Tower-Based Remote Sensing Reveals Mechanisms Behind A Two-Phased Spring Transition In A Mixed-Species Boreal Forest

The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information.Plain Language Summary The boreal forest is one of the most sensitive regions on the planet to climate change, yet its sensitivity remains poorly understood. In particular, the timing and drivers of the spring transition, as the forest changes from a winter adapted state to a summer adapted state, carry significant uncertainties. Remote sensing metrics can be used to characterize the spring transition, but their interpretation is complicated by persistent greenness, frequent snow cover, and a high prevalence of forests containing both deciduous and evergreen species. We collected tower-based remotely sensed metrics, stem radius, and carbon uptake measurements and show that the spring transition occurs in two distinct phases. The first phase is a reactivation of photosynthesis in evergreens and is triggered by thawed stems, warm air temperature, and moist soil. The second phase is a change in evergreen photoprotective pigment levels and the leaf-out of deciduous species. It is triggered by soil thaw. Both phases were detected with different remote sensing metrics that depended on species type. Our results illustrate how satellite measurements could be improved to capture the spring transition over diverse landscapes and what environmental factors control the spring transition.