1,2,- 1Centre for Ecological Research and Forestry Applications (CREAF), Cerdanyola del Vallès, Spain
- 2Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain.
- 3Microwaves and Radar Institute, German Aerospace Centre (DLR), 82234 Wessling, Germany.
- 4Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
Decreasing water availability due to climate change reduces the vegetation water pool. This affects the capacity of vegetation to mediate land-atmosphere feedbacks through photosynthesis and transpiration and impacts vegetation health worldwide [1]. Thus, it is paramount to model vegetation water storage (VWS; the mass of water per ground area) in order to monitor vegetation function. Passive microwave sensors on satellites like the Soil Moisture Active-Passive (SMAP) quantify the attenuation that vegetation exerts over land microwave emissions expressed as the vegetation optical depth (VOD). The VOD is linearly related to VWS via the b factor (VOD = b·VWS) and is a good proxy of VWS. Still, satellite-based VWS estimates have been scarcely validated and, importantly, values of b are purely empirical and are time-invariant, omitting relevant phenological changes in VWS [2]. The lack of accurate estimation of b values limits our capacity to better understand the VOD-VWS relationship, to accurately model VWS, or to further explore the transit time of water in vegetation [3]. Here, we bridge this gap by using newly generated, quasi-global benchmark maps of VWS for leaves (VWSleaf), wood (VWSwood) and their summation (VWStotal). These maps are based on ground information of plant traits (specific leaf area and wood density) from the TRY database [4] and their relationship with leaf and wood water storage [5]. Here, we first find that the linear relationship between SMAP L-band VOD and VWS holds when VWStotal is used. We extend this analysis to AMSR2 X- and Ku-VOD data and find linear relationships with VWSleaf (we test against leaves due to the shallow sensing depth of X- and Ku-VOD). Second, we assess the SMAP VWS datasets against VWStotal and find that spatial differences in VWS are biome-dependent. Third, we divide global maps of annual averages of VOD by VWStotal (for L-VOD) and by VWSleaf (for X- and Ku-VOD) to derive global, multi-frequency maps of b and to study its spatiotemporal variation. Results provide new insights on the accuracy of VOD-derived VWS estimates and open a new path towards estimating VWS for different canopy layers, which has wide implications for the remote sensing and the plant ecology research communities.
[1] Grossiord, C., et al. (2020). Plant responses to rising vapor pressure deficit. New Phytologist, 226, 1550–1566.
[2] Togliatti, K., et al. (2019). Satellite L-band vegetation optical depth is directly proportional to crop water in the US Corn Belt. Remote Sensing of Environment, 233, 111378.
[3] Felton, A. J., et al. (2025). Global estimates of the storage and transit time of water through vegetation. Nature Water, 3(1), 59-69.
[4] Kattge, J., et al. (2020). TRY plant trait database–enhanced coverage and open access. Global Change Biology, 26, 119-188.
[5] Stewart, L., et al. (submitted). Wood You Be-Leaf It? The First Trait-Based Map of Global Vegetation Water Storage. To be presented at EGU 2026.
How to cite: Chaparro, D., Stewart, L., Mencuccini, M., Jagdhuber, T., and Binks, O.: Global assessment of SMAP-derived vegetation water storage and estimation of the b-parameter using a trait-based VWS map, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13532, https://doi.org/10.5194/egusphere-egu26-13532, 2026.
