Andreas Wiesmann

Interferometric point target analysis for deformation mapping

Interferometric Point Target Analysis (IPTA) is a method to exploit the temporal and spatial characteristics of interferometric signatures collected from point targets to accurately map surface deformation histories, terrain heights, and relative atmospheric path delays. In this contribution the IPTA concept is introduced, including the point selection criteria, the phase model and the iterative improvement of the model parameters. Intermediate and final results of an IPTA example using a stack of ERS-1 and ERS-2 data, confirm the validity of the concept and indicate a high accuracy of the resulting products.

Microwave emission model of layered snowpacks

Abstract A thermal microwave emission model of layered snowpacks (MEMLS) was developed for the frequency range 5–100 GHz. It is based on radiative transfer, using six-flux theory to describe multiple volume scattering and absorption, including radiation trapping due to total reflection and a combination of coherent and incoherent superpositions of reflections between layer interfaces. The scattering coefficient is determined empirically from measured snow samples, whereas the absorption coefficient, the effective permittivity, refraction, and reflection at layer interfaces are based on physical models and on measured ice dielectric properties. The number of layers is only limited by computer time and memory. A limitation of the empirical fits and thus of MEMLS is in the range of observed frequencies and correlation lengths (a measure of grain size). First model validation for dry winter snow was successful. An extension to larger grains is given in a companion article (Matzler and Wiesmann, 1999) . The objective of the present article is to describe and illustrate the model and to pave the way for further improvements. MEMLS has been coded in MATLAB. It forms part of a combined land-surface-atmosphere microwave emission model for radiometry from satellites (Pulliainen et al., 1998) .

Extension of the Microwave Emission Model of Layered Snowpacks to Coarse-Grained Snow

The microwave emission model of layered snowpacks (MEMLS) is a multilayer and multiple-scattering radiative transfer model developed for dry winter snow using an empirical parametrization of the scattering coefficient (se the copanion article). A limitation is in the applicable range of frequencies and correlation lengths. In order to extend the model, a physical determination of the volume-scattering coefficients, describing the coupling between the six fluxes, is developed here, based on the improved Born approximation. An exponential spatial autocorrelation function was selected. With this addition, MEMLS obtains a complete physical basis. The extended model is void of free parameters. The validation was done with two types of experiments made at the alpine test site, Weissfluhjoch: 1) radiometry at 11 GHz, 21 GHz, 35 GHz, 48 GHz, and 94 GHz of winter snow samples on a blackbody and on a metal plate, respectively, and 2) radiometric monitoring at 4.9 GHz, 10.4 GHz, 21 GHz, 35 GHz, and 94 GHz of coarse-grained crusts growing and decaying during melt-and-refreeze cycles. Digitized snow sections were used to measure snow structure in both experiments. The coarsest grains were found in the refrozen crusts with a correlation length up to 0.71 mm; the winter snow samples had smaller values, from 0.035 mm for new snow to about 0.33 mm for depth hoar. Good results have been obtained in all cases studied so far.

An advanced system for the automatic classification of multitemporal SAR images

A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis.

Large-Scale Mapping of Boreal Forest in SIBERIA using ERS Tandem Coherence and JERS Backscatter Data

Siberias boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km2 in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m3/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.

Radiometric and structural measurements of snow samples

The interaction of microwaves with the natural snow cover strongly depends on the complex structure of the snowpack. In order to quantify this dependency, dedicated experiments were performed with homogeneous slabs of dry, natural snow samples measured over a frequency range from 11 to 94 GHz. A new method introduced by Matzler and Wegmuller [1995] and Weise [1996a] for determining the scattering and absorption behavior of test samples was applied and further developed by application of a multiple scattering model. Homogeneous samples of dry snow were (1) investigated using a set of portable, linearly polarized Dicke radiometers at frequencies of 11, 21, 35, 48 and 94 GHz, (2) characterized by temperature, grain size and shape, density and permittivity, and (3) structurally analyzed by digitized snow sections in order to obtain statistical information of the snow structure i.e. the autocorrelation function. During the winters 1994/1995 and 1995/1996 additional measurements of snow samples were made to extend the variability of the investigated snow types. Up to now, 20 samples, representing alpine snow in winter (that is, without melt metamorphism) have been collected during three winter campaigns. Here, we present the method and the radiative transfer model and show how it can be inverted to obtain scattering and absorption coefficients. A first assessment of the snow sample data is also presented. The results show good agreement between the measured and the theoretical absorption coefficient. The scattering coefficient turns out to be a strong function of frequency and correlation length as expected from Rayleigh scattering. However, distinct differences can be noted.

JERS SAR interferometry for land subsidence monitoring

In this paper, the potential of L-band repeat-pass differential synthetic aperture radar (SAR) interferometry for land subsidence monitoring is evaluated using Japanese Earth Resources Satellite (JERS) SAR data. Bologna, Mexico City, and the Ruhrgebiet are selected as application sites representing slow to fast deformation velocities. The investigation includes feasibility aspects such as data availability, the temporal decorrelation over different landcover classes and the range of useful spatial baselines, an analysis of the achieved deformation accuracy, and considerations on the complementarity to European Remote Sensing satellite (ERS) SAR interferometry and leveling surveys. In spite of the rather limited data availability, land subsidence maps could be generated for the three selected application sites. In contrast to ERS C-band SAR data, JERS L-band interferometry permitted the retrieval of subsidence values over vegetated areas and forest when using interferograms of less than one year acquisition time interval and short baseline. In addition, the longer L-band wavelength was found to be superior in the case of large deformation gradients that lead to phase-unwrapping problems in C-band interferometry.

ELBARA II, an L-band radiometer system for soil moisture research.

L-band (1–2 GHz) microwave radiometry is a remote sensing technique that can be used to monitor soil moisture, and is deployed in the Soil Moisture and Ocean Salinity (SMOS) Mission of the European Space Agency (ESA). Performing ground-based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission is important for validating the satellite data and for the further improvement of the radiative transfer models used in the soil-moisture retrieval algorithms. To address these needs, three identical L-band radiometer systems were ordered by ESA. They rely on the proven architecture of the ETH L-Band radiometer for soil moisture research (ELBARA) with major improvements in the microwave electronics, the internal calibration sources, the data acquisition, the user interface, and the mechanics. The purpose of this paper is to describe the design of the instruments and the main characteristics that are relevant for the user.

Ionospheric Electron Concentration Effects on SAR and INSAR

The launch of ALOS and the high potential expected for the L-band PALSAR motivated us to investigate ionospheric electron concentration effects using JERS L-band SAR data acquired at high latitudes. An important focus of our work was on the identification of ionospheric effects and resulted in methodologies to detect ionospheric effects in single SAR acquisitions as well as in repeat-orbit pairs. For cases where significant ionospheric anomalies are present, procedures to improve SAR offset tracking and interferometric results are proposed and the retrieval of free electron density maps is discussed.

Multitemporal JERS repeat-pass coherence for growing-stock volume estimation of Siberian forest

Multitemporal radar data from the Japanese Earth Resources Satellite (JERS) satellite from the period 1993 to 1998 have been used to investigate if L-band interferometric coherence with a 44-day temporal baseline is suitable for estimations of growing-stock volume in boreal forest. Two forest regions north of Krasnoyarsk in Siberia have been used as test areas. Seasonal variations in the repeat-pass coherence have been studied, and a comparison with C-band coherence from the European Remote sensing Satellite 1 and 2 (ERS-1/2) tandem missions in 1997 and 1998 has been done. JERS coherence from the winter shows a clear correlation with the forest growing-stock volume. For the summer scenes, the spread in the values is too large to give reliable results. Acquisitions from the spring and fall show large problems with decorrelation caused by temporal changes. The results indicate potential of repeat-pass interferometric L-band coherence in winter, as will be provided by the forthcoming Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) to map growing-stock volume in Siberia and boreal forests.