Thu, Aug 05, 2021:On Demand
Background/Question/Methods
Land surface phenology, the tracking of growing season productivity via satellite remote sensing, enables ecosystem scale tracking of the drivers and consequences of a changing climate, but its utility is limited in some areas. In dryland ecosystems low vegetation cover is the primary limitation in LSP detection, especially from current satellite sensors. Low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using both simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty.
Results/Conclusions We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can sometimes have undetectable phenology even with 100% fractional vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still be in excess of 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Our work also highlights some understudied limitations in drylands LSP. For example some areas may have the occasional season which meets a minimum VI threshold for detecting phenology, potentially indicating a higher than normal productive year. Our results show that these years may be false positive detections due to VI uncertainty as opposed to increased productivity. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.
Results/Conclusions We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can sometimes have undetectable phenology even with 100% fractional vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still be in excess of 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Our work also highlights some understudied limitations in drylands LSP. For example some areas may have the occasional season which meets a minimum VI threshold for detecting phenology, potentially indicating a higher than normal productive year. Our results show that these years may be false positive detections due to VI uncertainty as opposed to increased productivity. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.