## 4 Potential Applications, Discussions, and Perspectives
### Which Alternative Data to Better Constrain LDAS-Monde? LDAS-Monde re-analyses presented above were repeated, assimilating only SSM or LAI, the results suggested that most of the skill came from the assimilation of LAI (not shown). Assimilating LAI permits to analyze not only LAI itself, but also the root zone soil moisture. While assimilating SSM does mainly affect the first layers of soil (layer 2, 1 cm to 4 cm and layer 3, 4 cm to 10 cm), assimilating LAI has an impact on deeper layers (up to 60 cm) and is more efficient to analyse the root zone soil  
Figure 11: Seasonal (**a**) root mean square differences (RMSD) and (**b**) correlation values between observed leaf area index (LAI) and (in blue) a 12-month model run, (in red) a 12-month model run initialised by analysed conditions from LDAS-Monde. (**c**) RMSD differences values between a 12-month model run and a 12-month model run initialised by analysed conditions from LDAS-Monde. moisture too. This has also been suggested by [13], when analysing the ISBA LSM sensitivity to the assimilated observations through the SEKF Jacobians. However, the LAI product used in this study is available every 10 days at best, making it less efficient to constrain the ISBA LSM, particularly in areas of the world affected by clouds for long periods of time (e.g., areas affected by the monsoon regime). Another caveat is the use of a single LAI value for all vegetation types that are represented in SURFEX. As detailed in [20], the innovation is computed from the difference between the observed LAI and the modelled LAI aggregated over all the vegetation types. Then, the Kalman gain is calculated for each individual vegetation type. The analysis increment is added to the background for each vegetation type, producing a vegetation-dependent analysis update. The vegetation dependence is introduced in the Kalman gain via the Jacobian elements. As mentioned already, the possibility of having LAI estimates for each type of vegetation is under investigation [59] and has the capacity of overcoming the above-mentioned weakness. An extension of this work is under development to assimilate in LDAS-Monde the disaggregated LAI product for each vegetation type independently, with very promising preliminary results [65]. Microwave remote sensing over land has mainly focused on soil moisture retrieval [66; 67] and vegetation was mostly considered during the retrieval of surface soil moisture as a by-pass product affecting the signal penetration to the surface [68; 69]. The attenuation of the signal (i.e., when passing through the vegetation) depends on the vegetation optical depth (VOD). VOD describes the attenuation of radiation due to scattering and absorption within the vegetation layer, which is caused by the water contained in the vegetation. It is function of the frequency of the microwave sensor, the water content of the plant (trunk, branches, leaves), as well as the biomass (e.g., [70; 71; 72; 73]. VOD can be retrieved from microwave data, for example, from the L-band soil moisture and ocean salinity (SMOS) mission [74] or the C-band advanced scatterometer (ASCAT) mission on-board the meteorological operational satellite A (MetOp-A) [75; 76]. VOD can be related to LAI (e.g., [77; 78; 79; 80]. Figure 12 presents a map of temporal correlation coefficient values between modelled LAI and microwave-derived VOD from radar backscatter measurements of ASCAT [75; 76] (Figure 12a), as well as their distribution (Figure 12b) for 2010-2016. High correlations values are found in large parts of the domain, with a median value of 0.57. The northern part of the domain shows R values greater than 0.7, while smaller R values (and even negative R values) are found in the southern part of the domain. Over dry soils, sub-surface scattering from the microwave signal potentially affects the VOD estimates ([PERSON], TU WIEN, personal communication, April 2018). The same VOD dataset has a higher median R value with the observed LAI, that is, 0.88.