PhD Student, Polytech Annecy-Chambéry.


Courriel :

Office  : A109

Adress : LISTIC – Polytech Annecy-Chambéry, BP 80439 – Annecy le Vieux, 74944 ANNECY Cedex, France



Topic : Estimation, fusion and analysis of multi-sensor / multi-temporal glaciers velocity observations derived from remote sensing images


Ice velocity maps are necessary to precisely monitor ice dynamics and to infer subglacial processes such as ocean forcing or subglacial hydrology. Since the 1970’s, the temporal and spatial resolution of SAR and optical images has improved. This allows the measurement of image scene-pair ice velocities with a spatial resolution up to 50 m and a temporal resolution up to 2 days [Gardner et al., 2018; Milllan et al., 2019; Friedl et al., 2021]. However, these ice velocity maps still contain noise, gaps and artefacts.

First, since all the operational method used for ice velocity estimation relies on image matching, we explore the application of two dense estimators coming from computer vision: the optical flow algorithm GeFolki and the deep learning algorithm PWCNet multi-modal [Charrier et al., 2020a].

Second, ice velocity data are complex to analyze since velocity measurements span across different temporal baselines. Velocities obtained from a small temporal baseline are close to the derivative of the displacement but are more likely to be contaminated by noise uncorrelated with time. Velocities obtained from a long temporal baseline approximate the mean velocity between two dates but can be affected by temporal decorrelation. Therefore, we propose to invert time series of velocities with an optimal temporal sampling from all available displacement measurements. We develop for that an Iterative Weighted Least Square, relying on an improved temporal closure of the displacement measurement network [Charrier et al., 2020b; Charrier et al., 2021; Charrier et al., 2022a]. An adaptation of this method to multi-sensor datasets have latter been proposed [Charrier et al., 2022c].

The final goal is to fuse velocity maps computed using different sensors, by different authors in order to study the intra-annual variations of ice velocities and its link with subglacial hydrology.

Supervisors : Emmanuel Trouvé, Elise Colin Koeniguer, Yajing Yan

Beginning of the thesis October 2019

Doctoral school:  SIESciences Ingénierie Environnement”



Charrier, L., Yan, Y., Koeniguer, E. C., Mouginot J., Millan R. & Trouvé, E. (2022c). Fusion of multi-temporal and multi-sensor ice velocity observations by  International Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jun 2022, Nice (accepted) (pdf accessible here)

Charrier, L., Di Martino, T., Colin-Koeniguer, E., Weissgerber, F., Plyer, A. (2022b). Extraction relevance from SAR temporal profiles on cryosphere by a deep autoencoder International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jun 2022, Nice (accpeted)

Charrier, L., Yan, Y., Koeniguer,E. C. , Leinss, S. and Trouvé, E., (2022a) “Extraction of Velocity Time Series With an Optimal Temporal Sampling From Displacement Observation Networks,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-10, 2022, Art no. 4302810, doi: 10.1109/TGRS.2021.3128289. (pdf accesible here)

Charrier, L., Yan, Y., Colin-Koeniguer, E., & Trouvé, E. (2021). Fusion of Glacier Displacement Observations with Different Temporal Baselines. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 5497-5500). IEEE. doi: 10.1109/TGRS.2021.3128289 (pdf accesible here)

Charrier, L., Godet, P., Rambour, C., Weissgerber, F., Erdmann, S., & Koeniguer, E. C. (2020a,). Analysis of dense coregistration methods applied to optical and SAR time-series for ice flow estimations. In 2020 IEEE Radar Conference (RadarConf20) (pp. 1-6). IEEE., doi: 10.1109/RadarConf2043947.2020.9266643. (pdf accesible here)



Charrier, L., Yan, Y., Koeniguer, E., Trouvé, E., Millan, R., Mouginot, J., & Derkacheva, A. (2020b). Fusion and mining of glacier surface flow velocity time series. In EGU General Assembly Conference Abstracts (p. 20335).

Scheuchl, B., Rignot, E. J., Brancato, V., Mouginot, J., Jeong, S., Milillo, P., & Charrier, L. (2019). Tidally influenced, short term grounding line variations measured with Sentinel-1, RADARSAT-2, and Cosmo Skymed. In AGU Fall Meeting Abstracts (Vol. 2019, pp. C21D-1474).

Charrier, L., Scheuchl, B., Mouginot, J., Jeong, S., Brancato, V., Rignot, E. Grounding zone mapping in Antarctica using radar interferometry MDIS 2019



2019 – today        Teaching assistant at CentraleSupélec – Earth Observation  (71.5 hours)

2020 – today       Teaching assistant at Polytech Annecy – Informatic and Image analysis (39.3 hours)



In 2021, we wrote a blog paper on remote sensing and glaciers on the project we are supervising with my colleague at CentralSupelec: click here to discover it ! The 2022 edition is available here: it is a nice comparison between ice velocities from ITS_LIVE and RETREAT website, radiometric changes and snow cover.



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