Submitted
Modanesi, S., Massari, C., Bechtold, M., Lievens, H., Tarpanelli, A., Brocca, L., Zappa, L. and DeLannoy, G. J. M. Challenges and benefits of quantifying irrigation through the assimilation ofSentinel-1 backscatter observations into Noah-MP. Submitted to HESS, 2022.
Published in peer reviewed journal
Elwan, E., Le Page, M., Jarlan, L., Brocca, L., Modanesi, S., Dari, J., Quintana Segui, P., Zribi, M.Irrigation mapping on two contrasted climatic contexts using Sentinel-1 and Sentinel-2 data.Water 2022, 14, 804. https://doi.org/10.3390/w14050804
Modanesi, S., Massari, C., Gruber, A., Lievens, H., Tarpanelli, A., Morbidelli, R., and De Lannoy,G. J. M. Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land,Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, 2021.
Modanesi, S., Dari, J., Massari, C., Tarpanelli, A., Barbetta, S., De Lannoy, G. J. M., Gruber, A.,Lievens, H., Bechtold, M., Quast, R., Vreugdenhil, M., Zribi, M., Le Page, M., Brocca, L. Acomparison between satellite- and model-based approaches developed in the ESAIrrigation+project framework to estimate irrigation quantities. IEEE International Workshop onMetrology for Agriculture and Forestry (MetroAgriFor), 2021, pp. 268-272, doi:10.1109/MetroAgriFor52389.2021.9628453, 2021.
Massari, C., Modanesi, S., Dari, J., Gruber, A., De Lannoy, G.J.M., Girotto, M., Quintana-Seguí,P., Le Page, M.; Jarlan, L., Zribi, M., Ouaadi, N., Vreugdenhil, M., Zappa, L., Dorigo, W., Wagner,W., Brombacher, J., Pelgrum, H., Jaquot, P., Freeman, V., Volden, E., Fernandez Prieto, D.,Tarpanelli, A., Barbetta, S., Brocca, L. A Review of Irrigation Information Retrievals from Spaceand Their Utility for Users. Remote Sens., 13, 4112. https://doi.org/10.3390/rs13204112, 2021.
De Santis, D., Biondi, D., Crow, W.T., Camici, S., Modanesi, S., Brocca, L., Massari, C.Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An ExtensiveExperiment in Over 700 Catchments Throughout Europe Water Resour. Res., 57, (6), Articlee2021WR029643, https://doi.org/10.1029/2021WR029643, 2021.
Modanesi, S., Massari, C., Camici, S., Brocca, L., Amarnath, G. Do satellite surface soil moistureobservations better retain information about crop-yield variability in drought conditions?Water Resour. Res., 56, (2), Article e2019WR025855, https://doi.org/10.1029/2019WR025855, 2020.
Azimi, S., Dariane, A. B., Modanesi, S., Bauer-Marschallinger, B., Bindlish, R., Wagner, W., & Massari, C. (2020). Assimilation of Sentinel 1 and SMAP–based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins. Journal of Hydrology, 581, 124367.
Bauer-Marschallinger, B., Naeimi, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., Modanesi, S.,Massari, C., Ciabatta, L., Brocca, L., Wagner, W. Towards Global Soil Moisture Monitoring withSentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Trans. Geosci. Remote Sens. DOI: 10.1109/TGRS.2018.2858004, 2018
Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T., Modanesi, S., Ciabatta, L.,Massari, C., Brocca, L., Wagner, W. Soil Moisture from Fusion of Scatterometer and SAR:Closing the Scale Gap with Temporal Filtering. Remote Sens. 2018, 10(7), 1030.https://doi.org/10.3390/rs10071030, 2018.