The demands for bare-earth or ground-level digital elevation models (DEMs) are significant for various environmental and ecological studies. As one of the most widely used global DEMs, the Shuttle Radar Topography Mission (SRTM) DEM is available to the public. However, the SRTM DEM is not a bare-earth DEM because it includes man-made structures and vegetation. The objective of this paper is to develop a mathematical morphology-based approach to generate ground-level DEM (GLD) from the SRTM DEM in forest environments. The proposed algorithm is implemented as follows. First, an initial GLD is derived from the SRTM DEM by using morphological operations with a single structuring element. Second, homogeneous forest patches are generated by applying watershed segmentation to pseudocanopy height (PCH) that is obtained by subtracting the initial GLD from the SRTM DEM. Based on segmented patches, a refined GLD is derived by using morphological operations with adaptive structuring elements of different sizes. Third, PCH is updated with the refined GLD and then resegmented into forest patches, from which a final GLD is obtained by subtracting the updated mean PCH from the SRTM DEM. Finally, bare-earth DEMs from the National Elevation Data Set (NED) are used to validate the extracted GLD. The results show that the root mean square error of the final GLD compared with the NED is significantly reduced for two study sites. This type of GLD would be applicable to large-scale environmental studies where accurate topographical information is not available.

River bathymetry plays a key role in estimating river discharge as well as improving our modeling capabilities of fluvial geomorphology. Genetic algorithm techniques can be used to derive river bed topography using only water surface elevations and the corresponding temporal and spatial rates of change. Each river bathymetric estimate, also referred to as a solution, is modeled as a successive collection of cross-sections, also referred to as genes. An initial population of potential solutions is created from solutions comprised of random cross-sections with depths ranging as deep as the surface height to no depth at all. The population is successively evolved by randomly mutating the depths at a random number of cross-sections. The solutions are selected for the next generation by evaluating their individual fitness. Three varieties of fitness tests are applied to each potential solution: Saint-Venant's 1-D flow equation, flow continuity, and the statistical power-law relationships suggested by Leapold & Maddock[1]. In this manner, an ideal solution is derived.

The measurement of spatially distributed water surface elevation (WSE) at the global scale, which is planned by "Surface Water and Ocean Topography (SWOT) mission", will bring lots of fresh knowledge on global-scale terrestrial water circulation especially when observed WSE is merged into global hydrodynamics modes by data assimilation. In this paper, we discussed the predictability of WSE by a global hydrodynamics model, which is required as a dynamic core for global-scale data assimilation using SWOT. From the comparison against WSE observed by the altimeter on Envisat, we found that the up-to-date global hydrodynamics model is able to reproduce the seasonal variations of WSE in the Amazon River, though predicted WSE still has errors due to the uncertainties in topographic parameters and sub-grid-scale dynamics of the model. Reduction of these uncertainties is required along with the development of the algorithm itself for achieving global scale data assimilation of WSE.

The elevation of the ocean surface has been measured for over two decades from spaceborne altimeters. However, existing altimeter measurements are not adequate to characterize the dynamic variations of most inland water bodies, nor of ocean eddies at scales of less than about 100 km, notwithstanding that such eddies play a key role in ocean circulation and climate change. For terrestrial hydrology, in situ and spaceborne measurements of water surface elevation form the basis for estimates of water storage change in lakes, reservoirs, and wetlands, and of river discharge. However, storage in most inland water bodies, e.g., millions of Arctic lakes, is not readily measured using existing technologies. A solution to the needs of both surface water hydrology and physical oceanography communities is the measurement of water elevations along rivers, lakes, streams, and wetlands and over the ocean surface using swath altimetry. The proposed surface water and ocean topography (SWOT) mission will make such measurements. The core technology for SWOT is the Ka-band radar interferometer (KaRIN), which would achieve spatial resolution on the order of tens of meters and centimetric vertical precision when averaged over targets of interest. Average revisit times will depend upon latitude, with two to four revisits at low to mid latitudes and up to ten revisits at high latitudes per$\sim$20-day orbit repeat period.

Frequently large synthetic aperture radar (SAR) mosaics are not precisely georeferenced because topographic distortions are not removed during the mosaicking process due to the lack of adequate digital elevation models (DEMs). The Shuttle Radar Topography Mission (SRTM) has recently provided high-resolution DEM data with nearly global coverage and makes it possible to rectify SAR mosaics. Though techniques are available for rectifying individual scenes of SAR imagery using DEM data, these methods encounter difficulties when rectifying SAR mosaics because abrupt geometric discontinuities occur in SAR mosaics at scene boundaries. This paper introduces an automated method to removing topographic distortions from SAR mosaics and producing orthorectified mosaics, without accessing original SAR images. The procedures include SAR image simulation from DEMs, two-staged image matching between SAR mosaics and the simulated image, automated tie-point derivation and screening, piecewise image rectification for localized adjustment, and production of orthorectified mosaics. The method is used to orthorectify both high-water and low-water Global Rain Forest Mapping project SAR mosaics covering the entire Amazon basin. Validation results show that one-pixel (i.e., 92 m) positioning accuracy (root mean square error) was achieved in both cases, compared to 14-16 pixel errors (i.e., 1288-1472 m) of the original mosaics.

The authors find that interferometric processing of repeat-pass L-HH-band shuttle imaging radar (SIR-C) data reveals centimeter-scale changes in the elevations of water surfaces within flooded vegetation. Because radar pulses reflect specularly from the water surface, interferometric observations of open water are incoherent. However, within flooded forests and inundated shrubs, the L-band radar pulse penetrates the vegetation canopy and follows a double-bounce travel path that includes the water and vegetation-trunk surfaces. In these environments, the returned radar energy and associated phase coherence are both stronger than the surrounding nonflooded terrain, permitting determination of the interferometric phase. Phase errors related to atmospheric water vapor are usually longer in wavelength and spatially distinct from phase signatures related to stage changes in tributaries and floodplain lakes. The interferometrically measured stage decreases match gauge data, providing further verification. Water level changes across 150 m to 2.75 km-wide water bodies containing inundated vegetation can be reliably measured. The authors' results suggest that if future interferometric L-HH-band SAR missions are implemented with short temporal baselines, it is possible to measure the hydrologic response of wetlands and inundated floodplains to changes in mainstem water level.