ORCID Profile
0000-0002-1756-1096
Current Organisation
CNRS
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: MDPI AG
Date: 03-11-2017
DOI: 10.3390/RS9111119
Publisher: Elsevier BV
Date: 07-2016
Publisher: Elsevier BV
Date: 07-2016
Publisher: American Meteorological Society
Date: 27-05-2015
Abstract: The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
Publisher: Elsevier BV
Date: 10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: SPIE
Date: 07-10-2010
DOI: 10.1117/12.865751
Publisher: MDPI AG
Date: 20-11-2020
DOI: 10.3390/RS12223816
Abstract: Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q·ha−1 to an RMSE 7.49 q·ha−1). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications’ accuracy.
No related grants have been discovered for Ahmad Al Bitar.