**1. Introduction**

Vegetation plays an important role in ecosystem carbon and hydrological cycles [1,2] and is a sensitive indicator of ecosystem response to climate change. Vegetation phenology, which is the periodic life activity of plants, provides an independent measure of how

**Citation:** Cui, X.; Xu, G.; He, X.; Luo, D. Influences of Seasonal Soil Moisture and Temperature on Vegetation Phenology in the Qilian Mountains. *Remote Sens.* **2022**, *14*, 3645. https://doi.org/10.3390/ rs14153645

Academic Editor: Zhuosen Wang

Received: 2 June 2022 Accepted: 26 July 2022 Published: 29 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ecosystems respond to climate change [3] and varies significantly according to climate zone and vegetation type, especially in temperate and northern regions [4,5]. Affected by climate change and human activity, the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS) show substantial interannual variability [6]. Phenological changes result in small-area changes in plant activity within the community and large-area changes in overall land surface processes, such as the carbon budget, surface energy flux, and regional climate [7]. Therefore, understanding phenological variation and its response to climate change is critical for improving terrestrial biosphere models and climate models [8].

Current research methodologies on vegetation phenology mainly include the traditional ground observation method and satellite remote sensing monitoring method. Traditional ground observations can provide detailed specific plant phenology information at the species-scale or individual plant scale but have limitations in terms of observational stations and spatial coverage [9]. In addition, most ground observation sites only focus on cultivated plants rather than natural vegetation [10]. Remote sensing data from satellites can provide long time series and a high temporal resolution vegetation index (VI) and have been widely applied in large-scale vegetation phenology monitoring [11]. The satellite remote sensing monitoring method primarily uses time series VIs, and the normalized difference vegetation index (NDVI) is the most commonly used VI [12]. The NDVI is simple to calculate and sensitive to plant growth and can track seasonal dynamic changes in vegetation [13].

Climate changes can be directly reflected in vegetation phenology [6]. Temperature can be considered the most important factor affecting vegetation phenology in many regions. Numerous studies have found that advances in spring phenology at middle and high latitudes are primarily controlled by increased global surface mean temperature [14–17]. Additionally, precipitation is a key factor in regulating vegetation phenology, particularly in water-limited arid and semiarid regions [18]. For example, Ren et al. [19] showed that the influence of precipitation on the interannual variation in the SOS and EOS is more important than that of temperature in the Inner Mongolian Autonomous Region. Compared with precipitation, soil moisture is the most direct water supply for vegetation and is susceptible to drought, which can affect vegetation phenology [20]. Some observational studies have suggested that soil water availability is also an important factor that can trigger vegetation growth in water-limited areas [21,22]. An understanding of the impact of soil moisture dynamics on vegetation phenology is very important and can increase our understanding of the influence of climate change on ecosystems. However, there are insufficient studies related to this topic.

The Qilian Mountains (QLMs) are located in the arid/semiarid region of northwestern China, which is a transitional zone between the Qinghai–Tibet Plateau (QTP), Loess Plateau, and Inner Mongolia Plateau. The QLMs have a vulnerable ecosystem and complex climate, and the hydrothermal conditions differ from east to west [23]. In recent years, the QLMs have experienced significant climate changes, which involve a significant trend of warming and wetting, frequent climate anomalies [24], and local vegetation becoming sensitive to climate changes [25]. In addition, the large east–west span and spatial heterogeneity among the vegetation types in the QLMs lead to enormous differences in the response relationship between the vegetation phenology of different vegetation types and climatic factors. Soil moisture plays an essential role in maintaining vegetation growth, especially in arid and semi-arid regions [20]. Soil moisture in the QLMs increases with an increase in altitude and is heterogeneous between different types of land cover [26]. Due to the complex topography and climatic conditions, the ecosystems in the QLMs are fragile and sensitive to climate change, and thus it is necessary to systematically explore the effects of temperature, precipitation, and soil moisture on vegetation phenology.

Based on moderate resolution imaging spectroradiometer (MODIS) NDVI time series products from 2001 to 2020, this study extracted the SOS, EOS, and LOS for the QLMs' vegetation and analyzed the characteristics of the changes in vegetation phenology and

the response relationship between vegetation phenology and driving factors, including temperature, precipitation, and soil moisture. The main objectives of this study were to (1) investigate the characteristics of the spatiotemporal patterns of vegetation phenology in the QLMs during the period 2001–2020, (2) evaluate the effects of seasonal temperature, precipitation, and soil moisture on the SOS and the EOS in the study area, and (3) explore the relationship between the phenology of different elevation zones, vegetation type, and climatic factors in the QLMs. This study can contribute to our understanding of the mechanism of the effects of climate change on vegetation phenology in arid mountain areas, and the findings enable the prediction of the future evolution of ecosystems and the implementation of effective ecosystem management.

#### **2. Data and Methods**
