## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Fluxes
#### 3.2.3 Vegetation Water Fluxes  
Transpiration dominates the continental water cycle [165; 166; 167]. It is therefore crucial to better represent and constrain vegetation activity and its contribution to the continental energy and water cycles. Historically, continental microwave remote sensing has been focusing on soil moisture retrieval [168; 169]. Vegetation was mostly considered during the retrieval of surface soil moisture as a by-product as it affects the signal penetration to the surface [170; 171]. The attenuation of the microwave signal when passing through the vegetation layer depends on the so-called Vegetation Optical Depth (VOD). The VOD depends on the frequency of the sensor, on the water content of the plant (trunk, branches, leaves) as well as on the biomass [172; 173; 174; 175; 176; 177; 178]). As such, there has been recent interest in using VOD to assess biomass changes [178; 179; 180; 37; 39; 170; 181] or plant water stress strategies [177]. The VOD might prove especially useful in regions where soil moisture retrieval is especially complicated by dense vegetation coverage (e.g., forested landscape). C or X-band do not penetrate much into the canopy and are more sensitive to the top part of the canopy. Figure 15 shows that L band is sensitive up to very dense canopies (300 Mg/hectare) enabling the probing of almost all vegetation canopies [182]. A further advantage of the VOD is that data can be acquired even in the presence of clouds. C-band senses a thicker layer of the canopy and branches so that the signal somewhat differs from the X-band signal especially in humid regions with dense canopy coverage such as the Amazon rainforest (Figure 16). The lower frequency signal, in L-band, senses a thicker vegetation layer corresponding to the above ground biomass such as the ones derived in [183] (Figure 15). Figure 14: Maps of the Paráná region for November 2009 to February 2010 showing (**top–left**, clockwise): GOSAT-JULES difference, JULES wetland emissions, JULES wetland fraction, GRACE water storage anomaly, Sustainable Wetland Adaptation and Mitigation Program (SWAMP) wetland fraction and JULES rainfall amount [163]. Most of our observations of the land surface relate to surface variables (soil moisture, biomass, vegetation coverage among others) but ultimately one of the key objectives is to better constrain the vegetation water, energy and carbon fluxes. Recently, direct observations of a proxy for photosynthesis have been made possible, at the global scale using Solar-Induced Fluorescence (SIF) [184; 185; 186; 187; 188; 189; 190; 191; 192]). SIF is a by-product of photosynthesis, which is directly related and even nearly-proportional to photosynthesis in most conditions and is a direct indicator of vegetation stress [156]. One of the advantages of SIF is that it provides information that is not included in the other datasets (e.g., vegetation indices or VOD). Indeed, SIF is able to correctly capture the seasonal cycle across diverse climates dominated by phenology and temperature (Figure 16 top panel the northeastern US), by temperature and water stress (Figure 16 middle panel Mediterranean Spain) or in rainforests, where vegetation indices struggle to correctly define the seasonality which is imposed by leaf aging [193; 194; 195]) and radiation structure [196] (Figure 16 lower panel, the Tropical Amazon). SIF thus provides unique information to constrain land-surface fluxes and its response to droughts (e.g., [197]). It is, however, particularly noisy [186] so that averaging in both time and space is needed to extract a meaningful signal. This noise is also a major road block for direct use in either a data assimilation context or to directly assess surface fluxes. As a result, recent approaches have tried to use SIF not directly but as a target of machine learning strategies using MODIS broadband reflectances (Reconstructed SIF, [198]).