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Remote sensing holds promise for ecosystem‐level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field‐based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory‐based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of “space for time” approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress. 

Abstract. A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought.