Location: Hydrology and Remote Sensing Laboratory2015 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.
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.
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).
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