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In order to apply hydrological models in the water resources investigation successfully, careful calibration and uncertainty analysis are required. Although many automatic calibration methods were developed, the time consumed for running the hydrologic model is still a problem for hydrologic modelers. To reduce the computational complexity and increase performance of the calibration procedure, a software package (Fast Automated Calibration Tool, FACT) that works on Soil and Water Assessment Tool (SWAT) was developed. Sequential Uncertainty Fitting Algorithm (SUFI-2) was chosen to build the software package on, because SUFI-2 combines optimization with uncertainty analysis and can handle a large number of parameters. SUFI-2 implemented in SWAT-CUP (a software program that was developed for SWAT) is very useful in an interactive manner, however; it has some drawbacks which are the time consumed, user interaction requirement and update problems of SWAT model files. In this study, the calibration procedure was implemented in a MATLAB script, which completes the full calibration in one single run. The developed tool was applied on Sarisu-Eylikler Basin SWAT model that had r(2) = 0.41, NSE = 0.11 between 1992 and 2010 without calibration. When the model was calibrated using SUFI-2 in SWAT-CUP, the model was improved to r(2) = 0.57, NSE = 0.44, P-factor = 0.69 and R-factor = 1.00 at the end of 42 iterations using 100 simulation counts in each iteration. However; when the developed calibration tool was applied with 250 simulation counts without any user interaction for each iteration, the model was improved to r(2) = 0.59, NSE = 0.57, P-factor = 0.72, and R-factor = 1.32 at the end of three iterations. Thus, the developed calibration procedure took a shorter time (unlike SUFI2 in SWAT-CUP, FACT takes several hours) compared to SWAT-CUP with minimum user involvement.

Hydrological models have been widely used for water resources management. Successful application of hydrological models depends on careful calibration and uncertainty analysis. Spatial unit of water balance calculations may differ widely in different models from grids to hydrological response units (HRU). The Soil and Water Assessment Tool (SWAT) software uses HRU as the spatial unit. SWAT simulates hydrological processes at sub-basin level by deriving HRUs by thresholding areas of soil type, land use, and slope combinations. This may ignore some important areas, which may have great impact on hydrological processes in the watershed. In this study, a hierarchical HRU approach was developed in order to increase model performance and reduce computational complexity simultaneously. For hierarchical optimization, HRUs are first divided into two-HRU types and are optimized with respect to some relevant influence parameters. Then, each HRU is further divided into two. Each child HRU inherits the optimum parameter values of the parent HRU as its initial value. This approach decreases the total calibration time while obtaining a better result. The performance of the hierarchical methodology is demonstrated on two basins, namely Sarisu-Eylikler and Namazgah Dam Lake Basins in Turkey. In Sarisu-Eylikler, we obtained good results by a combination of curve number (CN2), soil hydraulic conductivity, and slope for generating HRUs, while in Namazgah use of only CN2 gave better results.

Detection of region boundaries is a very challenging task especially in the presence of noise or speckle as in synthetic aperture radar images. In this work, we propose an interactive boundary detection technique which makes use of Bsplines and well-known powerful tools of information theory such as the Kullback-Leibler divergence (KLD) and Bhattacharyya distance. The proposed architecture consists of the following four main steps: (1) The user selects points inside and outside of a region. (2) Profiles that link these inside and outside points are extracted. (3) Boundary points that lie on the profile are located. (4) Finally, the B-splines that provide both elasticity and smoothness are used for connecting boundary points together to obtain an accurate estimate of the actual boundary. Existing work related to this approach are extended in two axes. First the use of multiple points both inside and outside of a region made possible to obtain a few times more boundary points. A tracking stage is proposed to put the boundary points in the right order and at the same time eliminate some of them that are erroneously detected as boundary points as well. Experiments were conducted using simulated and real SAR images.

Noise has to be taken into account in the algorithms of classification, target detection and anomaly detection. Recent studies indicate that noise estimation is also crucial in subspace identification of Hyper Spectral Images (his). Several techniques were proposed for noise estimation including: multiple linear regression based techniques, spectral unmixing and remixing etc. The noise in HSI is widely accepted to be a spatially stationary random process. But the variance of the noise varies from one wavelength to another. Two types of noise are considered: the first one is the circuitry noise (thermal noise) which is signal independent. The second one is the photonic noise (shot noise) which is signal dependent. The latter is considered to be the dominant one. A reliable way to accurately estimate the noise requires the identification of a large uniform region in the image. To this end, we propose a region growing technique. At the end of this process, a certain number of regions with different sizes and uniformities are obtained. The next step consists of identifying the most uniform region having the largest area. Once the most uniform and largest region of the scene is identified the next step is to apply an ideal low pass filter to this region. This yields an estimate of the noise-free data, hence the noise itself by calculating the difference. It is also possible to apply the well-known scatter plot technique. Experiments suggest that the proposed scheme produces comparable results to its competitors. A major advantage of the technique is the automated identification of an homogenous region.

Tuz Golu, a salt lake situated in the central Anatolia, which dries during summer resulting in a spatially homogeneous high reflective surface is one of the LANDNET Sites [4, 6]. As a relatively new site and with support from the European Space Agency (ESA) and Space Technologies Research Institute of The Scientific and Technological Research Council of Turkey (TUBITAK UZAY) it was decided that this would be a good candidate to carry out an international comparison of this nature. This paper provides an overview of the CEOS international comparison carried out at Tuz Golu in 13 - 27 August 2010.