IEEE International Geoscience and Remote Sensing Symposium

Channel 2 of the 9 Microwave Sounding Units (MSUs) flown on NOAA polar orbiting platforms provides a 23-year time series of middle-tropospheric temperature. These measurements may be of sufficient quality for climate studies if intersatellite calibration offsets and drifts can be accurately characterized and removed. One of the most important and difficult to characterize sources of long-term drift in the data is due to the evolution of the local observing time due to slow changes in the orbital parameters of each NOAA platform, which can alias diurnal temperature changes into the long-term time series. To account for this effect, we have constructed monthly diurnal climatologies of MSU Channel 2 brightness temperature using the hourly output of a general circulation model as input for a microwave radiative transfer model. We report the results of this calculation, and validate the result by comparing with MSU observations.

During the last decade of the 20th century, Turkey was shocked with numerous disasters. As a result, central government or Turkey has decided to settle a national Crises Management Center. One of the main responsibilities of this center is to predict, track and minimize the effects of disasters in any part of the country. GIS-based disaster simulators are currently being developed for this purpose. In this paper, a subset of those simulators will be reviewed. Some technical details of the simulations, basic underlying models of simulations, and extent of required RS data will be presented.

We formulate an approach to reconstruct surface circulation from a high-frequency (HF) radar data in open boundary areas. The approach uses flow decomposition through two scalar potentials, the self-consistent open boundary conditions, the cost function (penalty) for the validation of free model input and selective filter. The nowcast skill is enhanced through an ensemble averaging technique. The current nowcast in Monterey Bay was realized through the approach for August 1994. We demonstrate it has weak sensitivity to level of noise in the observations. The nowcast gives better understanding of inshore circulation, eddy activities and upwelling processes.

Spatial scaling is of particular importance in remote sensing applications to terrestrial ecosystems where spatial heterogeneity is the norm. Surface parameters derived at different resolutions can be considerably different even though they are derived using the same algorithms or models. This article addresses issues related to spatial scaling of net primary productivity (NPP). The main objective is to develop algorithms for spatial scaling of NPP using subpixel information. NPP calculations at 30 m and 1km resolutions were performed using the Boreal Ecosystem Productivity Simulator (BEPS). The area of interest is near Fraserdale, Ontario. It is found from this investigation that lumped (coarse resolution) calculations can be considerably biased (up to 64 %) from distributed (fine resolution) case, suggesting that global and regional NPP maps can be biased by the same amount if surface heterogeneity within the mapping resolution is ignored. The bias is negative when conifer-labeled pixels contain considerable deciduous forests. Due to relatively high and variable NPP values of open land areas with growing grasses, the bias is negative when deciduous-labeled pixels are mixed with open land. There is no trend between the biasness and open land fractions within conifer-labeled pixels. Based on these results, algorithms for removing these biases in lumped NPP are developed using subpixel land cover information.

Here we consider the use of model-based methods for the detection of buried objects from a sequence of synthetic aperture images obtained by a radar sensor moving linearly down a track. The scattering physics of the underlying sensing modality cause the relevant target signatures to change in a complex yet predictable manner from one image to the next. To arrive at a tractable processing scheme that exploits these motion-induced changes, we develop a flexible parametric model capable of capturing the full variation of these signatures. A detection scheme based on a principal components analysis of estimated model vectors is then derived. Results are demonstrated using field data from a forward-looking sensor.

A structural geological study has been carried out by automatic and visual interpretation of Landsat 7 imagery. The new improved features of ETM+ imagery are tested in the southern part of the Apennines mountain chain, which is characterized by several inverse faults and overthrusting. Spatial information is crucial for structure detection; nevertheless spectral data can also help in the geological interpretation of optical images. In order to combine the spatial and spectral information, panchromatic and multispectral images were fused in synergetic imagery. A lineament analysis was accomplished by visual interpretation and additional processing techniques such as edge detection and morphologic filtering. The combination of different analytical techniques enables the production of a lineament map of the study area. A spatial statistic analysis of the lineaments was performed to analyze their frequency and main direction. The structural geological interpretation of remotely sensed data was compared to the field data collected over some sample areas and structural geological studies carried out by different authors. The features of geological interest detected during the interpretation process were digitized using a raster-based GIS software. A preliminary vector structural geological map was produced.

In May 1999 the SMOS Earth Explorer mission was selected by ESA to provide global soil moisture and sea salinity maps. SMOS payload is MIRAS: an L-band 2D aperture synthesis interferometric radiometer, that generates brightness temperature images by processing the cross-correlations measured between the pairs of signals collected by each array element. In this paper we compare the results of the error budget used in previous SMOS studies to those obtained from a simulation campaign for different scenarios (brightness temperature input scenes). The total radiometric error, which includes the radiometric sensitivity term mainly due to the finite integration time, and the radiometric accuracy due to instrumental imperfections as well as residual calibration error, is computed from a series of 20 Monte Carlo simulations for each instrument configuration and scenario for both the brightness temperatures retrieved in the antenna reference frame (X- and Y-polarizations) and the Earth reference frame (V- and H- polarizations at each pixel). Finally, intercalibration instrumental drifts due to in-orbit temperature variations are analyzed using a standard configuration.

To calibrate synthetic aperture radar (SAR) images to normalized radar cross sections (NRCS) a calibration constant is required. Usually the calibration constant is determined by analyzing measurements of corner reflectors. However, due to the high costs there are only a very limited number of corner reflectors available. In this paper a new method for estimating the calibration constant on the basis of a few days of SAR data is introduced. The method is based on knowledge of the dependency of the NRCS on the ocean surface wind, which is described by well-tested empirical C-band models, e.g., CMOD4 and CMOD IFR2. Given the mean wind vector at each SAR wave mode image both models enable to derive the mean NRCS of the image. Application of the method is demonstrated and validated utilizing a total of 34000 SAR imagettes and co-located winds from the European Centre for Medium range Weather Forecast and the ERS-2 scatterometer. The SAR imagettes were processed to ENVISAT ASAR-like single look complex SAR images using three weeks of SAR wave mode data. It is shown that the method is an ideal tool for retrieving the SAR calibration constant and is capable to monitor and estimate variations of the calibration constant, e.g., due to saturation of the SAR analogue to digital convertor or gain drifts.

The objective of this paper is to verify the viability of using existing radar satellite (ERS and RADARSAT), as an operational tool for monitoring land cover in Amazo/spl circ/nia. It is well known that C band radar data are not adequate for land applications, but as cloud cover in Amazo/spl circ/nia is a constant problem, particularly in certain areas, radar data can help, as complimentary information, for change monitoring. In this paper ERS and RADARSAT images are classified using texture measures, in several classes of land use, and then the adequacy of using these classes for change detection is analyzed. Progressive sequential feature selection, using the Kappa coefficient of agreement as a selection criterion, chooses a subset of the texture layers that maximizes that coefficient. It was observed that even for the best feature set, the Kappa coefficient was considered too low and unsuitable to be used for change detection. However, it is shown that this coefficient progressively increases when classes are merged sequentially. When only two classes are considered, identified as forest/non-forest, the overall accuracy is higher than 85%, which was considered adequate for change detection. The classifications of the 1992, 1993 and 1996 ERS1/2 images over the Tapajo/spl acute/s National Forest, Brazil, were performed using the iterative contextual mode (ICM) classifier. Deforestation was detected for those points changing from forest in one year to non-forest in other year, with very good agreement with the results obtained with optical imagery sequence. Similar results were obtained using RADARSAT imagery for the year 1996.

The Flood Information Management System (FIMS) represents the confluence of several cutting edge technologies and methodologies. The FIMS website combines real-time in situ data, internationally recognized hydraulic and hydrologic models, multiresolution remotely sensed images and a customized Web interface all within one coherent and easily accessible Internet location. The FIMS prototype development focused on two river basins with active river monitoring programs, the Fraser River in British Columbia and the St. John River in New Brunswick. Near-real-time sensors in both these river basins provide FIMS with its water quantity baseline data. Web-based software engineering provides the bridge between hydraulic/hydrologic models and the graphical user interfaces used to display the near-real-time geospatial results on the Internet. Users are able to interactively visualise Ikonos, Radarsat, and Landsat image maps of their areas, visualize hydrometric station locations and actual flow data, then run models that show flood hazard, risk and extents based on current and predicted flows.