A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Abstract

Abstract. Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.