TANDEM-X BISTATIC INSAR FOR MEASURING SNOW AND ICE MELT DYNAMICS

被引:0
作者
Rizzo, Paola [1 ]
Gonzalez, Carolina [1 ]
Campos, Alexandre Becker [1 ,2 ]
Dell'Amore, Luca [1 ]
Milillo, Pietro [1 ,3 ]
Nagler, Thomas [4 ]
机构
[1] German Aerosp Ctr DLR, Wessling, Germany
[2] Friedrich Alexander Univ FAU, Erlangen, Germany
[3] Univ Houston, Houston, TX USA
[4] ENVEO GmbH, Innsbruck, Austria
来源
IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024 | 2024年
关键词
TanDEM-X; DEM calibration; surface penetration bias; snow depth; snow melt;
D O I
10.1109/IGARSS53475.2024.10640880
中图分类号
学科分类号
摘要
Single-pass SAR interferometry (InSAR) has demonstrated a great potential for the monitoring of ice and snow melt dynamics. In particular, digital elevation models (DEM) derived from the TanDEM-X bistatic SAR mission are widely used for measuring elevation changes over glaciers through time-tagged DEM differencing. A critical aspect of this approach is represented by the mutual calibration of the input DEMs, which are normally affected by residual offsets and tilts, caused by uncertainties on the baseline estimation. Moreover, a further crucial aspect which needs to be addressed is the penetration of radar waves into the snow pack, which is closely linked to both the properties of snow and the radar parameters, such as frequency and acquisition geometry. This in turn jeopardizes the retrieval of the topographic height of the surface and adds a significant amount of uncertainty when performing DEM differencing over snow-covered areas. In this paper, we present an overview of the activities which are currently being carried out at DLR, together with partner institutions and companies, aimed at providing more reliable estimations of snow depth and glaciers topographic height changes using TanDEM-X bistatic InSAR data. We present a novel technique for performing an automatic selection of reliable calibration points, based on the use of natural targets, together with the mutual calibration procedure. Moreover, we rely on a data-driven machine learning approach for the estimation and compensation of the surface penetration bias. Preliminary results are extremely promising, also in view of future bistatic SAR missions, such as the ESA Harmony Earth Explorer mission.
引用
收藏
页码:639 / 643
页数:5
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