Location: Range Management ResearchTitle: Constraints and opportunities for detecting land surface phenology in drylands
Submitted to: Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/3/2021
Publication Date: 10/27/2021
Citation: Taylor, S.D., Browning, D.M., Baca, R.A., Gao, F.N. 2021. Constraints and opportunities for detecting land surface phenology in drylands. Journal of Remote Sensing. 2:1-15. https://doi.org/10.1101/2021.05.21.445173.
Interpretive Summary: Knowing when plant species green-up, produce flowers, and lose their leaves are very important for growing crops, raising livestock, supporting pollinators and wildlife migrations. Tracking these phenomena over large areas requires using satellite imagery. In much of the world where water is scarce, it is difficult to reliably determine these patterns (known as 'land surface phenology') using imagery from satellites. We use a combination of time series images from an unmanned aerial vehicle (UAV) and simulation models to examine the importance of factors that influence the ability to track land surface phenology. We found the amount of plants when looking from above, or the percent cover, relative to surrounding soil is the most important thing determining detectability. Next was the type of plant, where evergreen plants, which retain green leaves all year-round, are harder than deciduous plants. Finally the precision of satellite measurements plays a small but important role, especially when there are equal amounts of plant and soil cover. Our findings help land managers, farmers, and livestock producers identify situations or places where satellite imagery is a good option for tracking phenology on the ground as a means to efficiently monitor and track patterns in productivity.
Technical Abstract: 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. 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% 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.