Open Access
| Issue |
BIO Web Conf.
Volume 216, 2026
The 6th Sustainability and Resilience of Coastal Management (SRCM 2025)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 12 | |
| Section | Geo-Marine and Mapping Application for Coastal Areas | |
| DOI | https://doi.org/10.1051/bioconf/202621601003 | |
| Published online | 05 February 2026 | |
- C.J. Vörösmarty, P.B. McIntyre, and M.O. Gessner, “Global threats to human water security and river biodiversity,” Nature, vol. 467, pp. 555-561, 2010. [Google Scholar]
- C.B. Mouw et al., “Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions,” Remote Sens Environ, vol. 160, pp. 15-30, 2015. [Google Scholar]
- G. Dall’Olmo and A.A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl Opt, vol. 50, no. 15, pp. 2111-2125, 2011. [Google Scholar]
- T. Kutser, “The possibility of using remote sensing to monitor shallow lakes,” Hydrobiologia, vol. 698, pp. 25-42, 2012. [Google Scholar]
- K. Shi, Y. Zhang, G. Zhu, B. Qin, D. Pan, and C. Huang, “Challenges and perspectives in remote sensing of inland water quality in the era of high-resolution multispectral satellites,” Remote Sens Environ, vol. 280, p. 113182, 2022. [Google Scholar]
- Q. Vanhellemont, “Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives,” Remote Sens Environ, vol. 225, pp. 175-192, 2019. [Google Scholar]
- N. Pahlevan, S. Sarkar, B.A. Franz, S.V. Balasubramanian, and J. He, “Satellite-based remote sensing of water quality in lakes and reservoirs: Challenges and opportunities,” Water (Basel), vol. 9, no. 7, pp. 1-24, 2017. [Google Scholar]
- D. Odermatt, A. Gitelson, V.E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens Environ, vol. 118, pp. 116-126, 2012. [Google Scholar]
- K. Toming, T. Kutser, L. Tuvikene, M. Viik, T. Tamm, and T. Nõges, “Spatio-temporal variability of phytoplankton primary production in a large shallow lake,” Hydrobiologia, vol. 780, pp. 185-199, 2016. [Google Scholar]
- R. Doerffer and H. Schiller, “The MERIS Case 2 water algorithm,” Int J Remote Sens, vol. 28, no. 3-4, pp. 517-535, 2007. [Google Scholar]
- EUMETSAT, “OLCI Algorithm Theoretical Basis Document (OLCI ATBD) - Neural network atmospheric correction and in-water processing (C2RCC),” EUMETSAT, Darmstadt, Germany, 2017. [Google Scholar]
- G. Tilstone, “Performance of ocean colour chlorophyll-a algorithms for the coastal and shelf seas of the North East Atlantic,” Remote Sens Environ, vol. 189, pp. 1-19, 2017. [Google Scholar]
- J. Chen, Y. Li, and Y. Zhang, “Estimation of chlorophyll-a concentration in inland waters using Sentinel-2 MSI imagery and ensemble learning,” Remote Sens (Basel), vol. 12, no. 3, p. 441, 2020. [Google Scholar]
- Y. Zhang, K. Shi, and B. Qin, “Deep learning-based retrieval of chlorophyll-a concentration in large shallow lakes from Sentinel-2 MSI,” Science of the Total Environment, vol. 776, p. 145928, 2021. [Google Scholar]
- M. Syariz, C.-H. Lin, M. Nguyen, L. Jaelani, and A. Blanco, “WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval,” Remote Sens (Basel), vol. 12, p. 1966, Jun. 2020, doi: 10.3390/rs12121966. [Google Scholar]
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