Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem
AbstractNear surface (i.e., camera) and satellite remote sensing metrics 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. 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 two widespread species in southwestern U.S. ecosystems with those metrics estimated from near-surface cameras and MODIS NDVI for five years (2012–2016). Using Timesat software to estimate SOS and EOS from the phenocam green chromatic coordinate (GCC) greenness index resulted in good agreement with ground observations for honey mesquite but not black grama. Despite differences in the detectability of SOS and EOS for the two species, 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. View Full-Text
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Browning, D.M.; Karl, J.W.; Morin, D.; Richardson, A.D.; Tweedie, C.E. Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem. Remote Sens. 2017, 9, 1071.
Browning DM, Karl JW, Morin D, Richardson AD, Tweedie CE. Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem. Remote Sensing. 2017; 9(10):1071.Chicago/Turabian Style
Browning, Dawn M.; Karl, Jason W.; Morin, David; Richardson, Andrew D.; Tweedie, Craig E. 2017. "Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem." Remote Sens. 9, no. 10: 1071.
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