Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6283-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6283-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2021
Research article |
| 13 Dec 2021
| 13 Dec 2021
Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land
Sara Modanesi
Research Institute for Geo-hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
DICEA Department of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139 Florence, Italy
Christian Massari
CORRESPONDING AUTHOR
Research Institute for Geo-hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Alexander Gruber
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Hans Lievens
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Angelica Tarpanelli
Research Institute for Geo-hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Renato Morbidelli
Department of Civil and Environmental Engineering of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Gabrielle J. M. De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Cited
13 citations as recorded by crossref.
- Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation L. Luo et al. 10.1016/j.agsy.2023.103711
- Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space J. Dari et al. 10.5194/essd-15-1555-2023
- Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product? E. Jalilvand et al. 10.1029/2022WR033342
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data G. Paolini et al. 10.1109/JSTARS.2022.3222884
- Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations S. de Roos et al. 10.1016/j.rse.2023.113621
- Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. 10.5194/hess-26-4685-2022
- Towards Understanding the Influence of Vertical Water Distribution on Radar Backscatter from Vegetation Using a Multi-Layer Water Cloud Model P. Vermunt et al. 10.3390/rs14163867
- HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
- Radiometric Re-Compensation of Sentinel-1 SAR Data Products for Artificial Biases due to Antenna Pattern Changes K. Schmidt et al. 10.3390/rs15051377
- Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture L. Zappa et al. 10.1016/j.agwat.2024.108773
- Irrigation estimates from space: Implementation of different approaches to model the evapotranspiration contribution within a soil-moisture-based inversion algorithm J. Dari et al. 10.1016/j.agwat.2022.107537
- A Review of Irrigation Information Retrievals from Space and Their Utility for Users C. Massari et al. 10.3390/rs13204112
12 citations as recorded by crossref.
- Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation L. Luo et al. 10.1016/j.agsy.2023.103711
- Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space J. Dari et al. 10.5194/essd-15-1555-2023
- Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product? E. Jalilvand et al. 10.1029/2022WR033342
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data G. Paolini et al. 10.1109/JSTARS.2022.3222884
- Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations S. de Roos et al. 10.1016/j.rse.2023.113621
- Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. 10.5194/hess-26-4685-2022
- Towards Understanding the Influence of Vertical Water Distribution on Radar Backscatter from Vegetation Using a Multi-Layer Water Cloud Model P. Vermunt et al. 10.3390/rs14163867
- HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
- Radiometric Re-Compensation of Sentinel-1 SAR Data Products for Artificial Biases due to Antenna Pattern Changes K. Schmidt et al. 10.3390/rs15051377
- Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture L. Zappa et al. 10.1016/j.agwat.2024.108773
- Irrigation estimates from space: Implementation of different approaches to model the evapotranspiration contribution within a soil-moisture-based inversion algorithm J. Dari et al. 10.1016/j.agwat.2022.107537
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Worldwide, the amount of water used for agricultural purposes is rising and the quantification...
