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Temperate forests and grasslands have different drought response strategies. Trees often control their stomata to reduce water loss in order to prevent hydraulic failure and ensure the survival of their aboveground biomass. In contrast, grasses generally have a less strong stomatal control and maintain high photosynthesis and transpiration until the soil moisture gets depleted. That is when their leaves wilt and the grasslands see a reduction in their aboveground green biomass. Both the increased stomatal control and the reduction in aboveground biomass decrease the surface conductance, i.e. decrease the exchange of water and carbon between the leaves and the atmosphere. Therefore, the drought response of vegetation has major impacts on the land-atmosphere fluxes of water, energy, and carbon, as well as the development of droughts and heat waves.Here, we study to which extent the different drought responses of forests and grasslands are reflected in remote sensing data. We hypothesise that (i) for both forests and grasslands, there are drought-induced changes in thermal infrared based data (e.g., land surface temperature), because of the decreased surface conductance for both land cover types. Furthermore, we hypothesise that (ii) drought-induced changes in optical based indices (e.g. the normalized difference vegetation index) can be detected for grasslands but not for forests, because of the different drought response strategies of trees and grasses. In this study we jointly analyze site-scale and remote sensing data. We use eddy-covariance data for 52 forest sites and 11 grassland sites across the northern hemisphere to calculate the surface conductance, and we identify droughts from low soil moisture content and reduced surface conductance. Then we analyse how the drought response is reflected in thermal and optical indices derived from MODIS satellite data.The results show that our hypotheses are largely confirmed. The land surface temperature increases with drought-induced reductions in surface conductance for both forests and grasslands. By contrast, the optical indices show a much stronger response for grasslands than for forests. We conclude that the different canopy-level drought response strategies of trees and grasses are reflected in remote sensing data. Our study highlights that the joint investigation of multiple remote sensing data streams enables insights beyond the analyses of individual indices, such as a better understanding of the drought response strategies across land cover types. Further, a host of different satellite data should be used to monitor and study vegetation drought responses of forests and grasslands to ensure accurate inference on the implications on water, energy, and carbon fluxes.

Soil water availability is a critical requirement for vegetation functioning in a water-limited regime. Vegetation takes up water from varying soil depths depending on their rooting location and soil moisture availability. The uptake depth varies spatially across climate regimes and vegetation types and temporally between seasons. Yet, a scientific consensus on the global relevance of near-surface and sub-surface soil moisture for vegetation functioning is still lacking and is the focus of this study. In particular, we calculate the correlation between the Near-Infrared Reflectance of Vegetation (NIRv) with both satellite-derived near-surface soil moisture from ESA-CCI and terrestrial water storage from GRACE. This is done globally and with monthly data during the growing season at each grid cell and accounting for the confounding effects of temperature and radiation. We analyze how these correlations vary spatially across varying vegetation types and climatic regimes, and temporally between all growing season months and particularly dry months. Finally, we repeat the analyses using Sun-induced fluorescence (SIF) data instead of NIRv. We find that NIRv and SIF correlate more strongly with near-surface soil moisture compared to terrestrial water storage in semi-arid regions with low tree cover. This suggests that the vegetation preferentially takes up water from near-surface soil moisture whenever available to meet its transpiration demand. In contrast, in regions with more tree cover and in drier regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture. This indicates that trees can make use of their deep rooting systems to access deeper soil moisture resources, similar to vegetation in arid regions. In particularly dry months, correlations with near-surface soil moisture increase while this is even more the case with terrestrial water storage, highlighting the relevance of deeper water resources during rain-scarce periods.Overall, while direct observations of sub-surface soil moisture are scarce, this study employs different satellite-based data streams in order to estimate the relevance of near-surface versus sub-surface soil moisture for vegetation functioning. This can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.

Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of their rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture and terrestrial water storage, present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterise vegetation functioning through the Near-Infrared Reflectance of Vegetation (NIRv), and compare its relation to (i) near-surface soil moisture from ESA-CCI and (ii) total water storage from GRACE at the monthly time scale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. In line with this, an attribution analysis that examines the relative importance of these soil water storages for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.