Phenology of plant communities is a multi-scale phenomenon that reflects processes acting at multiple scales - leaves, individual canopies, and the larger plant community. Remotely sensed metrics of photosynthetic activity derived from near surface (i.e., camera), drone and satellite remote sensing have become widely used indicators of plant growing seasons. While robust linkages have been established between field metrics and ecosystem exchange in many land cover types, assessment of how well remotely-derived season start and end dates depict field conditions in arid ecosystems remain unknown. Specifically, in modest to sparsely vegetation landscapes in the arid and semiarid regions, the contribution of bare plant interspaces is expected to influence the utility these remotely sensed metrics of vegetation greenness. We evaluated the correspondence between field measures of start (SOS; leaves unfolded and canopy greenness >0) and end of season (EOS) and canopy greenness for common C4 grass and C3 shrub species in southwestern U.S. ecosystems with those metrics estimated from near-surface cameras, time series drone imagery, and MODIS NDVI for five years (2014–2018).
Results/Conclusions
We found that phenocam greenness (GCC) was well correlated with field measures with higher Spearman rank correlations for more conspicuous C3 shrubs (r = 0.760) than C4 grasses (r = 0.644). We also found that NDVI patterns from the drone imagery were very similar across all three C4 grass species and were distinctly different than patterns for the C3 shrub honey mesquite. Honey mesquite transitioned from dormant to peak green in between 14 to 16 days in mid-March to early April while the rapid increase in C4 grasses occurred later in the season following onset of the summer rains in early July. Phenocam GCC was significantly correlated with field estimates of canopy greenness for both species throughout the growing season. MODIS NDVI for this arid grassland site was driven by the black grama signal although a mesquite signal was discernable in average rainfall years. Our findings suggest phenocams could help meet myriad needs in natural resource management and drone imagery can facilitate broader scale interpretations of land surface phenology.