干旱区生态环境知识资源中心

To better understand how terrestrial vegetative ecosystems respond to climate and/or anthropogenic effects, the scientific community is increasingly interested in developing methods of employing satellite data to track changes in land surface phenology (e.g., timing and rate of green-up, amplitude and duration of growing season, and timing and rate of senescence of plant classes). By increasing the inherent resolution of signal extraction procedures while minimizing the effects of cloud cover and prolonged data gaps, such tools can significantly improve land cover classification and land cover change monitoring on multiple scales. This report describes an intuitive approach for tracking the intra-annual details and interannual variability of multiyear time series, employing a sequence of annual high-order polynomial splines (up to the 14th order), stabilized by minimizing model roughness and weighted to fit the upper data envelope to minimize cloud cover bias. The algorithm is tested using multiyear time series for three very different classes of vegetation-stable agriculture, high elevation montane shrubland, and semi-arid grassland with high interannual variability. The results accurately track both short- and long-term land surface phenology and illustrate a robust potential for extracting temporal and spatial detail from a variety of satellite-based multiyear vegetation signals.