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An observation-based approach for global runoff estimation: exploiting satellite soil moisture and Grace

S. Camici, L. Brocca, C. Massari, G. Giuliani, N. Sneeuw, H. Hashemi Farahani, M. Restano, and J. Benveniste. (2020)(EGU 2020, Vienna, 4-8 May).

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Univ. -Prof. Ph. D. Christian Holm University of Stuttgart

Replication Data for: Generating Minimal Training Sets for Machine Learned PotentialsJ. Finkbeiner, S. Tovey, and C. Holm. Dataset, (2024)Related to: Jan Finkbeiner, Samuel Tovey, Christian Holm: Generating Minimal Training Sets for Machine Learned Potentials (2023). arXiv: 2309.03840.

Christian León University of Stuttgart

Christian Schmid University of Stuttgart

Christian Hölzl University of Stuttgart

Christian Bartsch University of Stuttgart

Other publications of authors with the same name

Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy.L. Ciabatta, L. Brocca, C. Massari, T. Moramarco, S. Gabellani, S. Puca, and W. Wagner. Int. J. Applied Earth Observation and Geoinformation, (2016)Potential of NGGM MAGIC for global runoff estimation: the STREAM approachS. Camici, L. Brocca, C. Massari, A. Tarpanelli, N. Sneeuw, and et al.. (2023)Satellite observations for runoff and river discharge estimation: STREAMRIDE approach&\#160$\mathsemicolon$S. Camici, A. Tarpanelli, L. Brocca, C. Massari, K. Nielsen, N. Sneeuw, M. Tourian, S. Yi, M. Restano, and J. Benveniste. (March 2022)An observation-based approach for global runoff estimation: exploiting satellite soil moisture and GraceS. Camici, L. Brocca, C. Massari, G. Giuliani, N. Sneeuw, H. Hashemi Farahani, M. Restano, and J. Benveniste. (2020)(EGU 2020, Vienna, 4-8 May).A Review of the Applications of ASCAT Soil Moisture Products.L. Brocca, W. Crow, L. Ciabatta, C. Massari, P. de Rosnay, M. Enenkel, S. Hahn, G. Amarnath, S. Camici, A. Tarpanelli and 1 other author(s). IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 10 (5): 2285-2306 (2017)Soil moisture and rainfall retrieval from AMSR2 data in Italy.E. Santi, S. Paloscia, S. Pettinato, L. Brocca, L. Ciabatta, and C. Massari. IGARSS, page 1651-1654. IEEE, (2016)Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction.C. Massari, S. Camici, L. Ciabatta, and L. Brocca. Remote Sensing, 10 (2): 292 (2018)Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles.B. Bauer-Marschallinger, V. Freeman, S. Cao, C. Paulik, S. Schaufler, T. Stachl, S. Modanesi, C. Massari, L. Ciabatta, L. Brocca and 1 other author(s). IEEE Trans. Geoscience and Remote Sensing, 57 (1): 520-539 (2019)Remote Sensing of Terrestrial Rainfall From Ku-Band Scatterometers.L. Brocca, C. Massari, L. Ciabatta, W. Wagner, and A. Stoffelen. IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 9 (1): 533-539 (2016)The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison.T. Pellarin, C. Román-Cascón, C. Baron, R. Bindlish, L. Brocca, P. Camberlin, D. Fernández-Prieto, Y. Kerr, C. Massari, G. Panthou and 3 other author(s). Remote Sensing, 12 (3): 481 (2020)