Research on phenology focuses on the phases of recurring biological life cycles, how these cycles are influenced by biotic and abiotic drivers and the interrelations among the same or different species [1
]. Forest phenology is one of the most reliable biological indicators for evaluating the impacts of global warming in temperate and boreal zones because of its high sensitivity to climate change [2
]. In turn, variations in forest phenology strongly affect the fitness and distribution of temperate and boreal forests [3
] and, thus, the interspecific interactions among plants [4
], ecosystem structure and function by varying the length of growing season [5
], terrestrial ecosystem water and carbon balances [7
], and climate system energy balances [9
]. However, the processes underlying forest phenology are still unclear and need further research as they are essential to enhance the understanding of forest ecosystems and their responses to climate change.
Global warming has become an undeniable fact in recent years, changing the timings of plant flowering, leaf-out, growth stages, and leaf senescence over the past few decades [10
]. Extensive research primarily focused on the start of the growing season (SOS) and found an earlier SOS in high latitudes and high altitudes based on the ground observations, satellite remote sensing, and model simulations [11
]. However, the SOS trend diverges significantly among previous studies [18
]. Due to the complexity and perplexity of vegetation phenological processes during autumn, studies on the processes of the end of the growing season (EOS) and its associated control factors have not received sufficient attention [21
]. As the final phase of the plants’ seasonal cycle, EOS serves as an important occasion to recycle nutrients from senescing leaves to other plant tissues to sustain their growth in the following spring [23
]. The EOS was recognized to be as important as the SOS in influencing the interannual variability of the carbon balance by controlling the length of the photosynthetically active period [10
]. Therefore, combining the SOS, EOS, and length of the growing season (LOS) for vegetation and its associated environmental factors will improve our understanding of the response of vegetation to climate change.
Temperature is a primary climatic factor regulating the SOS and EOS in temperate and boreal forests. Recent studies have indicated that spring warming directly regulates the SOS, and the winter chilling requirement for dormancy release indirectly regulates the SOS [13
]. Warming during summer and autumn can significantly delay the EOS [27
], but the response of EOS to warming differed among species [28
]. The warming trends also affect the time of phenology; for example, many studies show that phenology trends are significantly faster pre-1990 than post-2000 [19
]. The EOS has a different temperature sensitivity compared with SOS; for example, the EOS response to temperature is larger than that of SOS [27
]. Precipitation has also been found to be a primary driving factor affecting the SOS and EOS in a complex manner in temperate and boreal forests [11
]. For example, Fu et al. [12
] found that winter precipitation has a positive correlation with the heat requirement for SOS at northern middle and high latitudes. Precipitation also has a considerable impact on the EOS, particularly for arid and semi-arid regions [31
]. Therefore, understanding the response of forest phenology to climate change is valuable. However, the processes underlying the forest phenology, which may differ among regions and ecosystems due to their associated climatic factors and the complex response of spring and autumn phenology to climate change in the mountains, are still not fully understood.
Mountain forests typically cover a concentrated and abundant vertical band spectrum of biological climates, providing important ecological service functions in maintaining biological diversity, regulating regional climate, and conserving water [35
]. The elevational gradients can be treated as space-for-time substitutions to evaluate the influences of climate change on forest ecosystems. The environmental gradient of mountainous systems is unique, making it a sensitive indicator of climate change, and has become a focus for current climate change research. Meanwhile, warming trends may vary across the bioclimatic gradient, with stronger trends at higher latitudes and altitudes [36
]. Therefore, biomes at high elevations are experiencing a more rapid change in temperature than those at low elevations [36
]. Recent studies have shown trends of earlier SOS and delayed EOS in many mountain zones, including the Greater Khingan Mountains [38
], the Changbai Mountains [39
], the Andes Mountains [40
], and semi-arid mountain regions [41
], indicating that rising temperature could influence the phenological metrics. However, there is a lack of answers to whether the phenological advancement along different elevation gradients is uniform or diverse and how is the dynamics of uniform or diverse.
Satellite-based information has been used for monitoring the spatial heterogeneity of land surface phenology over recent decades and for discerning the spatiotemporal patterns of land surface phenology responding to the effects of climatic change [13
]. Recent studies have effectively described the vegetation phenology using fine-resolution (<30 m), which can be achieved from fusing spare fine-and frequency medium-resolution images [43
], from multiple years into a single synthetic year [44
], and from optical HJ-1 satellites [45
] and Sentinel-2 [46
]. Although finer-resolution sensors are more effective for capturing the heterogeneous land cover, such sensors usually need longer revisit times with too few cloud-free images to effectively capture the seasonal changes in greenness [47
]. According to the spatiotemporal resolutions and the length of time series, three satellite sensors, namely, Moderate Resolution Imaging Spectroradiometer (MODIS), Système Pour I’Observation de la Terre (SPOT), and Advanced Very High Resolution Radiometer (AVHRR), have often been used to monitor vegetation phenology [16
]. Vegetation indices (e.g., EVI, Enhanced Vegetation Index; NDVI, Normalized Difference Vegetation Index) based on the spectral reflectance of vegetation are well correlated with chlorophyll abundance, photosynthetically active biomass, and energy absorption [48
]. The EVI, which could avoid the problem of NDVI saturation in high vegetation coverage areas and reduce the effects of atmospheric and soil background, has proven to be a robust indicator of vegetation growth [51
]. Therefore, a MODIS EVI dataset with a 500 m spatial resolution and an 8-day temporal resolution was used in this study for retrieving the forest phenology in the Qinling Mountains (QMs), China, taking into account the heterogeneity of the mountains and NDVI saturation in areas of dense forest coverage.
The QMs are located in a typical and important ecological and geographical transition zone in China. The transitional nature of the physical conditions, which is sensitive to external interference, provides an ideal region for examining the climatic and altitudinal effects on forest phenology metrics that is, hence, ideal for understanding the response of the regional forest ecosystems to ongoing global warming. We hypothesize that (1) the recent climate change has altered the forest growing season in the QMs, but the changes in SOS, EOS, and LOS along different elevation gradients are uneven; (2) temperature and precipitation are the key driving factors for SOS and EOS in the QMs. In this study, we investigated the relationships between topography, climate factors, and forest phenological metrics. In particular, we aim to (i) quantify the spatial pattern and interannual variability of the forest SOS, EOS, and LOS in the QMs from 2001 to 2017; (ii) evaluate the influence of elevation gradients on SOS, EOS, and LOS on a spatiotemporal scale; and (iii) explore the relationships between climatic factors (e.g., temperature, precipitation) and the spatiotemporal variations of forest phenology metrics.