Remote Sensing, Hydrology, and Field Experiments II
Presiding: M H Cosh, USDA/ARS Hydrology and Remote Sensing Laboratory; B Mohanty, Texas A&M University
H34A-01 15:30h
Tracking Freshwater from Space
River discharge as well as lake and wetland storage of water are critical elements of land surface hydrology, yet they are poorly observed globally and the prospects for improvement from in-situ networks are bleak. Considering this, a NASA Surface Water working group has been focused on answering the following science and applications questions: (1) What are the observational and data assimilation requirements for measuring natural and manmade surface storage and river discharge that will allow us to (a) understand the land surface branch of the global hydrologic cycle, (b) predict the consequences of global change, and (c) make assessments for water resources management? (2) What are the roles of wetlands, lakes, and rivers (a) as regulators of biogeochemical and constituent cycles (e.g., carbon, nutrients, and sediments) and (b) in creating or ameliorating water-related hazards of relevance to society? Global models of weather and climate could be constrained spatially and temporally by stream discharge and surface storage measurements. Yet this constraint is rarely applied, despite weather and climate modeling results showing that predicted precipitation is often inconsistent with observed discharge. Thus, as satellite missions are developed for global observations of critical hydrologic parameters such as soil moisture (i.e., HYDROS) and precipitation (i.e., GPM), the lack of concomitant measurements of runoff and surface water storage at compatible spatial and temporal scales may well result in inconsistent parameterizations of global hydrologic, weather, and climate models. Fortunately, several spaceborne methods have provided potential avenues toward answering these hydrologic questions. Among the most promising are active radar and lidar methods that measure inundation area, water heights, and changes. For example, radar altimetry is well known for its ability to measure ocean surface topography and such methods should be easily adaptable to inland waters. The global observations possible from such platforms will have important implications for global water cycle research. Future directions for the SWWG include expanding our scientific interests beyond water mass-balance and hydrodynamics. Issues regarding water quality and water management - even on global scales - are becoming more important. Sediment transport remains a fundamental science goal for many, especially considering the increased efforts toward river and wetland restoration. Hydrologic modeling and remote sensing efforts that connect each of these topics should be a greater focus within the SWWG. Everyone is most welcome to join us in these endeavors.
http://www.geology.ohio-state.edu/swwg
H34A-02 15:45h
Modeling Groundwater Depth in the Mississippi Delta Using Weather and ASTER Satellite Data
Groundwater resources are often used for irrigating agricultural lands, urban and rural water supplies, and recreational and industrial purposes. In order to plan and regulate the groundwater usage in a sustainable manner, it is necessary to estimate depth to groundwater at a higher resolution in a region. In general, these depths are available only for a limited number of well locations. In this paper, we develop a model for predicting depth to ground water in the Mississippi Delta using variables derived from weather and satellite data. The Mississippi Delta is one of the six geomorphic regions in Mississippi; the other regions are the Coastal Plains, Valley Silty Uplands, Blackland Prairie, Gulf Coast Marsh, and Eastern Gulf Coast Flatwoods. This Delta encompasses approximately 35,000 sq. miles of agricultural lands in 19 of 82 counties of Mississippi. The soils in the Delta are rich in organic matter and are predominantly used for crops, such as cotton, soybean, rice, and corn. The data requirement to develop the model included groundwater depth, precipitation, elevation, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data for the study area. We collected ground water depth for various well locations from the Mississippi Department of Environmental Quality, elevation and ASTER data from the United States Geological Survey (USGS), and precipitation data for various weather stations from the National Climate Data Center. Using ASTER data (in visible and infrared bands) a vegetation index was developed and used as a variable in the model. Using a geographic information system, geospatial interpolation techniques (inverse distance weighted, local polynomial, global polynomial, radial basis function, and kriging) were applied, and a continuous surface showing groundwater depth was created using the ordinary kriging which produced minimum error. The following data were tabulated for the weather station locations: interpolated groundwater depth, elevation, precipitation, and ASTER-based vegetation index. The digital elevation data had a 30 m x 30 m resolution and did not require spatial interpolation. Finally, a regression analysis was performed between groundwater depth (response variable) and elevation, precipitation, and ASTER-derived vegetation index (explanatory variables) to develop the model to predict depth to groundwater for the Mississippi Delta.
H34A-03 16:00h
Time-Evolution Statistics of GPS Surface Reflectivity Following Precipitation in Desert and Cultivated Environments
Use of the L-band Global Positioning System (GPS) forward-reflected signal for remote sensing of surface moisture shows promise as a new and complimentary method to more developed backscattering and emission techniques. The ratio of reflected-to-direct GPS signals provides a measurement of surface reflectivity. At present, due to the relatively high incident angles, embedded range code, and forward reflection of the signal, the reflected GPS signal statistical characteristics as a function of soil type, vegetation, precipitation amount, and drying time are not well known. The objective of this analysis is to examine the effect of precipitation amount and surface characteristics on the distribution of GPS surface reflectivity as a function of time following rain events over both a desert environment in New Mexico and cultivated areas near Ames, Iowa (Soil Moisture Experiments 2002). Reflectivity data from New Mexico and Iowa were collected using an airborne GPS Remote Sensor (GPSRS)/reflectometer. Iowa data sets include measurements over both sparsely and heavily vegetated areas. Reflectivity measurements from Iowa are examined with respect to precipitation amount, vegetation coverage (i.e. leaf-area-index), and measured surface soil moisture. A fuller understanding of variations in GPS reflectivity distribution and parametric form due to precipitation and drying time, over various soil/terrain types, is necessary for the development of GPS-based soil moisture retrieval algorithms that are specific to the surface type and environmental conditions.
http://centauri.larc.nasa.gov/gps/home.htm
H34A-04 16:15h
Frequency- and Time-Domain Measurement of Bare Soils and Wheat Canopy Using Monostatic Horn Antenna GPR - Implications and Applications for Radar Remote Sensing
GPR with a suspended 1 GHz horn antenna was deployed in the greenhouse and field for measurement of near-surface hydrologic parameters over bare and wheat canopy covered soils. Analyses of time-domain (TD) surface reflections (SR) and signal propagation times (PT) to reflective layers underlying plant canopy or a soil layer showed that SR values progressively decreased with increasing canopy biomass according to Beer-Lambert type relationships, while PT values remained unaffected. TD GPR measurements also showed the effects of soil texture on water content dynamics. Conversions of GPR reflections from TD to frequency domain (FD) showed differing dynamic responses for scattering from specific canopy and soil elements and entire profiles for varying soil types and conditions at 1.26 GHz (L-band) and 430 MHz (P-band), frequencies which are used by air- and spaceborne radar platforms. In general, L-band was more affected by surface (0-1 cm depth) soil water content conditions and vegetation than P-band, as expected. This demonstrates the usefulness of a 1 GHz center frequency horn antenna GPR for (1) characterization of vegetation canopy effects and subcanopy water content measurements within a well-defined footprint, and (2) calibration and verification of radar imagery using a single sensor.
H34A-05 16:30h
Rapid Mapping of Soil Electrical Conductivity by Radar Satellite Remote Sensing for Landmine Detection
Many soil physical and chemical properties interfere with landmine detector signals. It has been shown that prior knowledge of the distribution of these properties would allow appropriate technology selection and increased demining operation effectiveness/efficiency. For this reason, economic and rapid mapping techniques using remote sensing for these properties over wide areas are considered. Since soil electrical conductivity (EC) interferes with the most widely used landmine detection systems, such as metal detectors and ground penetrating radar, it has been proposed to start with developing a rapid mapping technique for EC using remote sensing. Although airborne, ground EM systems, and laboratory analyses are proven methods for mapping EC, they generally lack the appropriate resolution required. In addition surveys by such methods are costly and time consuming for mapping large areas such as entire countries. Therefore, EC prediction by satellite imaging of soil moisture change using RADARSAT is being tested in eastern Alberta (Canada) and northern Mississippi (U.S.A.). Areas of little soil moisture change with time can be associated with high moisture retention and higher clay content, suggesting an association with higher EC. However, use of airborne and ground EM systems and laboratory analyses are recommended for validation of EC distributions mapped by remote sensing. Fusion of RADARSAT soil moisture images at varied dates are used to identify boundaries between high and low moisture retention areas in both the northern Mississippi and Alberta test sites to predict areas of high and low EC. These predictions are being validated by ground EM surveys, laboratory analyses and, in some cases, by various other methods such as soil and military traficability maps. Soil sample are collected across the high-low EC boundaries for laboratory analyses. Laboratory analyses consist of soil texture/mineralogy, moisture versus spectral EC, and strength tests.
H34A-06 16:45h
An Integrated Atmospheric and Hydrological Based Malaria Epidemic Alert System
Malaria is a growing global threat, with increasing morbidity and mortality. In India there have been >40 epidemics in the last five years, in part due to abnormal meteorological conditions as well as the buildup of an immunologically native population. In most parts of India, periodic epidemics of malaria occur every five to seven years. Malaria epidemics are serious national/regional health emergencies, occurring with little or no warning where the public health system is unprepared to respond to the emerging problem. However, epidemic conditions develop over several weeks, theoretically allowing time for preventative action. The study area for the proposed research is located in Mewat, south of Delhi. It is estimated that 90% of the malaria burden is influenced by environmental factors, so that successful malaria intervention approaches must be adapted to local environmental conditions. Of particular importance are air and water temperature, relative humidity, soil moisture, and precipitation. Extreme climatic conditions prevail in Mewat, with uneven topography, 450mm average annual rainfall in 25 to 35 days, high temperature variability in different seasons, low relative humidity. Automated surface measurements are obtained for temperature, relative humidity, water temperature, precipitation and soil moisture. The Regional Atmospheric Modeling System (RAMS) is used to predict these variables over the spatial domain which are used in dynamic hydrological models to yield the parameters important to malaria transmission, including surface wetness, mean water table depth, percent surface saturation and total surface runoff. The locations of saturated surface regions associated with mosquito breeding sites near populated regions, along with water temperature, and then are used to determine larvae development and mosquito abundance. ASTER, LANDSAT and MODIS imagery are used to retrieve soil moisture, vegetation indices and land cover types. Pan-sharpened 1m spatial resolution QuickBird data has been used to identify small mosquito breeding sites with an accuracy of 90 %, as verified by ground observations. These layers of information, along with a 30m resolution Digital Elevation Model and field measurements of malaria incidence, larvae and mosquito counts, were examined in a GIS system to identify the environmental parameters effective in mosquito distribution. The Genetic Algorithm for Rule Set Production (GARP) has been applied to the region using the parameters defined above to predict regions susceptible to malaria transmission.