Permanently shadowed regions (PSRs) at the poles of the Moon are potential reservoirs of trapped volatile species, including water ice. Knowledge of the distribution and abundance of water ice at the poles provides key scientific... more
Permanently shadowed regions (PSRs) at the poles of the Moon are potential reservoirs of trapped volatile species, including water ice. Knowledge of the distribution and abundance of water ice at the poles provides key scientific background for understanding the evolution of volatiles in the Earth-Moon system and for human exploration efforts. The Lunar Reconnaissance Orbiter Camera (LROC) acquired images of the terrain within PSRs to search for indications of water ice. In addition, the LRO Miniature Radio-Frequency (Mini-RF) instrument acquired S-band radar observations to further characterize these regions. Specifically, the m-chi decomposition was used to assess the distribution of materials within and around PSRs based on the type of backscatter. Double bounce backscatter is indicative of water ice, but could also be produced by randomly distributed blocks at the wavelength scale. To ascertain whether these signatures are due to water ice or blocks, we quantified the abundance of detectable blocks in areas with double-bounce backscatter using the LROC Narrow Angle Camera (NAC). Block populations were measured for a suite of craters with different ages, sizes, and radar characteristics. For fresh craters, a correlation between block size, block density and double-bounce backscatter was found. Within PSRs exhibiting double-bounce backscatter, no blocks were found. Additionally, no albedo variations were observed at PSRs, in contrast to observations of PSRs on Mercury. While the possibility of water ice in some lunar craters still exists, these results indicate that they are likely small-scale, and that the observed radar anomalies at PSR-bearing craters are most likely due to the presence of wavelength-scale blocks.
Archaeological remote sensing is not a novel discipline. Indeed, there is already a suite of geoscientific techniques that are regularly used by practitioners in the field, according to standards and best practice guidelines. However, (i)... more
Archaeological remote sensing is not a novel discipline. Indeed, there is already a suite of geoscientific techniques that are regularly used by practitioners in the field, according to standards and best practice guidelines. However, (i) the technological development of sensors for data capture; (ii) the accessibility of new remote sensing and Earth Observation data; and (iii) the awareness that a combination of different techniques can lead to retrieval of diverse and complementary information to characterize landscapes and objects of archaeological value and significance, are currently three triggers stimulating advances in methodologies for data acquisition, signal processing, and the integration and fusion of extracted information. The Special Issue "Remote Sensing and Geosciences for Archaeology" therefore presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. Site discovery, understanding of cultural landscapes, augmented knowledge of heritage, condition assessment, and conservation are the main research and practice targets that the papers published in this Special Issue aim to address.
This paper concerns the application of mathematical morphology operations for suppression of speckle from radar satellite images. Different traditional widely-used filters are compared with the morphological alternate filters, especially... more
This paper concerns the application of mathematical morphology operations for suppression of speckle from radar satellite images. Different traditional widely-used filters are compared with the morphological alternate filters, especially with the filter with multiple structuring function. The comprehensive comparison of the traditional filters is presented, as well as the background on mathematical morphology and morphological filters. Then the evaluation of compared filters applied on TerraSAR-X images is presented.
A bistatic X-band experiment was successfully performed early November 2007. TerraSAR-X was used as transmitter and DLR's new airborne radar system F-SAR, which was programmed to acquire data in a quasicontinuous mode to avoid echo... more
A bistatic X-band experiment was successfully performed early November 2007. TerraSAR-X was used as transmitter and DLR's new airborne radar system F-SAR, which was programmed to acquire data in a quasicontinuous mode to avoid echo window synchronization issues, was used as bistatic receiver. Precise phase and time referencing between both systems, which is essential for obtaining high resolution SAR images, was derived during the bistatic processing. Hardware setup and performance analyses of the bistatic configuration are presented together with first processing results that verify the predicted synchronization and imaging performance.
Recent advances in active microwave remote sensing techniques provide the potential for monitoring soil moisture conditions at the spatial and temporal scales required for detailed local modeling efforts. The goal of this research was to... more
Recent advances in active microwave remote sensing techniques provide the potential for monitoring soil moisture conditions at the spatial and temporal scales required for detailed local modeling efforts. The goal of this research was to produce accurate and spatially distributed estimates of soil moisture using a time series of ERS-2 images for the Konza Prairie, a tallgrass environment in northeast Kansas. The methods used in this research involve field data collection of soil moisture, digital image interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS-2) imagery, and environmental modeling in a raster GIS environment. To accomplish the research goals, the effect of variable terrain on radar image backscatter values was quantified and reduced. Next, the scattering behavior of the overlying vegetation canopy was simulated using a water cloud model that estimated the contribution of vegetation backscatter (sigma oveg) to the total backscatter coefficient (sigma ototal). Critical to this process were estimates of aboveground primary production made using the normalized difference vegetation index from a combination of AVHRR and LANDSAT TM images. With sigmao veg removed from the amount of backscatter contributed by the soil surface (sigmaosoil) was calculated and the linear relationship between sigmaosoil and volumetric soil moisture was determined. This regression model was then inverted and solved for volumetric soil moisture to quantify near surface soil moisture conditions across the study area. Local incidence angle had the strongest relationship on SAR image backscatter values (r = -0.35) and when used in an empirical correction function reduced image variance by at least 8%. Backscatter modeling to separate the vegetation and soil components of the radar signal performed worse than expected, resulting in a weak correlation between composite sigmao soil and volumetric soil water content (r = 0.21) and different values for burned and unburned watersheds (r = 0.09 and r = 0.32, respectively). Soil backscatter values were estimated without accounting for canopy and litter layer moisture conditions, causing a reduction in the effectiveness of the cloud model output. The model performed very well, however, on a day basis with single date correlations for burned and unburned watersheds being among the highest yet reported when using radar satellite data. While many studies have questioned the sensitivity of C-band radars, operating at moderate incidence angles, to near surface soil moisture conditions, results here show that the ERS-2 data are capable of monitoring soil moisture conditions over even dense natural vegetation characteristic of tallgrass prairie.
