-0016.
Paloscia, S., Santi, E., Pettinato, S., Mladenova, I., Jackson, T.J., Bindlish, R., Cosh, M.H. 2015. A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data. Frontiers of Earth Science. 3(16):1-10.
Gao, F.N., Hilker, T., Zhu, X., Anderson, M.C., Masek, J., Wang, P., Yang, Y. 2015. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geoscience and Remote Sensing Magazine. 3:47-60.
Coopersmith, E.J., Cosh, M.H., Bell, J., Crow, W.T. 2016. Multi-profile analysis of soil moisture within the U.S. Climate Reference Network. Vadose Zone Journal. 15(1). doi: 10.2136/vzj2015.01.0016.
Coopersmith, E.J., Cosh, M.H., Bindlish, R., Bell, J. 2015. Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN). Advances in Water Resources. 85:79-85. doi: 10.1016/j.advwatres.2015.09.003.
Kim, S., Jackson, T.J., Yueh, S., Xu, X., Hensley, S. 2015. Feasibility of inter-comparing airborne and spaceborne observations of radar backscattering coefficients. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8:3507-3519.
Song, L., Liu, S., Kustas, W.P., Zhou, J., Ma, Y. 2015. Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data. Remote Sensing in Hydrology and Water Management. 7:5828-5848. DOI: 10.3390/rs70505828.
Huang, H., Kim, S., Tsang, L., Xu, X., Liao, T., Jackson, T.J., Yueh, S. 2016. Coherent model of L-band radar scattering by soybean plants: model development, validation and retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(1):272-284.
Kornelsen, K., Cosh, M.H., Coulibaly, P. 2015. Potential of bias correction for downscaling passive microwave and soil moisture data. Journal of Geophysical Research Atmospheres. 120(13), 6460-6479. doi: 10.1002/2015JD023550.
Kool, D., Kustas, W.P., Ben-Gal, A., Lazarovitch, N., Heitman, J., Sauer, T.J., Agam, N. 2016. Seasonal energy and evapotranspiration partitioning in a desert vineyard. Agricultural and Forest Meteorology. 218–219 (2016) 277–287.
Xia, T., Kustas, W.P., Anderson, M.C., Alfieri, J.G., Gao, F.N., Mckee, L.G., Prueger, J.H., Geli, H., Neale, C., Sanchez, L., Alsina, M., Wang, Z. 2016. Mapping evapotranspiration with high resolution aircraft imagery over vineyards using one and two source modeling schemes. Hydrology and Earth System Sciences. 20:1523-1545.
Zhao, T., Shi, J., Bindlish, R., Jackson, T.J., Cosh, M.H., Lingmei, J., Zhongjun, Z., Huimin, L. 2015. Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval. Physics and Chemistry of the Earth. 83-84:65-74.
Yang, G., Weng, Q., Pu, R., Gao, F.N., Sun, C., Li, H., Zhao, C. 2016. Evaluation of ESTARFM based algorithm for generating land surface temperature products by fusing ASTER and MODIS data during the HiWATER-MUSOEXE. Remote Sensing. 8, 75; doi:10.3390/rs8010075.
Otkin, J., Anderson, M.C., Hain, C., Svoboda, M., Johnson, D., Mueller, R., Tadesse, T., Wardlow, B., Brown, J. 2016. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agricultural and Forest Meteorology. 218-219, 230-242.
Hain, C., Crow, W.T., Anderson, M.C., Yilmaz, M. 2015. Diagnosing neglected soil moisture source/sink processes via a thermal infrared-based two-source energy balance model. Journal of Hydrometeorology. 16:1070-1086. doi: 010.1175/JHM-D-14-0017.1.
Aghakouchak, A., Farahmand, A., Teixeira, J., Wardlow, B., Melton, F., Anderson, M.C., Hain 2015. Remote sensing of drought: progress, challenges and opportunities. Reviews of Geophysics. doi: 10.1002/2014RG000456.
Anderson, M.C., Zolin, C., Hain, C., Semmens, K.A., Yilmaz, M., Gao, F.N. 2015. Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013. Journal of Hydrology. doi: 10.1016/j.jhydrol.2015.1001.1005.
Otkin, J., Shafer, M., Svoboda, M., Wardlow, B., Anderson, M.C., Hain, C., Basara, J. 2015. Facilitating the use of drought early warning information through interactions with stakeholders. American Meteorological Society. 96:1073-1078.
Cosh, M.H., Ochsner, T., Mckee, L.G., Dong, G., Basara, J., Evett, S.R., Hatch, C., Small, E., Steele-Dunne, S., Zreda, M., Sayde, C. 2016. The Soil Moisture Active Passive Marena Oklahoma In Situ Sensor Testbed (SMAP-MOISST): Design and initial results. Vadose Zone Journal. 15(4). doi: 10.2136/vzj2015.09.0122.
Alvarez, C., Ryu, D., Su, C., Crow, W.T., Robertson, D., Leahy, C. 2015. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes. Hydrology and Earth System Sciences. 19:1659-1676. doi: 10.5194/hess-19-1659-20151659-1676.
Han, E., Crow, W.T., Hain, C., Anderson, M.C. 2015. On the use of a water balance to evaluate inter-annual terrestrial ET variability. Journal of Hydrometeorology. 16:1102-1108. doi: 10.1175/JHM-D-14-0175.1.
Crow, W.T., Su, C., Ryu, D., Yilmaz, M. 2015. Optimal averaging of soil moisture predictions from ensemble land surface model simulations. Water Resources Research. 51:9273–9289. doi: 10.1002/2015WR016944.
Semmens, K., Anderson, M.C., Kustas, W.P., Gao, F.N., Alfieri, J.G., Mckee, L.G., Prueger, J.H., Hain, C., Cammalleri, C., Yang, Y., Xia, T., Sanchez, L., Alsina, M., Velez, M. 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment. doi: 10.1016/j.rse.2015.1010.1025.
Pan, M., Fisher, C., Chaney, N., Zhan, W., Aires, F., Crow, W.T., Entekhabi, D., Wood, E. 2015. Triple collocation: beyond three estimates and separation of structural/non-structural errors. Remote Sensing of Environment. 171:299-310.
Gruber, A., Crow, W.T., Dorigo, W., Wagner, W. 2015. The potential of 2D Kalman filtering for soil moisture data assimilation. Remote Sensing of Environment. 171:137-148.
Gruber, A., Su, C., Zwiebeck, A., Crow, W.T., Dorigo, W., Wagner, W. 2016. Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation. 45(B):200-211. doi: 10.1016/j.jag.2015.09.002.
Gruber, A., Su, S., Crow, W.T., Zwiebeck, A., Dorigo, Wagner 2016. Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. Journal of Geophysical Research Atmospheres. 121:1208-1219. doi: 10.1002/2015JD024027.
Lu, H., Crow, W.T., Zhu, Y. 2015. The impact of assumed error variances on surface soil moisture and snow depth hydrologic data assimilation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(11):5116-5129. doi: 10.1109/JSTARS.2015.2487740.
Lei, F., Crow, W.T., Shen, H., Parinussa, R., Holmes, T. 2015. The impact of acquisition times on the accuracy of microwave soil moisture retrievals over the contiguous U.S. 7(10)13448-13465. doi: 10.3390/rs71013448.
Crow, W.T., Lei, F., Anderson, M.C., Hain, C., Scott, R.L., Billesbach, D., Arkebauer, T. 2015. Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation. Geophysical Research Letters. 42:8415-8423. doi: 10.1002/2015GL065929.
Liu, P., Bongiovqnni, T., Monsivais-Huertero, A., Judge, J., Steele-Dunne, S., Bindlish, R., Jackson, T.J. 2016. Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(4):1357-1369.
Anderson, M.C., Zolin, C., Sentelhas, P., Hain, C., Semmens, K., Yilmaz, M., Gao, F.N., Otkin, J., Tetrault, R. 2016. Assessing correlations of satellite-derived evapotranspiration, precipitation and leaf area index anomalies with yields of major Brazilian crops. Remote Sensing of Environment. 174:82-99.
Liao, T., Kim, S., Tan, S., Tsang, L., Su, C., Jackson, T.J. 2016. Multiple scattering effects with cyclical terms in active remote sensing of vegetated surface using vector radiative transfer theory. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(4):1414-1429.