Next Article in Journal
A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images
Next Article in Special Issue
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact
Previous Article in Journal
Correction: Luo, Y.P. et al., Using Near-Infrared Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems. Remote Sens. 2018, 10, 1293.
Previous Article in Special Issue
Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
Article

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

1
Climate Change and Water Resources, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Université D’abomey Calavi, Cotonou 03 BP 526, Benin
2
Laboratoire de Physique de l’Atmosphère et de l’Océan, Ecole Supérieure Polytechnique, Université Cheikh Anta Diop, Dakar BP 5085, Senegal
3
CNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France
4
Département de Physique, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar BP 5085, Senegal
5
Laboratory of Hydraulics and Water Control, National Institute of Water, University of Abomey-Calavi (LHME/INE/UAC), Abomey-Calavi 03 BP 526, Benin
6
Faculté d’Agronomie, Université de Parakou, Parakou BP 123, Benin
7
Agence Nationale de la Météorologie (ANAM), Ouagadougou BP 576, Burkina-Faso
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 735; https://doi.org/10.3390/rs11060735
Received: 6 February 2019 / Revised: 12 March 2019 / Accepted: 20 March 2019 / Published: 26 March 2019
This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation. View Full-Text
Keywords: data assimilation; land surface modeling; reanalysis; remote sensing data assimilation; land surface modeling; reanalysis; remote sensing
Show Figures

Graphical abstract

MDPI and ACS Style

Tall, M.; Albergel, C.; Bonan, B.; Zheng, Y.; Guichard, F.; Dramé, M.S.; Gaye, A.T.; Sintondji, L.O.; Hountondji, F.C.C.; Nikiema, P.M.; Calvet, J.-C. Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018. Remote Sens. 2019, 11, 735. https://doi.org/10.3390/rs11060735

AMA Style

Tall M, Albergel C, Bonan B, Zheng Y, Guichard F, Dramé MS, Gaye AT, Sintondji LO, Hountondji FCC, Nikiema PM, Calvet J-C. Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018. Remote Sensing. 2019; 11(6):735. https://doi.org/10.3390/rs11060735

Chicago/Turabian Style

Tall, Moustapha, Clément Albergel, Bertrand Bonan, Yongjun Zheng, Françoise Guichard, Mamadou S. Dramé, Amadou T. Gaye, Luc O. Sintondji, Fabien C.C. Hountondji, Pinghouinde M. Nikiema, and Jean-Christophe Calvet. 2019. "Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018" Remote Sensing 11, no. 6: 735. https://doi.org/10.3390/rs11060735

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop