Speaker
Description
Although seasonal snow cover is widely recognized as a critical water resource, accurate SWE assessment is still challenging when considered at an operational level, especially in regions characterized by complex topography [1]. SWE retrieval methods based on polarimetry are inherently linked to the assumption of specific models of the snowpack, [1], [2], [3],[4], [5]. Such approaches can turn out to be problematic in heterogeneous areas, where the link between Radar observables and snow parameters is modulated by local conditions. DInSAR-based methods allow for a direct conversion of the interferometric phase between SAR acquisitions from two different dates into the SWE variation that took place in between [1], [6], [7], [8], [9]. The underlying physical model stems from the fundamental assumption of a transparent snowpack [7], [8].DInSAR-based SWE retrieval is expected to perform best at lower frequencies such as L-Band, where the assumption of a transparent snowpack is inherently better verified and interferometric coherence is generally higher [9]. A downside of such technique is that the resulting accuracy is critically dependent on the capability to compensate for topography and tropospheric delays at local scale, which can be quite a challenging task in mountain regions [9]. In addition, the amount of SWE at a given date needs to be calculated by integrating differential measurements, which might introduce propagation errors.
Interestingly, direct measurement of absolute SWE at a given date is enabled by SAR Tomography (TomoSAR), which leverages multiple across-track baselines to provide direct imaging of the snowpack. Experimental works on the use of tomography demonstrated that X- and Ku-Band waves provide sensitivity to scattering from both the air/snow and snow/terrain interfaces in dry snow, as well as the possibility to image the multi-layered structure of the snowpack [10], [11], [12]. This methodology was successfully applied in [12], demonstrating not only the retrieval of bulk density, but also the possibility to study snow density within each detected layer.
Within this paper, we aim at investigating the extent to within which the result in [12] can be reproduced by using a formation of Multiple-Input-Multiple-Output (MIMO) X-Band SAR satellites. The formation is assumed to implement a Frequency Division Multiplexing (FDM) access scheme, where all satellites transmit simultaneously on different frequency bands and receive the echoes scattered by the Earth’s surface in all transmitted bands. Fine vertical resolution is achieved by developing a novel approach to set the satellite positions, referred to as Minimum Redundancy Wavenumber Illumination. As a result, we show two examples where formations of 4 or 5 satellites are deployed to provide the equivalent of 17 and 26 monostatic acquisitions, respectively, allowing for tomographic imaging of the snowpack at a vertical resolution better than half a meter. The proposed concept is supported by numerical simulations and sensitivity analyses, which advocate for the feasibility of accurate retrieval of snow parameters.
REFERENCES
[1]. Tsang. L, et al. Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, 2022, https://doi.org/10.5194/tc-16-3531-2022
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[7]. Rott, Helmut & Nagler, Thomas & Scheiber, Rolf. (2003). Snow mass retrieval by means of SAR interferometry., Proceedings of the FRINGE 2003 Workshop (ESA SP-550). 1-5 December 2003, ESA/ESRIN, Frascati, Italy. Editor: H. Lacoste. Published on CDROM., id.29
[8]. S. Leinss, A. Wiesmann, J. Lemmetyinen and I. Hajnsek, "Snow Water Equivalent of Dry Snow Measured by Differential Interferometry," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3773-3790, Aug. 2015, doi: 10.1109/JSTARS.2015.2432031.
[9]. Tarricone, J., Webb, R. W., Marshall, H.-P., Nolin, A. W., and Meyer, F. J.: Estimating snow accumulation and ablation with L-band InSAR, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-224, in review, 2022 .
[10]. S. Tebaldini and L. Ferro-Famil, "High resolution three-dimensional imaging of a snowpack from ground-based sar data acquired at X and Ku Band," 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013, pp. 77-80, doi: 10.1109/IGARSS.2013.6721096
[11]. O. Frey, C. L. Werner, R. Caduff and A. Wiesmann, "A time series of SAR tomographic profiles of a snowpack," Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, 2016, pp. 1-5
[12]. Rekioua et al., Snowpack permittivity profile retrieval from tomographic SAR data, Comptes Rendus Physique, 2017
