Nikola Besic  

Here are the most important publications I have worked on

1. [PREPRINT] M. Schwartz, F. Fogel, N. Besic, D. Robert, L. Geist, J.-P. Renaud, J.-M. Monnet, C. Mosig, C. Vega, A. d’Aspremont, L. Landrieu, and P. Ciais, “FORMSpot: A decade of tree-level, country-scale forest monitoring,” arXiv (undergoing review at Remote Sensing of Environment), 2026. DOI: 10.48550/arXiv.2512.17021.
   Open access (pdf DOI link)
2. [PREPRINT] N. Besic, P. Ciais, F. Li, R. Jackson, K. Yuan, S. Peng, B. Poulter, Z. Zhang, and Q. Zhu, “Beyond the simple mean: a way to improve multi-model bottom-up wetland CH4 estimates?,” EGUsphere (undergoing review at Geoscientific Model Development), 2026. DOI: 10.5194/egusphere-2026-2604.
   Open access (pdf DOI link)
3. Y. Su, Y. Xu, X. Zhang, D. Makowski, A. Pellissier-Tanon, S. Francini, T. Shi, K. Yu, S. Liu, H. Chen, H. Li, J. Chang, S. Chen, C. Yin, N. Besic, M. Brandt, A. Viana-Soto, K. Kowalski, C. Senf, A. d’Aspremont, and P. Ciais, “Disturbance characteristics and forest properties regulate surface warming and recovery across Europe,”, 2026. (accepted for publication in Nature Geoscience - with a preprint published at Research Square - 10.21203/rs.3.rs-8782712/v1)
   DOI and pdf of the final version should be available soon!
4. R. Pouysegur, K. Fujisaki, L. Boulonne, N. Besic, C. Vega, F. Caroulle, and F.-X. de Saintonge, “Forest stand descriptions from the French Soil Quality Monitoring Network: repeated surveys (2006–2024) and documented silvicultural management,” Annals of Forest Science, 2026. (accepted for publication )
   DOI and pdf should be available soon!
5. P. Ciais, S. Peng, J. Chang, F. Li, Q. Zhu, K. Yuan, G. Hugelius, H. Li, Y. Cai, F. Chevallier, K. Tibrewal, E. A. Kort, K. Arndt, J. Watts, B. Buma, N. Besic, P. I. Palmer, H. Cadillo-Quiroz, E. Euskirchen, M. Gondwem, A. Hoyt, R. Jackson, S. Malone, D. Monteverde, S. Natali, M. Ramonet, C. Rey-Sanchez, L. Sagang, E. Schuur, R. Vargas, R. Varner, and B. Poulter, “A global methane observation system to reduce uncertainty for anthropogenic and natural sources and sinks for detecting and attributing climate feedbacks,” Advanced Science, 2026. (accepted for publication )
   DOI and pdf should be available soon!
6. Y. Su, D. Makowski, X. Zhang, S. Liu, K. Yu, Y. Xu, H. Chen, S. Deng, T. Shi, C. Zhou, S. Chen, H. Li, C. Yin, N. Besic, M. Brandt, A. d’Aspremont, and P. Ciais, “A remote sensing-based assessment of the cooling effects of urban trees in European cities,” NPJ Urban Sustainability, vol. 6, no. 1, p. 96, Apr. 2026. DOI: 10.1038/s42949-026-00399-w.
   Open access (pdf DOI link)
7. H. E. Cuny, J.-D. Bontemps, N. Besic, A. Colin, L. Hertzog, A. Le Squin, W. Marchand, C. Vega, and J.-M. Leban, “Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France,” Biogeosciences, 23, 2365–2388, 2026. DOI: 10.5194/bg-23-2365-2026.
   Open access (pdf DOI link)
8. N. Picard, N. Besic, M. Meliho, F. Mortier, J. Sainte-Marie, and M. Legay, “Bayesian model averaging of climate-dependent forest models using Expectation-Maximization,” Ecological modelling, Volume 510, 2025. DOI: 10.1016/j.ecolmodel.2025.111355.
   Postprint available (pdf)
9. M. Schwartz, P. Ciais, E. Sean, A. de Truchis, C. Vega, N. Besic, I. Fayad, J.-P. Wigneron, S. Brood, A. Pelissier-Tanona, J. Pauls, G. Belouze, and Y. Xu, “Retrieving yearly forest growth from satellite data: A deep learning based approach,” Remote Sensing of Environment, Volume 330, 2025. DOI: 10.1016/j.rse.2025.114959.
   Open access (pdf DOI link)
10. P. Ciais, C. Zhou, P. Schneider, M. Schwartz, N. Besic, C. Vega, and J. Bontemps, “An outlook on the rapid decline of carbon sequestration in French forests and associated reporting needs,” Comptes Rendus. Géoscience, vol. 358, pp. 27–49, 2025. DOI: 10.5802/crgeos.309.
    Open access (pdf DOI link)
11. L.-A. Ramirez Parra, J.-P. Renaud, R. Bindner, T. Cordonnier, L. Hertzog, N. Besic, J.-D. Bontemps, and C. Vega, “Fiabilité des modèles de télédétection établis sur de grands territoires forestiers : une analyse de leur applicabilité à l’échelle locale,” Revue Forestière Française, 76, 2, 173-185 2025. DOI: 10.20870/revforfr.2025.9611. (in French)
    Open access (pdf DOI link)
12. G. Destouet, N. Besic, E. Joetzjer, and M. Cuntz, “Turbulent transport extraction in time and frequency and the estimation of eddy fluxes at high resolution,” Atmospheric Measurement Techniques, 18, 3193–3215, 2025. DOI: 10.5194/amt-18-3193-2025.
    Open access (pdf DOI link)
13. N. Besic, N. Picard, C. Vega, J.-D. Bontemps, L. Hertzog, J.-P. Renaud, F. Fogel, M. Schwartz, A. Pellissier-Tanon, G. Destouet, F. Mortier, M. Planells-Rodriguez, and P. Ciais, “Remote sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?,” Geoscientific Model Development, 18, 337–359, 2025. DOI: 10.5194/gmd-18-337-2025.
    Open access (pdf DOI link)

    Reliable mapping of forest attributes requires not only combining complementary remote sensing approaches but also explicitly quantifying the uncertainty associated with spatial predictions. Ensemble modelling provides a natural framework for achieving both objectives.

    
14. Y. Su, M. Schwartz, I. Fayad, M. Garcia, M. Zavala, J. Tijerin-Trivino, J. Astigarraga, V. Cruz-Alonso, S. Liu, X. Zhang, S. Chen, F. Ritter, N. Besic, A. d’Aspremont, and P. Ciais, “Canopy height and biomass distribution across the forests of Iberian peninsula,” Nature Research - Scientific Data, 12, 678, 2025. DOI: 10.1038/s41597-025-05021-9.
    Open access (pdf DOI link)
15. F. Fogel, Y. Perron, N. Besic, L. Saint-André, A. Pellissier-Tanon, M. Schwartz, T. Boudras, I. Fayad, A. d'Aspremont, L. Landrieu, and P. Ciais, “Open-Canopy: Towards Very High Resolution Forest Monitoring,” IEEE/CVF Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pp. 1395-1406, 2025. DOI: 10.1109/CVPR52734.2025.00138.
    Open access (pdf CVF link)
16. N. Besic, S. Durrieu, A. Schleich, and C. Vega, “Using structural class pairing to address the spatial mismatch between GEDI measurements and NFI plots,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 12 854–12 867, 2024. DOI: 10.1109/JSTARS.2024.3425431.
    Open access (pdf DOI link)

    Simple allometric relationships between canopy height and biomass or carbon are often insufficient to capture forest structural complexity. Exploiting the full vertical information contained in lidar profiles, including lower canopy layers, enables a more robust characterization of aboveground carbon and provides the foundation for developing more accurate and generalizable forest carbon estimates.

    
17. N. Besic, N. Picard, J. Sainte-Marie, M. Meliho, C. Piedallu, and M. Legay, “A novel framework and a new score for the comparative analysis of forest models accounting for the impact of climate change,” Journal of Agricultural, Biological and Environmental Statistics, vol. 29, no. 1, pp. 73–91, 2023. DOI: 10.1007/s13253-023-00557-y.
    Postprint available (pdf)

    Comparing models in environmental rather than geographic space provides a new perspective on the complementarity between mechanistic and observation-based approaches, highlighting how they capture different aspects of ecosystem responses across scales.

    
18. J. Gehring, A. Ferrone, A.-C. Billault-Roux, N. Besic, K. D. Ahn, G. Lee, and A. Berne, “Radar and groundlevel measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games,” Earth System Science Data, vol. 13, no. 2, pp. 417–433, 2021. DOI: 10.5194/essd-13-417-2021.
    Open access (pdf DOI link)
19. J. Gehring, A. Oertel, É. Vignon, N. Jullien, N. Besic, and A. Berne, “Microphysics and dynamics of snowfall associated with a warm conveyor belt over Korea,” Atmospheric Chemistry and Physics, vol. 20, no. 12, pp. 7373–7392, 2020. DOI: 10.5194/acp-20-7373-2020.
    Open access (pdf DOI link)
20. A. Sunjerga, M. Rubinstein, N. Pineda, A. Mostajabi, M. Azadifar, D. Romero, O. Van der Velde, J. Montanya, J. Figueras i Ventura, N. Besic, J. Grazioli, A. Hering, U. Germann, G. Diendorfer, and F. Rachidi, “LMA observations of upward lightning flashes at the Säntis tower initiated by nearby lightning activity,” Electric Power Systems Research, vol. 181, p. 106 067, 2020, issn: 0378-7796. DOI: 10.1016/j.epsr.2019.106067.
    Open access (pdf DOI link)
21. J. Figueras i Ventura, N. Pineda, N. Besic, J. Grazioli, A. Hering, O. A. van der Velde, D. Romero, A. Sunjerga, A. Mostajabi, M. Azadifar, M. Rubinstein, J. Montanyà, U. Germann, and F. Rachidi, “Analysis of the lightning production of convective cells,” Atmospheric Measurement Techniques, vol. 12, no. 10, pp. 5573–5591, 2019. DOI: 10.5194/amt-12-5573-2019.
    Open access (pdf DOI link)
22. J. Figueras i Ventura, N. Pineda, N. Besic, J. Grazioli, A. Hering, O. A. van der Velde, D. Romero, A. Sunjerga, A. Mostajabi, M. Azadifar, M. Rubinstein, J. Montanyà, U. Germann, and F. Rachidi, “Polarimetric radar characteristics of lightning initiation and propagating channels,” Atmospheric Measurement Techniques, vol. 12, no. 5, pp. 2881–2911, 2019. DOI: 10.5194/amt-12-2881-2019.
    Open access (pdf DOI link)
23. N. Pineda, J. Figueras i Ventura, D. Romero, A. Mostajabi, M. Azadifar, A. Sunjerga, F. Rachidi, M. Rubinstein, J. Montanyà, O. van der Velde, P. Altube, N. Besic, J. Grazioli, U. Germann, and E. R. Williams, “Meteorological aspects of self-initiated upward lightning at the Säntis tower (Switzerland),” Journal of Geophysical Research: Atmospheres, vol. 124, no. 24, pp. 14 162–14 183, 2019. DOI: 10.1029/2019JD030834.
    Open access (pdf DOI link)
24. É. Vignon, N. Besic, N. Jullien, J. Gehring, and A. Berne, “Microphysics of snowfall over coastal East Antarctica simulated by Polar WRF and observed by radar,” Journal of Geophysical Research: Atmospheres, vol. 124, no. 21, pp. 11 452–11 476, 2019. DOI: 10.1029/2019JD031028.
    Open access (pdf DOI link)
25. N. Besic, J. Gehring, C. Praz, J. Figueras i Ventura, J. Grazioli, M. Gabella, U. Germann, and A. Berne, “Unraveling hydrometeor mixtures in polarimetric radar measurements,” Atmospheric Measurement Techniques, vol. 11, no. 8, pp. 4847–4866, 2018. DOI: 10.5194/amt-11-4847-2018.
    Open access (pdf DOI link)

    Statistical methods should be anchored in physical understanding. By building on radar signal physics and scattering mechanisms, this work illustrates how statistical models can remain consistent with the underlying physical processes.

    
26. F. Gerber, N. Besic, V. Sharma, R. Mott, M. Daniels, M. Gabella, A. Berne, U. Germann, and M. Lehning, “Spatial variability in snowprecipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain,” The Cryosphere, vol. 12, no. 10, pp. 3137–3160, 2018. DOI: 10.5194/tc-12-3137-2018.
    Open access (pdf DOI link)
27. S. Trefalt, A. Martynov, H. Barras, N. Besic, A. Hering, S. Lenggenhager, P. Noti, M. Röthlisberger, S. Schemm, U. Germann, and O. Martius, “A severe hail storm in complex topography in Switzerland - observations and processes,” Atmospheric Research, vol. 209, pp. 76–94, 2018, issn: 0169-8095. DOI: 10.1016/j.atmosres.2018.03.007.
    Open access (pdf DOI link)
28. D. Nerini, N. Besic, I. Sideris, U. Germann, and L. Foresti, “A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform,” Hydrology and Earth System Sciences, vol. 21, no. 6, pp. 2777–2797, 2017. DOI: 10.5194/hess-21-2777-2017.
    Open access (pdf DOI link)
29. N. Besic, J. Figueras i Ventura, J. Grazioli, M. Gabella, U. Germann, and A. Berne, “Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach,” Atmospheric Measurement Techniques, vol. 9, no. 9, pp. 4425–4445, 2016. DOI: 10.5194/amt-9-4425-2016.
    Open access (pdf DOI link)

    Combining learning-based approaches with physical knowledge can improve the performance of remote sensing models. Integrating physical understanding with data-driven learning leads to more robust inference of key environmental variables.

    
30. L. Pralon, G. Vasile, M. DallaMura, J. Chanussot, and N. Besic, “Evaluation of ICA-based ICTD for PoLSAR data analysis using a sliding window approach: Convergence rate, Gaussian sources, and spatial correlation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 7, pp. 4262–4271, 2016. DOI: 10.1109/TGRS.2016.2538900.
31. N. Besic, G. Vasile, J. Chanussot, and S. Stankovic, “Polarimetric incoherent target decomposition by means of independent component analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 3, pp. 1236–1247, 2015. DOI: 10.1109/TGRS.2014.2336381.
    Postprint available (pdf)

    Physically interpretable information should be extracted before any modelling step, keeping signal processing at the core of the analysis. This approach emphasizes interpretability and physical consistency over purely black-box solutions.

    
32. N. Besic, G. Vasile, J.-P. Dedieu, J. Chanussot, and S. Stankovic, “Stochastic approach in wet snow detection using multitemporal SAR data,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 244–248, 2015. DOI: 10.1109/LGRS.2014.2334355.
    Postprint available (pdf)

    Remote sensing should explicitly account for uncertainty. By combining radar signal physics with observational variability, this probabilistic framework propagates uncertainty throughout the entire estimation chain and produces probabilistic rather than deterministic maps.

    
33. N. Besic, G. Vasile, A. Anghel, T.-I. Petrut, C. Ioana, S. Stankovic, A. Girard, and G. d’Urso, “Zernike ultrasonic tomography for fluid velocity imaging based on pipeline intrusive time-of-flight measurements,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 61, no. 11, pp. 1846–1855, 2014. DOI: 10.1109/TUFFC.2014.006515.
    Postprint available (pdf)
34. N. Besic, G. Vasile, F. Gottardi, J. Gailhard, A. Girard, and G. d’Urso, “Calibration of a distributed SWE model using MODIS snow cover maps and in situ measurements,” Remote Sensing Letters, vol. 5, no. 3, pp. 230–239, 2014. DOI: 10.1080/2150704X.2014.897399.
    Postprint available (pdf)