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Reliable phenological observations are needed to quantify the impact of climate change on tree phenology. Ground observations remain a prime source of phenological data, but their accuracy and precision have not been systematically quantified. The high subjectivity of ground phenological observations affects their accuracy, and the high within‐population variability of tree phenology affects their precision. The magnitude of those effects is unknown to date.We first explored the inter‐observer variability in the timing of bud development and leaf senescence in trees using a unique dataset of seven observer inter‐calibration sessions. Then, using tree phenological data collected in three European forests (n = 2,346 observations for budburst, n = 539 for leaf senescence), we quantified how the 'observer uncertainty' (accuracy of the observations) and the 'population sampling uncertainty' (precision of the observations) combine to affect the estimates of the budburst and the leaf senescence dates.The median observer uncertainty was 8 days for budburst (BBCH = 7) and 15 days for leaf senescence (BBCH = 95). As expected, the population sampling uncertainty decreased with increasing sample size, and was about 6 days for budburst and 10 days for leaf senescence for a sample of 10 individuals monitored per population (corresponding to the median sample size in the phenological literature). As a whole, the overall uncertainty of phenological observations could reach up to 2 weeks for budburst and 1 month for leaf senescence.Synthesis. This paper quantifies for the first time the accuracy and precision of ground phenological observations in forest trees and as such offers tables to estimate the uncertainty of phenological data. We show that reliable estimates of budburst and leaf senescence require three times (n = 30) to two times (n = 20) larger sample sizes as compared to sample sizes usually considered in phenological studies. We further call for an increased effort of observer inter‐calibration, required to increase the accuracy of phenological observations. These recommendations reduce the uncertainty of phenological data, thereby improving the estimation of phenological trends over time, the response of phenology to temperature or the inference of phenological model parameters. [ABSTRACT FROM AUTHOR]

Assessing photosynthesis rates at the ecosystem scale and over large regions is important for tracking the global carbon cycle and remote sensing has provided new and useful approaches for performing this assessment. The photochemical reflectance index (PRI) is a good estimator of short-term light-use efficiency (LUE) at the leaf scale; however, confounding factors appear at larger temporal and spatial scales. In this study, canopy-scale PRI variability was investigated for three species ( Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L.) growing under contrasting soil moisture conditions. Throughout the growing season, no significant differences in chlorophyll content and in violaxanthin, antheraxanthin and zeaxanthin were found between species or treatments. The daily PRI vs PAR (photosynthetically active radiation) relationships were determined using continuous measurements obtained at high frequency throughout the entire growing season, from early spring budburst to later autumn senescence, and were used to deconvolute the physiological PRI variability related to LUE variations due to phenological variability and related to temporal changes in the biochemical and structural canopy attributes. The PRI vs PAR relationship is used to show that the canopy-scale PRI measured at low radiation depends on the chlorophyll content of the canopy. The range of PRI variations at an intra-daily scale and the dynamics of the xanthophyll pool do not vary between days, which suggests that the PRI responds to a xanthophyll ratio. The PAR values at PRI saturation are mainly related to the canopy chlorophyll content during budburst and senescence and to the soil moisture content when the chlorophyll content is no longer a limiting factor. This parameter is significantly lower in the oak species that experience less stress from variations in soil moisture and is species dependant. These results provide new insights regarding the analysis and the meaning of PRI variability as a proxy for LUE at the canopy scale. [ABSTRACT FROM AUTHOR]

Abstract: Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days. [Copyright &y& Elsevier]