# Towards Sentinel-2-based monthly water occurrence mapping with the Dynamic World data suite

Leo Helling, Barbara Belletti, Mathis Messager, Louis Rey, Hervé Parmentier,

Hervé Piégay

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Leo Helling, Barbara Belletti, Mathis Messager, Louis Rey, Hervé Parmentier, et al. "Towards Sentinel-2-based monthly water occurrence mapping with the Dynamic World data suite." EGU General Assembly 2025, Apr 2025, Vienna (Autriche), Austria. https://meetingorganizer.copernicus.org/EGU25/EGU25-18783.html, 10.5194/egusphere-egu25-18783. [hal-05410254]

HAL Id: hal-05410254

https://hal.science/hal-05410254v1

Submitted on 10 Dec 2025

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# Towards Sentinel-2-based monthly water occurrence mapping with the Dynamic World data suite

**Leo Helling**<fnref n="1" />, **Barbara Belletti**<fnref n="2" />, **Mathis Messager**<fnref n="3" />, **Louis Rey**<fnref n="1" />, **Hervé Parmentier**<fnref n="1" />, and **Hervé Piégay**<fnref n="1" />

Global water occurrence data derived from satellite imagery provide critical insights into surface water dynamics, informing science and management of key issues like climate change, water scarcity, and biodiversity loss. The Landsat-based Global Surface Water (GSW) dataset (Pekel et al., 2016) has notably provided an important archive of the global surface water areas and its changes over time. However, its 30-m resolution limits its applicability for smaller river systems. Since the launch of the Copernicus Sentinel-2 (S2) program, higher-resolution imagery (10 m) at recurrence times of 5 days is available, but has not yet been fully exploited. The only large-scale, temporally explicit layer of water occurrence based on S2 was provided by Yang et al. (2020) for the French Metropolitan region, but is limited by noise from clouds, terrain shadows, and seasonal snow.

The recently developed Dynamic World database (Brown et al., 2022) provides a probabilistic, pixel-scale land cover classification of S2 images updated globally in near-real time, potentially enabling computationally efficient, temporally continuous water mapping at high resolution. Here we evaluate DW's water detection capabilities and propose a workflow for large-scale, monthly surface water occurrence mapping. Our approach integrates probabilistic and physical-based water classification, topographic filtering, and cloud masking to overcome limitations of GSW and existing Sentinel-2 applications. DW's water probabilities were compared to spectral indices (NDWI, MNDWI) and combinations of these metrics were explored. We also assessed the potential for topographic data (FABDEM) and pixel-quality measures (CloudScore+) to reduce misclassification and allow the inclusion of more observations. The analysis is applied to the French Rhône-Mediterranean basin, a region chosen due to its diverse hydrological, climatic and geomorphological conditions. Verification is performed using a recently developed high-resolution annual land use product for mainland France (Manière, 2023) and results are compared to the GSW layer.

Preliminary results demonstrate that DW natively detects water in most areas well, but noise from shadow remains a challenge. Through combination with NDWI and further filtering with topographical data, significant classification improvements can be achieved. In addition, the pixel-based cloud-filtering with CloudScore+ enables the inclusion of more observations compared to

<footnote n="1">¹Environment City Society (EVS) laboratory, ENS de Lyon, Lyon, France</footnote>

<footnote n="2">²Environment City Society (EVS) laboratory, Université Jean-Monnet de Saint-Etienne, Saint-Etienne, France</footnote>

<footnote n="3">³EcoFloWs laboratory, National Research Institute for Agriculture, Food and Environment (INRAE), Lyon-Villeurbanne, France</footnote>
previous methods. We implemented this approach on Google Earth Engine with a simple and
efficient algorithm providing monthly water occurrence observations for a whole year. This
scalable workflow holds the potential to address significant limitations of prior methods and
facilitate large-scale surface water mapping at high resolution. The results are especially significant
in areas where in-situ hydrological monitoring is scarce.

References

Brown, C. F., Brumby, S. P., Guzder-Williams, B., et al. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. *Scientific Data*, 9(1), Article 1. https://doi.org/10.1038/s41597-022-01307-4

Manière, L. (2023). *Projet MAPD'O*. https://bassinversant.org/wp-content/uploads/2023/03/presentation_mapdo.pdf

Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. *Nature*, *540*(7633), Article 7633. https://doi.org/10.1038/nature20584

Yang, X., Qin, Q., Yésou, H., et al. (2020). Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. *Remote Sensing of Environment*, *244*, 111803. https://doi.org/10.1016/j.rse.2020.111803