Location: Hydrology and Remote Sensing Laboratory2019 Annual Report
Objective 1: Develop and evaluate new methodologies and tools for characterizing spatiotemporal variability in land-surface water balance components from plot to global scales, integrating multi-sensor remote and in-situ measurement sources. Sub-objective 1.1: Improve representations of water and energy exchanges in structured agricultural environments, developed using in-situ measurements. Sub-objective 1.2: Improve multi-sensor tools for mapping water use over irrigated and rainfed crops, forests and rangelands. Sub-objective 1.3 Improve remote sensing tools for mapping regional and global soil moisture. Sub-objective 1.4: Develop new techniques for measuring soil moisture variability in situ and upscaling for validation of satellite retrievals. Sub-objective 1.5: Evaluate the terrestrial water budget at basin scale via the integration of remote sensing with ground observations. Objective 2: Develop remote sensing and modeling approaches for determining the timing and magnitude of agricultural drought and its impact on agroecosystems and onhe regional hydrology. Sub-objective 2.1: Improve early warning tools for identifying agricultural drought onset, severity and recovery at local to regional scales. Sub-objective 2.2: Improve techniques for assessing crop and rangeland phenology and condition and for forecasting yields. Sub-objective 2.3: Enhance understanding and monitoring of drought impacts on regional hydrologic components. Objective 3 (short): Assess the hydrologic status and trends within the Lower Chesapeake Bay Long-Term Agroecosystem Research site through measurements, remote sensing, and modeling. Sub-objective 3.1: Establish long-term data streams for the LCB LTAR project to examine agroecosystem status and trends. Sub-objective 3.2: Examine the effects of irrigation intensification within the LCB LTAR on trends in regional hydrology and nitrogen dynamics. Sub-objective 3.3: Improve prediction capability of SWAT in evaluating the effects of both natural riparian and restored wetlands on water quality. Sub-objective 3.4: Investigate sources and fate of nitrate in the LCB LTAR.
This project seeks to develop new tools for agricultural monitoring and management that integrate ground observations, remote sensing data and modeling frameworks. In specific, these multiscale tools will be used to address characterization of water supply (soil moisture), water demand (evapotranspiration), water quality drivers and drought impacts over agricultural landscapes.
This report documents progress for the second year of Project 8042-13610-029-00D “Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems” which started in 2017. Substantial progress was made in all three objectives outlined in the new project plan, all of which fall under NP 211. Under Objective 1, research activities were focused on using data collected in project year 1 to develop and enhance modeling tools for mapping crop water use and energy balance over agricultural landscapes. As a part of the Grape Remote Sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX), micrometeorological measurements collected above and within the vine canopy and the inter-row space were used to investigate the influence of canopy structure on water and energy exchanges between the canopy and the atmosphere (sub-objective 1.1). Analyses include quantifying the effects of wind direction and vineyard architecture on turbulent mixing, spectral analyses characterizing the turbulent transport of heat and moisture within and above the canopy, investigations of radiative transfer through the plant canopy, and characterizing the spatial variability of the soil heat flux. Based on these analyses, new model algorithms for the thermal-based two-source energy balance and evapotranspiration modeling system were developed for highly structured canopies and tested at vineyard flux sites in the California Central Valley. Additionally, a new more robust aerodynamic soil resistance algorithm was implemented and tested for strongly clumped and sparsely vegetated arid and semiarid landscapes in the U.S., Spain and China (sub-objective 1.2). These model enhancements will improve remote sensing estimates of vegetation water use over natural and agricultural landscapes. National and international soil moisture monitoring capabilities were advanced in FY19 using both in situ sensor networks and spaceborne imagery. An initiative has begun to develop a strategy for a National Soil Moisture Network for improved drought monitoring, which will draw heavily from the Marena Oklahoma In Situ Sensor Testbed (MOISST). New collaborations have developed with the current Cyclone Global Navigation System (CYGNSS) NASA mission for soil moisture calibration and validation, as well as the future NASA-ISRO Synthetic Aperture Radar (NISAR) mission, which will monitor agricultural landscapes among other domains (sub-objective 1.4). A unified AMSR-E/AMSR2 Earth Science Data Record for soil moisture was completed and sent to the NASA National Snow and Ice Data Center Distributed Active Archive Center (sub-objective 1.3). Satellite-based methods for real-time monitoring of agricultural drought and crop yield and phenology were implemented and evaluated at new test sites and over larger regions. The global Evaporative Stress Index (ESI) was demonstrated to provide robust and timely depiction of the severe European drought that occurred in 2018. ESI was also compared to ground-based crop condition records collected in the Czech Republic, showing excellent correlations (sub-objective 2.1). The relationships between yields and vegetation index time series over central Iowa (2001-2018), generated with a multi-sensor data fusion system, were analyzed at both fine (30-m) and coarse (500-m) spatial resolutions. Larger scale analyses were conducted using Google Earth Engine (GEE), assessing spatial and temporal variability of corn and soybean yields over 10 major agricultural states and resulting in a published manuscript. An algorithm for mapping crop phenology at coarse resolution (500-m) was tested and published, with a near real-time algorithm to map crop emergence at field to sub-field scales (5-30m) currently under development and validation (sub-objective 2.2). Several important advances were made in the development of remote sensing and data assimilation/integration techniques to improve our understanding of the large-scale relationship between soil moisture and surface water fluxes. For example, research results demonstrated the first successful application of microwave remote sensing to diagnosis coupling-strength biases in land surface models (between soil moisture and both surface runoff and evapotranspiration). Another key result illustrated the ability of remote sensing to map global patterns in net surface water flux. These advances represent important milestones toward overall project goals of improving model representation of the impact of agricultural drought on regional hydrologic components (sub-objective 2.3) and developing new tools for estimating large-scale terrestrial water balance components (sub-objective 1.5). Data collection and analyses at the Lower Chesapeake Bay (LCB) Long Term Agroecosystem Research Data (LTAR) continued in FY19 under Objective 3 in support of remote sensing and modeling research on the connections between agricultural water use, land management and water quality. Real-time in situ water quality data were collected at the Tuckahoe and Greensboro USGS gage stations to extend our long-term water quality record. Consumptive water use (evapotranspiration), including localized enhancements due to irrigation and wetland complexes, was mapped over the LCB LTAR landscapes at 30-m resolution using a multi-source satellite retrieval toolkit, and will be used to calibrate Soil & Water Assessment Tool (SWAT) model runs simulating the connected hydrologic system (sub-objective 3.2). Wetland hydrology was continuously monitored at various wetland sites in the Choptank watershed to assess the connection of isolated wetlands to downstream discharges (sub-objective 3.3). The Agricultural Policy Environmental Xtender (APEX) model was enhanced with a new biochemical module for representing nutrient cycling in wetlands. The enhanced APEX model was tested against observed flows of sediment and various forms of nutrients (carbon, nitrogen and phosphorus) for a wetland located on the Eastern shore of Maryland, demonstrating improved ability to assess the environmental benefits of wetland conservation. In addition, monthly water samples at the gage stations were collected for Metolachlor Metabolite (MESA) analyses to determine groundwater lag time (sub-objective 3.4). Watershed sampling for groundwater lag time using the MESA as transit tracer, was initiated at 15 watersheds in the LTAR and Conservation Effects Assessment Project (CEAP) Watersheds networks. This nationwide network will answer key question concerning influences of land use, soil type, management on lag times.
1. Toolkit for daily water use monitoring in California Central Valley vineyards. Persistent and extreme drought has plagued California in the last decade with enormous implications on surface and groundwater water resources for agriculture. Achieving long-term water use sustainability in an economically viable way will require more efficient irrigation management to successfully address future water shortages. ARS scientists in Beltsville, Maryland, have led the Grape Remote Sensing Atmospheric profile & Evapotranspiration eXperiment (GRAPEX) project with the goal of developing a new remote sensing-based data fusion technique that allows, for the first time, accurate estimation of daily water use and stress information from field to regional scales for high-valued perennial crops. Operational application of this technique has begun and is expected to facilitate substantial reductions in irrigation water usage for these crops.
2. Global mapping of soil moisture/evapotranspiration coupling. Accurately estimating the flux of water and energy between the land surface and the lower atmosphere is important for forecasting the onset and evolution of an agricultural drought event. ARS scientists in Beltsville, Maryland, have developed a new mathematical technique for correcting sampled estimates of the correlation between surface soil moisture and surface evaporation for the impact of random measurement error. This technique enables the application of existing remotely sensed estimates of soil moisture and surface evaporation. By improving our understanding of land/atmosphere feedbacks, this research enhances our ability to forecast precipitation and air temperature extremes within the central United States.
3. Variability of corn and soybean yields explained by high resolution imagery. Accurate estimation of crop yield is critical for sustaining agricultural markets and ensuring food security. Remote sensing data have been used to estimate crop yield for decades. However, the value of high spatial and temporal resolution remote sensing data for yield estimation has not yet been thoroughly investigated. Using a remote sensing data fusion system, ARS scientists in Beltsville, Maryland, evaluated the added value of high temporal and spatial resolution data for yield estimation in the Corn Belt using Landsat, Sentinel-2, MODIS and multi-sensor data fusion. Results demonstrate the improvement in yield estimate accuracy achieved when more frequent satellite imagery with sub-field spatial resolution is incorporated into the modeling process. Once implemented operationally, this research will improve the ability of the National Agricultural Statistics Service to track and predict interannual variations in domestic commodity crop production.
4. Determining the connection of geographically isolated wetlands with downstream waters. The nexus of wetlands to downstream waters is critical to wetland regulatory status. Numerous studies have been reported on the impacts of riparian wetlands on downstream water flow and quality, but little is known about the impact of geographically isolated wetlands. To demonstrate the hydrological impacts of isolated wetlands on the downstream flow, we compared two model scenarios of Soil and Water Assessment Tool (or SWAT) model; one including all wetlands that connect to the stream network during the seasonal inundation and the other excluding all the connected wetlands. Our model simulation results indicated that geographically isolated wetlands served as important landscape features to control watershed hydrology. Further, based on our findings, we conclude that isolated wetlands exert significant impacts on maintenance of upstream hydrology and downstream flow for this region. This insight improves our ability to manage watersheds in a way that minimizes agricultural impacts on downstream water quality and quantity.
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Crow, W.T., Chen, F., Reiche, R., Xia, Liu, Q. 2018. SMAP Level 4 soil moisture estimates reveal possible bias in the runoff response of land surface models. Geophysical Research Letters. 45(10):4869-4878. https://doi.org/10.1016/j.rse.2018.05.008.
Chen, F., Crow, W.T., Bindlish, R., Colliander, A., Burgins, M., Asanuma, J., Aida, K. 2018. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation. Remote Sensing of Environment. 214:1-13. https://doi.org/10.1016/j.rse.2018.05.008.
Koster, R., Crow, W.T., Reichle, R., Mahanama, S. 2018. Estimating basin-scale water budgets with SMAP soil moisture data. Water Resources Research. 54(7):4228-4244. https://doi.org/10.1029/2018WR022669.
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Walker, V., Hornbuckle, B., Cosh, M.H. 2018. Investigating the SMOS dry bias in the corn belt of the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(12):4664-4675. https://doi.org/10.1109/JSTARS.2018.2864897.
Kraatz, S., Jacobs, J., Schroeder, R., Cho, E., Cosh, M.H., Seyfried, M.S., Prueger, J.H., Livingston, S.J. 2019. Evaluation of SMAP freeze/thaw retrieval accuracy at core validation sites in the contiguous United States. Remote Sensing. 10(9):1483. https://doi.org/10.3390/rs10091483.
Gao, F.N., Anderson, M.C., Daughtry, C.S., Johnson, D. 2018. Assessing variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery. Remote Sensing of Environment. 10:1489. https://doi.org/10.3390/rs10091489.
Tao, H., Gao, F.N., Liang, S., Peng, Y. 2019. Mapping climatological bare soil albedos over the contiguous United States using MODIS data. Remote Sensing. 11:666. https://doi.org/10.3390/rs11060666.
Li, Z., Huag, C., Zhu, Z., Gao, F.N., Tang, H., Xin, X., Ding, L., Shen, B., Liu, J., Chen, B., Wang, X., Yan, R. 2018. Mapping daily leaf area index at 30m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data. International Journal of Remote Sensing. 39(23):9025-9053. https://doi.org/10.1080/01431161.2018.1504342.
Lee, S., Yeo, I., Lang, M., Sadeghi, A.M., McCarty, G.W., Moglen, G.E., Evenson, G. 2018. Assessing the cumulative impacts of geographically isolated wetlands on watershed hydrology using the SWAT model coupled with improved wetland modules. Journal of Environmental Management. 223:37-48. https://doi.org/10.1016/j.jenvman.2018.06.006.
Knipper, K.R., Kustas, W.P., Anderson, M.C., Alfieri, J.G., Prueger, J.H., Hain, C., Gao, F.N., Yang, Y., McKee, L.G., Nieto, H., Hipps, L., Aisha, M., Sanchez, L. 2018. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science. https://doi.org/10.1007/s00271-018-0591-y.
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Song, L., Liu, S., Kustas, W.P., Nieto, H., Sun, L., Xu, Z., Skaggs, T.H., Ma, M., Xu, T., Tang, X., Li, Q. 2018. Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river the basin scale. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.10.002.
Kustas, W.P., Agam, N., Alfieri, J.G., McKee, L.G., Prueger, J.H., Hipps, L., Howard, A., Heitman, J. 2018. Below canopy radiation divergence in a vineyard – implications on inter-row surface energy balance. Irrigation Science. https://doi.org/10.1007/s00271-018-0601-0.
Kustas, W.P., Alfieri, J.G., Nieto, H., Wilson, T.G., Gao, F.N., Anderson, M.C. 2018. Utility of the Two-Source Energy Balance (TSEB) model in vine and interrow flux partitioning over the growing season. Irrigation Science. https://doi.org/10.1007/s00271-018-0586-8.
Nieto, H., Kustas, W.P., Alfieri, J.G., Feng, M., Hipps, L., Los, S., Prueger, J.H., McKee, L.G., Anderson, M.C. 2018. Impact of different within-canopy wind attenuation formulations on modelling evapotranspiration using TSEBm. Irrigation Science. https://doi.org/10.1007/s00271-018-0611-y.
Andreu, A., Kustas, W.P., Polo, M., Carrara, A., Gonzalez-Dugo, M. 2018. Modelling surface energy fluxes over a dehesa (oak savanna) ecosystem using a thermal based two source energy balance model (TSEB) I. Remote Sensing. https://doi.org/10.3390/rs10040567.
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Agam, N., Kustas, W.P., Alfieri, J.G., Gao, F.N., Mckee, L.G., Prueger, J.H., Hipps, L. 2019. Grass intercrop and soil water content have a secondary effect on soil heat flux (SHF) in a wine vineyard – implications on SHF measurements. Irrigation Science. https://doi.org/10.1007/s00271-019-00634-6.
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Sharifi, A., Lee, S., McCarty, G.W., Lang, M., Jeogn, J., Sadeghi, A.M., Rabenhorst, M. 2019. Enhancement of APEX model to assess effectiveness of wetland water quality benefits. Water. 11(3):606. https://doi.org/10.3390/w11030606.
Nearing, G., Yatheendradas, S., Crow, W.T., Zhan, X., Liu, J., Chen, F. 2018. The efficiency of data assimilation. Water Resources Research. 54(9):6374-6392. https://doi.org/10.1029/2017WR020991.
Al-Yaari, A., Ducharne, A., Crow, W.T., Cheruy, F., Wigneron, J. 2019. Space-borne microwave surface soil moisture observations provide missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States. Scientific Reports. 9:1657. https://doi.org/10.1038/s41598-018-38309-5.
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Qui, J., Wagner, W., Zhao, T., Crow, W.T. 2019. Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing. International Journal of Applied Earth Observation and Geoinformation. 80:47-57. https://doi.org/10.1016/j.jag.2019.03.015.
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