Automated wetland delineation from multi-frequency and muliti-polarized SAR Images in high temporal and spatial resolution

Water scarcity is one of the main challenges posed by the changing climate. Especially in semi-arid regions where water reservoirs are filled during the very short rainy season, but have to store enough water for the extremely long dry season, the intelligent handling of water resources is vital. This study focusses on Lac Bam in Burkina Faso, which is the largest natural lake of the country and of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. With respect to the competition for water resources an independent area-wide monitoring system is essential for the acceptance of any decision maker. The following contribution introduces a weather and illumination independent monitoring system for the automated wetland delineation with a high temporal (about two weeks) and a high spatial sampling (about five meters). The similarities of the multispectral and multi-polarized SAR acquisitions by RADARSAT-2 and TerraSAR-X are studied as well as the differences. The results indicate that even basic approaches without pre-classification time series analysis or post-classification filtering are already enough to establish a monitoring system of prime importance for a whole region

Mapping snow cover extent using optical and SAR data

Snow cover plays an important role both globally and regionally as it is fundamental for local water availability, river run-off, and groundwater recharge. Hence, the exact knowledge of extent and dynamic of the snow coverage is essential. This study combines synergetic optical and SAR data with the main object to map the snow cover extent. As the Sentinel-Mission provides a wide swath width and a high revisit time (2-3 days at mid-latitudes with same acquisition geometry), the Sentinel-1 Interferometric Wide Swath Mode (IW) SAR data and Sentinel-2 multi-spectral data are used. Additionally, the TanDEM-X DEM is applied for the exact determination of the snow line as well as for snow classification. The mapping of the snow cover extent is applied on the three test sites Devon Island in Canada, Nordenskiöld, Svalbard, and French Alps, France which are characterized by different topography and land cover. The classification achieved an overall accuracy of 85% for Devon Island, 60% for Nordenskiöld and 88 % for the French Alps

TerraSAR-X and Wetlands: A Review

Since its launch in 2007, TerraSAR-X observations have been widely used in a broad range of scientific applications. Particularly in wetland research, TerraSAR-X\u27s shortwave X-band synthetic aperture radar (SAR) possesses unique capabilities, such as high spatial and temporal resolution, for delineating and characterizing the inherent spatially and temporally complex and heterogeneous structure of wetland ecosystems and their dynamics. As transitional areas, wetlands comprise characteristics of both terrestrial and aquatic features, forming a large diversity of wetland types. This study reviews all published articles incorporating TerraSAR-X information into wetland research to provide a comprehensive study of how this sensor has been used with regard to polarization, and the function of the data, time-series analyses, or the assessment of specific wetland ecosystem types. What is evident throughout this literature review is the synergistic fusion of multi-frequency and multi-polarization SAR sensors, sometimes optical sensors, in almost all investigated studies to attain improved wetland classification results. Due to the short revisiting time of the TerraSAR-X sensor, it is possible to compute dense SAR time-series, allowing for a more precise observation of the seasonality in dynamic wetland areas as demonstrated in many of the reviewed studies

Shaping the Global High-Resolution TanDEM-X Digital Elevation Model

The global digital elevation model (DEM) produced by the TanDEM-X (TerraSAR-X add-on for digital elevation measurements) mission is an interferometric elevation model with unprecedented quality, accuracy, and coverage. It represents an unedited surface model as artifacts inherent to the interferometric synthetic aperture radar acquisition and processing technique are still present. The most prominent artifacts in the DEM are water bodies appearing with a rough surface due to low coherence. Additionally, outliers, voids, and larger data gaps may be present in this dataset. Therefore,DEM editing is crucial for many applicationsincluding hydrology or orthorectification of remote sensing data. Depending on the field of application, different techniques of quality enhancement are required. This article provides a comprehensive description of a semi-automatic framework specially developed for generating an edited version of the TanDEM-X dataset by shaping the high-resolution 12 m DEM with focus on water areas, outlier handling, and void filling. The default configuration parameters of the workflow can thereby be adapted interactively for challenging areas where appropriate. A quality assessment of the resulting edited DEM was done by statistical measures, visual methods, as well as by an artifact evaluation

Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients

Comparison of alternative image representations in the context of SAR change detection

This article compares four different alternative image representations in the context of a structure-based change detection. The framework is taken from the already published Curvelet-based change detection approach. Only the transform step is modified by inserting three additional transforms: the Laplacian pyramid, the Wavelet and the Surfacelet transform. The results of the change detection are compared to the single pixel difference image in order to find the representation that best illustrates the underlying structures. The Curvelet transform again turns out to be very powerful in describing man-made objects and landscapes

First Evaluation Results of the Water Indication Mask as a By-product of the TanDEM-X DEM

The main goal of the TanDEM-X mission is the production of a global Digital Elevation Model (DEM). A byproduct is the so-called Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process where the DEM is noisy. The WAM is derived from the SAR amplitude and the single-pass coherence. In this paper, the methodology of the water body detection is briefly explained and the results of four test sites covering different climatic regions are evaluated. The different characteristics of the WAM using amplitude and coherence image are described and their respective pros and cons are discussed

Validation of the tie-point concepts by the DEM adjustment approach of TanDEM-X

The aimed accuracies for the final TanDEM-X DEM of 10m absolute and 2m relative height error will be ensured by calibration data. One crucial data set for the relative accuracy is tie-points that connect adjacent DEM acquisitions in the approximately 4km-overlap-area with each other. In this paper an improved concept for tie-point candidates is presented that is based on averaging a larger region instead of comparing single points. This concept should be more robust against noise. It is validated by applying the DEM calibration on a simulated test area and if available on real TanDEM-X data. Also, the DEM calibration will be validated for the first time on a larger “real” test site by applying the TanDEM-X processing scenario