Research Area

Hydrological Monitoring

Observing the water cycle with next-generation techniques. The HydroLAB group develops and applies innovative — often low-cost and non-contact — methods to measure streamflow, soil moisture, surface velocity and water quality, bridging in-situ sensing, satellite remote sensing and image-based observation.

5 research lines UAS Remote sensing Image-based methods Low-cost sensors
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Topics

Five lines of research

Each area below links to a dedicated page with concepts, methods, projects and selected results. Start with the overview, then dive into the individual techniques.

Overview

Innovation in Hydrological Monitoring

How remote sensing, unmanned aerial systems, image-based methods and low-cost sensors are reshaping the way hydrological processes are observed — including the generalized HARMONIOUS workflow for reproducible UAS-based monitoring.

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Streamflow

Flow-Area Rating Curve

Stage–discharge relationships that turn simple, inexpensive water-level readings into continuous streamflow estimates — together with methods to quantify and reduce their inherent uncertainty.

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Soil water

Soil Moisture Monitoring

Soil moisture as a key control of the hydrological cycle — from in-situ sensor networks and satellite retrievals to spatial downscaling and the SMAR analytical relationship.

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Surface velocity

Image Velocimetry

Non-contact estimation of river surface velocities from imagery and video (PIV, PTV, STIV, LSPIV), developed within the HARMONIOUS COST Action for UAS-based river monitoring.

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Water quality

Water Quality Monitoring

Low-cost, image-based and remote-sensing methods combined with machine learning to monitor water quality in rivers and lakes — from turbidity to plastic detection — through scalable, citizen-driven solutions.

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HydroLAB · University of Naples Federico II — salvatoremanfreda.it. Part of the group’s activity within the IAHS MOXXI working group and the HARMONIOUS COST Action on innovative environmental monitoring.

In this section

Part of the HydroLAB research programme.

Publications on this topic

  • Dal Sasso, S. F., R. Ljubicic, A. Pizarro, S. Pearce, I. Maddock and S. Manfreda, Evaluating SSIMS-flow velocimetry performances under varying seeding densities: A proof-of-concept field study., 2026. [pdf]
  • Zhuang, R., S. Manfreda, Y. Zeng, L. Zhang, B. Szabó, P. Nasta, N. Romano and Z. Su, Bridging Satellite and UAS Scales for Surface Soil Moisture Mapping: A Two-Step Random Forest Downscaling Framework., Remote Sensing Applications: Society and Environment, 2026. [doi]
  • Marye, A. T., C. Caramiello, D. De Nardi, D. Miglino, G. Proietti, K. C. Saddi, C. Biscarini, S. Manfreda, M. Poggi and F. Tauro, Remote sensing for monitoring macroplastics in rivers: A review., WIREs Water, 2025. [pdf] [doi]
  • Miglino, D., S. Jomaa, M. Rode, K. C. Saddi, F. Isgrò and S. Manfreda, Technical note: Image processing for continuous river turbidity monitoring — full-scale tests and potential applications., Hydrology and Earth System Sciences, 2025. [pdf] [doi]
  • Wang, Y., P. Leng, J. Ma, S. Manfreda, C. Ma, Q. Song, G. Shang, X. Zhang and Z. L. Li, Generation of root zone soil moisture from the integration of all-weather satellite surface soil moisture estimates and an analytical model: A preliminary result in China., Journal of Hydrology, 2024. [pdf] [doi]
  • Albano, R., T. Lacava, A. Mazzariello, S. Manfreda, J. Adamowski and A. Sole, How can seasonality influence the performance of recent microwave satellite soil moisture products?., Remote Sensing, 2024. [pdf] [doi]
  • Manfreda, S., D. Miglino, K. C. Saddi, S. Jomaa, A. Etner, M. Perks, D. Strelnikova, S. Peña-Haro, I. Maddock, F. Tauro, S. Grimaldi and Y. Zeng, Advancing river monitoring using image-based techniques: Challenges and opportunities., Hydrological Sciences Journal, 2024. [pdf] [doi]
  • Han, Q., Y. Zeng, L. Zhang, C. Cira, E. Prikaziuk, T. Duan, C. Wang, B. Szabó, S. Manfreda, R. Zhuang and B. Su, Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale., Geoscientific Model Development, 2023. [pdf] [doi]
  • Mazzariello, A., R. Albano, T. Lacava, S. Manfreda and A. Sole, Intercomparison of recent microwave satellite soil moisture products on European ecoregions., Journal of Hydrology, 2023. [pdf] [doi]
  • Pizarro, A., S. F. Dal Sasso and S. Manfreda, VISION: VIdeo StabilisatION using automatic features selection for image velocimetry analysis in rivers., SoftwareX, 2022. [pdf] [doi]
  • Pizarro, A., P. Dimitriadis, T. Iliopoulou, S. Manfreda and D. Koutsoyiannis, Stochastic analysis of the marginal and dependence structure of streamflows: From fine-scale records to multi-centennial paleoclimatic reconstructions., Hydrology, 2022. [doi]
  • Paridad, P., S. F. Dal Sasso, A. Pizarro, L. Mita, M. Fiorentino, M. R. Margiotta, F. Faridani, A. Farid and S. Manfreda, Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions., Acta Horticulturae, 2022. [pdf] [doi]
  • Zhang, L., Y. Zeng, R. Zhuang, B. Szabó, S. Manfreda, Q. Han and Z. Su, In situ observation-constrained global surface soil moisture using Random Forest model., Remote Sensing, 2021. [doi]
  • Dal Sasso, S. F., A. Pizarro and S. Manfreda, Recent advancements and perspectives in UAS-based image velocimetry., Drones, 2021. [doi]
  • Dal Sasso, S. F., A. Pizarro, S. Pearce, I. Maddock and S. Manfreda, Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales., Journal of Hydrology, 2021. [doi]
  • Petropoulos, G. P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli and S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for soil water content and evaporative fraction retrievals in a Mediterranean setting., International Journal of Remote Sensing, 2021. [pdf] [doi]
  • Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Ben-Dor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia., IEEE Transactions on Geoscience and Remote Sensing, 2021. [doi]
  • Pizarro, A., S. F. Dal Sasso and S. Manfreda, Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation., Hydrological Processes, 2020. [doi]
  • Dal Sasso, S. F., A. Pizarro and S. Manfreda, Metrics for the quantification of seeding characteristics to enhance image velocimetry performance in rivers., Remote Sensing, 2020. [doi]
  • Perks, M. T., S. F. Dal Sasso, A. Hauet, E. Jamieson, J. Le Coz, S. Pearce, S. Peña-Haro, A. Pizarro, D. Strelnikova, F. Tauro, J. Bomhof, S. Grimaldi and S. Manfreda, Towards harmonisation of image velocimetry techniques for river surface velocity observations., Earth System Science Data, 2020. [doi]
  • Manfreda, S., A. Pizarro, T. Moramarco, L. Cimorelli, D. Pianese and S. Barbetta, Potential advantages of flow-area rating curves compared to classic stage-discharge relations., Journal of Hydrology, 2020. [doi]
  • Zhuang, R., Y. Zeng, S. Manfreda and Z. Su, Quantifying long-term land surface and root zone soil moisture over Tibetan Plateau., Remote Sensing, 2020. [pdf] [doi]
  • Baldwin, D., S. Manfreda, H. Lin and E. A. H. Smithwick, Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model., Remote Sensing, 2019. [pdf] [doi]
  • Manfreda, S., On the derivation of flow rating-curves in data-scarce environments., Journal of Hydrology, 2018. [doi]
  • Faridani, F., A. Farid, H. Ansari and S. Manfreda, A modified version of the SMAR model for estimating root-zone soil moisture from time series of surface soil moisture., Water SA, 2017. [pdf] [doi]
  • Albano, R., S. Manfreda and G. Celano, MYSIRR: Minimalist agro-hYdrological model for Sustainable IRRigation management — soil moisture and crop dynamics., SoftwareX, 2017. [pdf] [doi]
  • Baldwin, D., S. Manfreda, K. Keller and E. A. H. Smithwick, Predicting root zone soil moisture with soil properties and satellite near-surface moisture data at locations across the United States., Journal of Hydrology, 2017. [pdf] [doi]
  • Faridani, F., A. Farid, H. Ansari and S. Manfreda, Estimation of the root-zone soil moisture using passive microwave remote sensing and SMAR model., Journal of Irrigation and Drainage Engineering, 2016. [doi]
  • Calamita, G., A. Perrone, L. Brocca, B. Onorati and S. Manfreda, Field test of a multi-frequency electromagnetic induction sensor for soil moisture monitoring in southern Italy test sites., Journal of Hydrology, 2015. [pdf] [doi]
  • Manfreda, S., L. Brocca, T. Moramarco, F. Melone and J. Sheffield, A physically based approach for the estimation of root-zone soil moisture from surface measurements., Hydrology and Earth System Sciences, 2014. [doi]
  • Manfreda, S., T. Lacava, B. Onorati, N. Pergola, M. Di Leo, M. R. Margiotta and V. Tramutoli, On the use of AMSU-based products for the description of soil water content at basin scale., Hydrology and Earth System Sciences, 2011. [doi]
  • Manfreda, S., T. M. Scanlon and K. K. Caylor, On the importance of accurate depiction of infiltration processes on modelled soil moisture and vegetation water stress., Ecohydrology, 2010. [doi]
  • Manfreda, S. and M. Fiorentino, A Stochastic Approach for the Description of the Water Balance Dynamics in a River Basin., Hydrology and Earth System Sciences, 2008. [doi]
  • Manfreda, S., D. R. Cox, V. Isham, A. Porporato and I. Rodríguez-Iturbe, Reply to the Comment by S. Nadarajah on Space-time modeling of soil moisture: Stochastic rainfall forcing with heterogeneous vegetation., Water Resources Research, 2007. [doi]
  • Manfreda, S., M. McCabe, E. F. Wood, M. Fiorentino and I. Rodríguez-Iturbe, Spatial Patterns of Soil Moisture from Distributed Modeling., Advances in Water Resources, 2007. [doi]
  • Manfreda, S. and I. Rodrìguez-Iturbe, On the Spatial and Temporal Sampling of Soil Moisture Fields., Water Resources Research, 2006. [doi]
  • Rodríguez-Iturbe, I., V. Isham, D. R. Cox, S. Manfreda and A. Porporato, Space-time modeling of soil moisture: stochastic rainfall forcing with heterogeneous vegetation., Water Resources Research, 2006. [doi]
  • Isham, V., D. R. Cox, I. Rodríguez-Iturbe, A. Porporato and S. Manfreda, Representation of Space-Time Variability of Soil Moisture., Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2005. [pdf] [doi]
  • Manfreda, S., M. Fiorentino and V. Iacobellis, DREAM: a Distributed model for Runoff, Evapotranspiration, and Antecedent Soil Moisture Simulation., Advances in Geosciences, 2005. [doi]

About the author

He is Full Professor of Hydrology and Hydraulic Constructions at the University of Naples Federico II. He is currently chair of the IAHS MOXXI working group. His research primarily centers on hydrological modeling and monitoring. Recognizing the challenges posed by the complexity and limitations of traditional hydrological observations, he actively explores advanced and alternative monitoring techniques, such as the utilization of Unmanned Aerial Systems (UAS) coupled with image processing.