## 2 Materials and Methods
### Datasets and Data Processing
#### 2.2.3 ASCAT Soil Water Index and GEOV2 Leaf Area Index  
This study uses the ASCAT Soil Water Index (SWI) product distributed by the CGLS through its third version, i.e., SWI-001 Version 3.0. The SWI refers to the soil moisture content in relative units between 0 (dry) and 100 (saturated). It is computed based on a recursive exponential filtering method [52] using the backscatter observations from the ASCAT C-band radar on board MetOP satellites [53; 54]. The SWI retrieved from the exponential filter using a T-value (characteristic time length; the higher the T-values, the smoother the SWI) of one day is used. It represents the SWI in the top soil layer [52]. It is used in the present study as a proxy for SSM. During the period considered in the experiment, the amount of soil moisture data increases in 2015 because the data from MetOP-B (launched in 2012) are used in addition to those from MetOP-A (launched in 2006) (see Table 1). Figure 1 presents the map of the average ASCAT SSM estimate for the whole January 2007-June 2018 period over the study area. For more details on the ASCAT SSM, readers are referred to [29]. Consistent with previous studies, e.g., [55], SSM displays a typical spatial structure, which is dominated by a strong meridional gradient (with wetter soil to the South and drier to the North). Some smaller scale patterns also emerge, such as enhanced SSM spots along the Sourou river (13.04\({}^{\circ}\)N, 3.04\({}^{\circ}\)W) and around Niono in Mali (around 14.15\({}^{\circ}\)N, 5.59\({}^{\circ}\)W) in the north eastern part of the domain. These are consistent with the existing mapping of water bodies in the region (e.g., [56]) and are also probably related to the presence of irrigated rice puddles and crops [57].  
For the purpose of assimilating the SSM product, a rescaling of observations into model climatology space is needed in order to avoid introducing any artificial bias in the system caused, for example, by a possible mis-specification of physiographic parameters related to soil texture types [54; 58]. To that end, the SWI product is transformed into model-equivalent SSM (from the model second layer of soil, 1-4 cm), based on the first two statistical moments (the mean and the variance) through a linear transformation [59]. The relevance of performing a seasonal rescaling was emphasized by several studies (e.g., [37; 47]). In this study, the matching of SSM statistical distributions was made on a monthly basis by using a 3-month moving window over the January 2007-June 2018 period after screening for the presence of urban areas (>15%) and complex terrains (1500 m a.s.l.). Finally, the SWI observations are interpolated by an arithmetic average to the 0.25\({}^{\circ}\) model grid points (from their original 12.5 km spatial resolution).  
The GEOV2 LAI observations are also distributed by the CGLS. They are retrieved from the SPOT-VGT and PROBA-V satellite data using the methodology prescribed in [60]. The 1 km \(\times\) 1 km resolution observations are interpolated to 0.25\({}^{\circ}\) model grid points through an arithmetic average as in [19], so that at least 75% of the grid points are observed. In terms of temporal resolution, LAI observations are available with a 10-day frequency (at best). Figure 1b illustrates the averaged LAI (January 2001-June 2018). As in Figure 1a, the spatial structure of LAI is dominated by a strong meridional gradient (from lower LAI to the North to higher LAI to the South). This corresponds to three climatic regions: The Sahel (SH), the Sudan-Sahel (SS), and the Sudan-Guinea (SG). The country's climate is characterized by two distinct seasons: A dry season and a rainy season (May to October) with growing seasons varying from six (SG region) to three (SH region) months [22]. Observations are rescaled to match the spatial resolution of the two atmospheric forcing data-sets, 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and 0.50\({}^{\circ}\)\(\times\) 0.50\({}^{\circ}\) for ERA5 and ERA-Interim, respectively