Winter Wheat Green-up Date Variation and its Diverse Response on the Hydrothermal Conditions over the North China Plain, Using MODIS Time-Series Data

Characterizing Spatial Patterns of the Response Rate of Vegetation Green-Up Dates to Land Surface Temperature in Beijing, China (2001–2019)

The phenology indicator of vegetation green-up dates (GUD) is prone to being affected by changes in temperature. However, the influencing degree of urbanization-induced temperature warming on vegetation GUDs among different vegetation species along the urban-rural gradient remains inadequately described. In this study, based on the long-term (2001–2019) satellite-derived vegetation GUDs and nighttime land surface temperature (LST) of forests, grasslands, and croplands along the urban-rural gradient with Beijing (China) as a case study area, the responses of vegetation GUDs to temperature changes were quantitatively analyzed, taking into account the vegetation types and distances away from the urban domain. The results show that (1) long-term GUDs and LST are significantly negatively correlated, characterized by a weaker significant correlation near the urban area when compared with its surrounding areas, with the greatest absolute linear correlation coefficients (r) happening at rings 32 km (rmax = −0.93, forests), 20 km and 48 km (rmax = −0.83, grasslands), and 34 km (rmax = −0.82, croplands), respectively; (2) the magnitude of change in GUDs over the past 19 year (2001–2019) are significantly positively correlated with these in LST near the urban area, demonstrating a distance-decay trend, with the greatest advance in GUDs occurring at the ring nearest the urban area, by about 20 days (forests), 24.5 days (grasslands), and 15.6 days (croplands), respectively; (3) the spatial pattern of the response rate of GUDs change to LST change (days K−1) also showed a declining trend with distance, with GUD advanced by 6.8 days K−1 (forests), 7.5 days K−1 (grasslands), and 4.9 days K−1 (croplands) at the closest ring to the urban, decreasing to about 2.3 days K−1 (48 km), 4.1 days K−1 (18 km), and 1 day K−1 (18 km), respectively, indicating a notable influence of temperature warming on vegetation GUDs near the urban domains.

Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China

Due to the complex coupling between phenology and climatic factors, the influence mechanism of climate, especially preseason temperature and preseason precipitation, on vegetation phenology is still unclear. In the present study, we explored the long-term trends of phenological parameters of different vegetation types in China north of 30°N from 1982 to 2014 and their comprehensive responses to preseason temperature and precipitation. Simultaneously, annual double-season phenological stages were considered. Results show that the satellite-based phenological data were corresponding with the ground-based phenological data. Our analyses confirmed that the preseason temperature has a strong controlling effect on vegetation phenology. The start date of the growing season (SOS) had a significant advanced trend for 13.5% of the study area, and the end date of the growing season (EOS) showed a significant delayed trend for 23.1% of the study area. The impact of preseason precipitation on EOS was overall stronger than that on SOS, and different vegetation types had different responses. Compared with other vegetation types, SOS and EOS of crops were greatly affected by human activities while the preseason precipitation had less impact. This study will help us to make a scientific decision to tackle global climate change and regulate ecological engineering.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing-Tianjin-Hebei region during 2014-2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m-3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Impact of spring phenology variation on GPP and its lag feedback for winter wheat over the North China Plain

Spring green-up date (GUD) is a sensitive indicator of climate change, and of great significance to winter wheat production. However, our knowledge of the chain relationships among them is relatively weak. In this study, based on 8-day Enhanced Vegetation Index (EVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2015, we first assessed the performance of four algorithms for extracting winter wheat GUD in the North China Plain (NCP). A multiple linear regression model was then established to quantitatively determine the contributions of the time lag effects of hydrothermal variation on GUD. We further investigated the interactions between GUD and gross primary production (GPP) comprehensively. Our results showed that the rate of change in curvature algorithm (RCCmax) had better performance in capturing the spatiotemporal variation of winter wheat GUD relative to the other three methods (Kmax, CRmax, and cumCRmax). Regarding the non-identical lag time effects of hydrothermal factors, hydrothermal variations could explain winter wheat GUD variations for 82.05% of all pixels, 36.78% higher than that without considering the time lag effects. Variation in GUD negatively correlated with winter wheat GPP after green up in most parts of the NCP, significantly in 35.75% of all pixels with a mean rate of 1.89 g C m^-2 yr^-1 day^-1. Meanwhile, winter wheat GPP exerted a strongly positive feedback on GUD in >82.42% of all pixels (significant in 28.01% of all pixels), characterized by a humped-shape pattern along the long-term average plant productivity. This finding highlights the complex interaction between spring phenology and plant productivity, and also suggests the importance of preseason climate factors on spring phenology.

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies.