Julian dates and introduced temporal error in remote sensing vegetation phenology studies

Abstract

Remote‐sensing‐based vegetation phenology studies are commonly used to study agriculture, forestry, species distributions, and the effect of climate change on vegetation. These studies utilize annual time series of NDVI data to characterize seasonal growth patterns. The NDVI data for most of these studies have been pre‐processed using a maximum value compositing process to minimize contamination from clouds. A side effect of this process is a degradation of temporal data, since NDVI values are assigned to multiday periods rather than the specific date of image capture. In this study, the compositing process is examined to determine if there is a reliable pattern to pixel selection. Also, the magnitude of the introduced error is estimated by comparing vegetation phenology metrics calculated using the temporally degraded data and metrics calculated using the actual date of each pixel. The root mean square errors between these datasets ranged from 9.4 to 10.9 days, much larger than is acceptable for most phenology studies. We conclude that vegetation phenology studies must make use of accurate temporal data to characterize changes in vegetation seasonality.