Classification of sediments on exposed tidal flats in the German Bight using multi-frequency radar data

We present a new method for the extraction of roughness parameters of sand ripples on exposed tidal flats from multi-frequency synthetic aperture radar (SAR) data. The method is based on the Integral Equation Model (IEM) which predicts the normalized radar cross-section (NRCS) of randomly rough dielectric surfaces. The data used for this analysis were acquired in the German Bight of the North Sea by the Spaceborne Imaging Radar-C/X-Band SAR (SIR-C/X-SAR) in 1994. In-situ measurements of the root-mean-squared (rms) height and the correlation length of the sand ripples clearly demonstrate a relationship between these roughness parameters and the C-band NRCS determined from an ERS SAR image. Using the IEM we have calculated NRCS isolines for the three frequency bands deployed by SIR-C/X-SAR (L, C, and X band), as a function of the rms height and the correlation length of the sand ripples. For each SIR-C/X-SAR image pixel these two roughness parameters were determined from the intersections of the NRCS isolines at different radar bands, and they were used for a crude sediment classification for a small test area at the German North Sea coast. Comparing our results with available sediment maps, we conclude that the presented method is very promising for tidal flat classification by using data from presently existing airborne and future spaceborne multi-frequency SAR systems.

[1]  Alice Deschamps,et al.  Polarimetric C-band observations of soil moisture for pasture fields , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[2]  Adrian K. Fung,et al.  Backscattering from a randomly rough dielectric surface , 1992, IEEE Trans. Geosci. Remote. Sens..

[3]  Martin Gade,et al.  Determination of surface roughness parameters of tidal flats from SIR-C/X-SAR 3-frequency SAR data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[4]  Peter M. J. Herman,et al.  Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery , 2005 .

[5]  J. Van Zyl,et al.  An empirical soil moisture estimation algorithm using imaging radar , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[6]  F. Ulaby,et al.  Multitemporal land-cover classification using SIR-C/X-SAR imagery , 1998 .

[7]  M. S. Moran,et al.  Comparison of four models to determine surface soil moisture from C‐band radar imagery in a sparsely vegetated semiarid landscape , 2006 .

[8]  Martien Molenaar,et al.  Operational Remote Sensing for Sustainable Development , 2020 .

[9]  T. Schmugge,et al.  An Empirical Model for the Complex Dielectric Permittivity of Soils as a Function of Water Content , 1980, IEEE Transactions on Geoscience and Remote Sensing.

[10]  J. Ryu,et al.  Waterline extraction from Landsat TM data in a tidal flat: a case study in Gomso Bay, Korea , 2002 .

[11]  Joong-Sun Won,et al.  A critical grain size for Landsat ETM+ investigations into intertidal sediments: a case study of the Gomso tidal flats, Korea , 2004 .

[13]  J. R. Allen,et al.  Current Ripples : their relation to patterns of water and sediment motion , 1968 .

[14]  Jiancheng Shi,et al.  Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data , 1997, IEEE Trans. Geosci. Remote. Sens..

[15]  Jean Rousselle,et al.  Mapping near‐surface soil moisture with RADARSAT‐1 synthetic aperture radar data , 2004 .

[16]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[17]  J. Ehlers The Morphodynamics of the Wadden Sea , 1988 .

[18]  Fawwaz Ulaby,et al.  Preliminaly Evaluation of the SIR-B Response to Soil Moisture, Surface Roughness, and Crop Canopy Cover , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Kun-Shan Chen,et al.  An update on the IEM surface backscattering model , 2004, IEEE Geoscience and Remote Sensing Letters.

[20]  P. O’neill,et al.  Soil moisture estimation using time-series radar measurements of bare and vegetated fields in Washita '92 , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.