Xiaoqiu Chen

Temperature and snowfall trigger alpine vegetation green-up on the world's roof

Rapid temperature increase and its impacts on alpine ecosystems in the Qinghai-Tibetan Plateau, the worlds highest and largest plateau, are a matter of global concern. Satellite observations have revealed distinctly different trend changes and contradicting temperature responses of vegetation green-up dates, leading to broad debate about the Plateaus spring phenology and its climatic attribution. Large uncertainties in remote-sensing estimates of phenology significantly limit efforts to predict the impacts of climate change on vegetation growth and carbon balance in the Qinghai-Tibetan Plateau, which are further exacerbated by a lack of detailed ground observation calibration. Here, we revealed the spatiotemporal variations and climate drivers of ground-based herbaceous plant green-up dates using 72 green-up datasets for 22 herbaceous plant species at 23 phenological stations, and corresponding daily mean air temperature and daily precipitation data from 19 climate stations across eastern and southern parts of the Qinghai-Tibetan Plateau from 1981 to 2011. Results show that neither the continuously advancing trend from 1982 to 2011, nor a turning point in the mid to late 1990s as reported by remote-sensing studies can be verified by most of the green-up time series, and no robust evidence for a warmer winter-induced later green-up dates can be detected. Thus, chilling requirements may not be an important driver influencing green-up responses to spring warming. Moreover, temperature-only control of green-up dates appears mainly at stations with relatively scarce preseason snowfall and lower elevation, while coupled temperature and precipitation controls of green-up dates occur mostly at stations with relatively abundant preseason snowfall and higher elevation. The diversified interactions between snowfall and temperature during late winter to early spring likely determine the spatiotemporal variations of green-up dates. Therefore, prediction of vegetation growth and carbon balance responses to global climate change on the worlds roof should integrate both temperature and snowfall variations.

Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China

Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding.

Comparison of spatial patterns of satellite-derived and ground-based phenology for the deciduous broadleaf forest of China

Using spatial phenology model-based Ulmus pumila leaf unfolding and leaf fall data at 8 km × 8 km grids, we analysed spatial relationships between ground-based and satellite-derived growing season beginning and end dates during the period 2001–2005 and examined climatic controls on spatial correlations between ground-based and satellite-derived growing seasons. The results show that the regional mean satellite-derived growing season clearly started earlier and terminated slightly later than the regional mean ground-based growing season. Meanwhile, spatial patterns of satellite-derived growing season beginning and end dates correlate positively with spatial patterns of ground-based growing season beginning and end dates in each year (p < 0.001). Interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season beginning date and February–April temperature controls interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season beginning date. In contrast, interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season end date is not associated with interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season end date and September–November temperature.

Spatial modeling of the Ulmus pumila growing season in China’s temperate zone

To reveal the ecological mechanism of spatial patterns of plant phenology and spatial sensitivity of plant phenology responses to climate change, we used Ulmus pumila leaf unfolding and leaf fall data at 46 stations of China’s temperate zone during the period 1986–2005 to simulate 20-year mean and yearly spatial patterns of the beginning and end dates of the Ulmus pumila growing season by establishing air temperature-based spatial phenology models, and validate these models by extensive spatial extrapolation. Results show that the spatial patterns of 20-year mean and yearly February-April or September-November temperatures control the spatial patterns of 20-year mean and yearly beginning or end dates of the growing season. Spatial series of mean beginning dates shows a significantly negative correlation with spatial series of mean February-April temperatures at the 46 stations. The mean spring spatial phenology model explained 90% of beginning date variance (p<0.001) with a Root Mean Square Error (RMSE) of 4.7 days. In contrast, spatial series of mean end dates displays a significantly positive correlation with spatial series of mean September-November temperatures at the 46 stations. The mean autumn spatial phenology model explained 79% of end date variance (p<0.001) with a RMSE of 6 days. Similarly, spatial series of yearly beginning dates correlates negatively with spatial series of yearly February-April temperatures and the explained variances of yearly spring spatial phenology models to beginning date are between 72%–87% (p<0.001), whereas spatial series of yearly end dates correlates positively with spatial series of yearly September-November temperatures and the explained variances of yearly autumn spatial phenology models to end date are between 48%–76% (p<0.001). The overall RMSEs of yearly models in simulating beginning and end dates at all modeling stations are 7.3 days and 9 days, respectively. The spatial prediction accuracies of growing season’s beginning and end dates based on both 20-year mean and yearly models are close to the spatial simulation accuracies of these models, indicating that the models have a strong spatial extrapolation capability. Further analysis displays that the negative spatial response rate of growing season’s beginning date to air temperature was larger in warmer years with higher regional mean February-April temperatures than in colder years with lower regional mean February-April temperatures. This finding implies that climate warming in winter and spring may enhance sensitivity of the spatial response of growing season’s beginning date to air temperature.

Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland

Plant phenology is a key link for controlling interactions between climate change and biogeochemical cycles. Satellite-derived normalized difference vegetation index (NDVI) has been extensively used to detect plant phenology at regional scales. Here, we introduced a new vegetation index, plant senescence reflectance index (PSRI), and determined PSRI-derived start (SOS) and end (EOS) dates of the growing season using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2011 in the Inner Mongolian Grassland. Then, we validated the reliability of PSRI-derived SOS and EOS dates using NDVI-derived SOS and EOS dates. Moreover, we conducted temporal and spatial correlation analyses between PSRI-derived SOS/EOS date and climatic factors and revealed spatiotemporal patterns of PSRI-derived SOS and EOS dates across the entire research region at pixel scales. Results show that PSRI has similar performance with NDVI in extracting SOS and EOS dates in the Inner Mongolian Grassland. Precipitation regime is the key climate driver of interannual variation of grassland phenology, while temperature and precipitation regimes are the crucial controlling factors of spatial differentiation of grassland phenology. Thus, PSRI-derived vegetation phenology can effectively reflect land surface vegetation dynamics and its response to climate change. Moreover, a significant linear trend of PSRI-derived SOS and EOS dates was detected only at small portions of pixels, which is consistent with that of greenup and brownoff dates of herbaceous plant species in the Inner Mongolian Grassland. Overall, PSRI is a useful and robust metric in addition to NDVI for monitoring land surface grassland phenology.

Assessing Phenology at the Biome Level

Seasonal biome dynamics are controlled by recurrent variations of environmental conditions, especially climate. Therefore, in temperate areas with distinct climatic rhythmicity, seasonal biome features are rich and colorful. The most obvious natural stages during a year are growing season and rest period. Further, several characteristic sub-stages can be identified within growing season and rest period through careful observation. Because plant phenological events are integrative indicators of seasonal changes, using occurrence dates of phenophases to determine seasons (especially the growing season) is an effective way to reveal the overall characteristics of nature’s seasonality. This information is useful for doing agricultural tasks in the right season, making seasonal landscape designs, preventing plant diseases and insect pests, directing sightseeing (Yang and Chen 1980), and validating remote sensing phenology (Chen et al. 2000).

Daily Temperature-Based Temporal and Spatial Modeling of Tree Phenology

Using Ulmus pumila leaf unfolding and leaf fall data at 46 stations during the 1986–2005 period in China’s temperate zone, daily temperature-based temporal and spatial phenology models were constructed. The daily temperature-based temporal phenology model provides a more precise and rational tool than the monthly or multi-monthly mean temperature-based phenology model in detecting responses of tree phenology to temperature. For the entire China’s temperate zone, a 1 °C increase in spring and autumn daily temperatures during the optimum length periods may induce an advancement of 2.8 days in the beginning date and a delay of 2.1 days in the end date of the Ulmus pumila growing season, respectively. Meanwhile, the daily temperature-based spatial phenology model provides a more robust tool than the geo-location based spatial phenology model in simulating and predicting spatial patterns of tree phenology. Regarding 20-year mean growing season modeling, a spatial shift in mean spring and autumn daily temperatures by 1 °C may cause a spatial shift in mean beginning and end dates of the Ulmus pumila growing season by −3.1 and 2.6 days, respectively.

Antagonistic effects of growing season and autumn temperatures on the timing of leaf coloration in winter deciduous trees

Autumn phenology remains a relatively neglected aspect in climate change research, which hinders an accurate assessment of the global carbon cycle and its sensitivity to climate change. Leaf coloration, a key indicator of the growing season end, is thought to be triggered mainly by high or low temperature and drought. However, how the control of leaf coloration is split between temperature and drought is not known for many species. Moreover, whether growing season and autumn temperatures interact in influencing the timing of leaf coloration is not clear. Here, we revealed major climate drivers of leaf coloration dates and their interactions using 154 phenological datasets for four winter deciduous tree species at 89 stations, and the corresponding daily mean/minimum air temperature and precipitation data across Chinas temperate zone from 1981 to 2012. Results show that temperature is more decisive than drought in causing leaf coloration, and the growing season mean temperature plays a more important role than the autumn mean minimum temperature. Higher growing season temperature and lower autumn minimum temperature would induce earlier leaf coloration date. Moreover, the mean temperature over the growing season correlates positively with the autumn minimum temperature. This implies that growing season mean temperature may offset the requirement of autumn minimum temperature in triggering leaf coloration. Our findings deepen the understanding of leaf coloration mechanisms in winter deciduous trees and suggest that leaf life-span control depended on growing season mean temperature and autumn low temperature control and their interaction are major environmental cues. In the context of climate change, whether leaf coloration date advances or is delayed may depend on intensity of the offset effect of growing season temperature on autumn low temperature.