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Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Journal of Remote Sensing 2021年第1期2021卷 332-346页

作者： Shawn D.Taylor Dawn M.Browning ruben a.baca Feng Gao US Department of Agriculture Agricultural Research ServiceJornada Experimental RangeNew Mexico State UniversityLas CrucesNew Mexico 88003USA Oak Ridge Institute for Science and Education(ORISE) Oak RidgeTennessee 37830USA US Department of Agriculture Agricultural Research ServiceHydrology and Remote Sensing LaboratoryBeltsvilleMaryland 20705USA

Land surface phenology(LSP)enables global-scale tracking of ecosystem processes,but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices(VIs).Due to the importance of drylands for biodiversity,food security,and the carbon cycle,it is necessary to understand the limitations in measuring dryland ***,using simulated data and multitemporal unmanned aerial vehicle(UAV)imagery of a desert shrubland,we explore the feasibility of detecting LSP with respect to fractional vegetation cover,plant functional types,VI uncertainty,and two different detection *** simulated data,we found that plants with distinct VI signals,such as deciduous shrubs,can require up to 60%fractional cover to consistently detect *** plants,with lower seasonal VI amplitude,require considerably higher cover and can have undetectable phenology even with 100%vegetation *** evaluation of two algorithms showed that neither performed the best in all *** with adequate cover,biases in phenological metrics can still exceed 20 days and can never be 100%accurate due to VI uncertainty from shadows,sensor view angle,and atmospheric *** showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI *** high-resolution imagery,the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants,enabling the tracking of phenology at the individual *** thus have the potential to become an exemplary environment for future LSP research.

关键词： fractional straight imagery

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