Changes in vegetation spring dates in the second half of the twentieth century
International Journal of Remote Sensing
This study aims at estimating trends in spring phenology from vegetation index and air temperature at 2 m height. To this end, we have developed a methodology to infer spring phenological dates from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) time-series, which are then extrapolated to the period 1948-2006 with the help of Reanalysis data, using its 2 m height air temperature parameter. First, yearly NDVI is fitted to a double-logistic
... double-logistic function for the whole extent of the GIMMS database . This fitting procedure allows us to describe, on a yearly basis, the NDVI evolution for each pixel through the estimation of six parameters which include the spring date. Retrieved spring date time-series are then upscaled to Reanalysis database resolution and compared to degree-day amounts. Those degree-day amounts are estimated for various thresholds in order to determine the best thresholds for their calculations on a pixel-by-pixel basis. Once the correct thresholds are identified by correlation with corresponding GIMMS spring date time-series, spring dates are estimated for the whole extent of the Reanalysis database . Finally, Mann-Kendall trend tests are conducted on degree-day-retrieved spring date time-series and trends are estimated only for those pixels that show statistically significant trends. These trends in spring occurrence have an average value of -0.03 days per year, but range between -0.9 and +0.9 days per year, depending on the considered areas. Since the approach is based only on air temperature, retrieved spring dates for vegetation whose growth is limited by water are unreliable, as correlation analysis confirms. The obtained spring date trends show good coherence with previous studies and could be used for climate change impact studies, especially in polar and temperate areas, where the model is more reliable.