A. James Clark School of Engineering

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Subject: search.filters.subject.Brightness Temperature

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    EVALUATION OF THE NASA MICROWAVE RADIATIVE TRANSFER MODEL FOR SOIL MOISTURE ESTIMATION USING AQUARIUS BRIGHTNESS TEMPERATURE OBSERVATIONS OVER THE CONTINENTAL UNITED STATES
    (2014) Tarik, Saad Bin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The implications of near-surface soil moisture (~5 cm) variability in land surface processes and land-atmosphere interactions is important in regional and global scale climatology since it controls the partitioning of precipitation and radiation fluxes that play a crucial role in dictating weather and climate. Passive microwave (PMW) remote sensing is an increasingly popular approach to measure soil moisture because of its global coverage of the Earth. This study evaluates the performance of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) radiative transfer model (RTM) using Aquarius brightness temperature (Tb) observations with the eventual goal of integrating the RTM into a data assimilation (DA) framework for the purpose of improved soil moisture estimation. Statistics were calculated from two plus years of observations across different climate regions of the United States. Seasonal variations of soil moisture were also investigated. Results suggest the RTM reasonably reproduces Aquarius Tbs, but that systematic biases exist, which must be mitigated prior to inclusion into the DA framework.
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    Predicting Water Table Fluctuations Using Artificial Neural Network
    (2008-11-17) Wu, Chung-Yu; Shirmohammadi, Adel; Fischell Department of Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Correctly forecasting groundwater level fluctuations can assist water resource managers and engineers in efficient allocation of the regional water needs. Modeling such systems based on satellite remotely sensed data may be a viable option to predict water table fluctuations. Two types of water table prediction models based on Artificial Neural Network (ANN) technology were developed to simulate the water table fluctuations at two well sites in Maryland. One was based on the relationship between the variations of brightness temperature and water table depth. The other one was based on the relationship between the changes of soil moisture and water table depth. Water table depths recorded at these two wells, brightness temperature retrieved from the Advanced Microwave Scanning Radiometer, and soil moisture data produced by the Land Data Assimilation System were used to train and validate the models. Three models were constructed and they all performed well in predicting water table fluctuations. The root mean square errors of the water table depth forecasts for 12 months were between 0.043m and 0.047m for these three models. The results of sensitivity test showed that the models were more sensitive to the uncertainty in water table depth than to that in brightness temperature or in soil moisture content. This suggests that for situations where high resolution remotely sensed data is not available, an ANN water table prediction model still can be built if the trend of the time series of the data, such as brightness temperature or soil moisture, over the study site correlates well with the trend of the time series of the ground measurement at the study site. An extension of the study to a regional scale was also performed at 12 available well sites in Piedmont Plateau, Maryland. Hydrologic soil types, LDAS soil moistures, and water table depths at these locations were used in the ANN modeling. The root mean square error of one month long water table depth forecast was 0.142m. However, the accuracy of the monthly forecast decreases with the increase of time. A further study to improve the accuracy of long-term water table fluctuation forecast is recommended.