Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

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Abstract km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land ; volume:25 ; number:12 ; year:2021 ; pages:6283-6307 ; extent:25
Hydrology and earth system sciences ; 25, Heft 12 (2021), 6283-6307 (gesamt 25)

Classification: Natürliche Ressourcen, Energie und Umwelt

Creator: Modanesi, Sara
Massari, Christian
Gruber, Alexander
Lievens, Hans
Tarpanelli, Angelica
Morbidelli, Renato
De Lannoy, Gabrielle J. M.

DOI: 10.5194/hess-25-6283-2021

URN: urn:nbn:de:101:1-2021121604164096616627

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:27 AM CEST

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Modanesi, Sara
Massari, Christian
Gruber, Alexander
Lievens, Hans
Tarpanelli, Angelica
Morbidelli, Renato
De Lannoy, Gabrielle J. M.

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