**1. Introduction**

As one of the most serious meteorological and environmental disasters, drought can severely impact the natural environment, crop production, social economy, and human life, but its impact mode is not easy to be quantified [1]. The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) points out that in the past 30 years, the global average temperature has increased by 1.5 ◦C, extreme climate events occur frequently, and the degree of drought will continue to increase in the future. Drought impacts species and structure of vegetation. It is an important factor affecting vegetation growth, vegetation restoration, and soil desertification [2–4]. Changes in hydrothermal conditions can lead to biomass loss and ecosystem destruction. Therefore, investigating the spatiotemporal variation of drought during the growing season (from April to September) in Inner Mongolia, identifying causes of drought, and separating and quantifying relative contributions of the controlling factors of drought are of practical significance for drought remediation and ecosystem restoration.

Due to uncertainties in starting and ending times, spatial scale, time lag effects, and other factors of drought events, researchers mainly monitor and analyze drought effects through a series of drought indicators [1,5]. The Palmer Drought Severity Index (PDSI)

**Citation:** Ji, B.; Qin, Y.; Zhang, T.; Zhou, X.; Yi, G.; Zhang, M.; Li, M. Analyzing Driving Factors of Drought in Growing Season in the Inner Mongolia Based on Geodetector and GWR Models. *Remote Sens.* **2022**, *14*, 6007. https:// doi.org/10.3390/rs14236007

Academic Editor: Giorgio Baiamonte

Received: 15 October 2022 Accepted: 23 November 2022 Published: 27 November 2022

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is the most widely used water-balance-based meteorological drought index, which comprehensively considers water supply and demand. However, it has limitations in judging short-term droughts [6]. The Standardized Precipitation Index (SPI) calculates the probability of precipitation distribution; however, it is difficult to handle the task of meteorological drought monitoring under the context of global change [7]. The Standardized Precipitation Evapotranspiration Index (SPEI) leverages the advantages of PDSI and SPI [8,9]. It not only considers the balance of water and energy, but also reflects the deficit and accumulation process of surface water. Therefore, it is widely used in climate studies [7,10], agriculture [11], hydrology [12,13], and so on. At the same time, SPEI can be calculated at multiple time scales. SPEI-3, used to characterize drought in a seasonal scale, reflects short-term regional meteorological drought. It has a direct correlation with grassland biomass and vegetation growth [14,15], and is an important index of vegetation to study drought in a growing season.

The China-Mongolia Arid and Semiarid Area (CMASA) is one of the eight major arid regions in the world, and it is also an inland arid region with the highest latitude. Inner Mongolia is located in the transition region between the arid and semi-arid areas in the east of CMASA. Due to the perennial influence of the westerly wind system, atmospheric circulation, and pressure field of Qinghai-Tibet Plateau, temperature rise in the west of Inner Mongolia is significantly higher than that in the inland and surrounding areas of China, and is particularly sensitive to climate change [16,17]. Inner Mongolia is China's main grassland for pasture and agriculture. It is an important ecological barrier to the North of China. For a long time, Inner Mongolia has suffered from frequent regional and local droughts, which have significantly impacted the local economy. The intensification of desertification caused by droughts has become the primary ecological and environmental concern in Inner Mongolia [18]. There are many research activities on long-term drought monitoring in Inner Mongolia: An et al. [19] analyzed the spatiotemporal variation of droughts in Inner Mongolia in the past 60 years; Pei et al. [20] compared the differences and applicability of SPI and SPEI drought indexes at different time scales; Tong et al. [21] used linear regression and wavelet analysis to identify drought changes and drought patterns; however, few studies have quantitatively explained the causes of the droughts. In the past, drought analysis and regional water resource planning were mainly based on linear correlation between factors [22–24]. However, drought is a complex regionalization event. It is generally hard to refine intensity and interaction among various factors in different regions using just the traditional linear regression analysis [25,26]. At the same time, drought is closely related to natural conditions, human activities, and their interactions. However, interactions among these factors have not been well-investigated [27]. Different land cover types, soil conditions, topography, and other factors may cause spatial differentiation of local drought. Geodetector and GWR are statistical models considering spatial nonstationarity and the modeling process is simple but intuitive. A combination of the two can accurately describe the action, path, and intensity of the influencing factors and has a good application prospect [28–30].

In this study, the seasonal SPEI-3 index (SPEI for short) was calculated based on the data at the meteorological stations in Inner Mongolia; the spatiotemporal variation trend of SPEI in the growing season from 2000 to 2018 was obtained using univariate linear trend analysis. The main controlling factors of drought change were identified through Geodetector. The GWR model was used to quantitatively evaluate the effect of various driving factors on SPEI change during the growing season, and to explain the interaction between the main controlling factors for spatial heterogeneity.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Inner Mongolia is located in the northern border of China, spanning three major regions of northwest, north, and northeast, spreading along a long and narrow belt. The region covers a total area of about 1.183 million km<sup>2</sup> that accounts for about 12.1% of

China's total area. The region is rich in resources, with grasslands, forests, and arable land per capita ranking first in China. The Greater Khingan Range runs through the east of the study area in the north-south orientation. The Yin Mountains extend east-west in the south. Large deserts such as the Badain Jaran Desert, Tengger Desert, and Mu Us Desert are located in the west. The study area has an average altitude of about 1000 m. The climate in the region varies from arid-semiarid monsoon climate to humid-semi humid climate. It is often affected by cyclones on the Mongolian Plateau with strong wind in spring and by Lake Baikal, the world's largest freshwater lake by volume containing 22–23% of the world's fresh surface water. The climate is often controlled by prevailing westerlies belts or subtropical high-pressure belts, with high temperature and little rain in summer. Annual rainfall showed a decreasing trend from east to west and from north to south [22,31].

#### *2.2. Data Sources and Preprocessing*

#### 2.2.1. Meteorological Data

Daily mean air temperature, monthly cumulative precipitation, daily mean wind speed, and daily mean sunshine duration at 110 meteorological stations (Figure 1a) in and around Inner Mongolia from 2000 to 2018 were selected as meteorological data, which were provided by the China Meteorological Data Service Centre (http://data.cma.cn/). A homogeneity test of the meteorological data was carried out to fill in unavailable data. A statistical analysis on the mean air temperature (◦C), accumulated precipitation (mm), mean wind speed (m·s−1), and mean sunshine duration (h) at stations was performed for the growing seasons from 2001 to 2018. Spatial resolution for ordinary Kriging interpolation was set to 1 km × 1 km; the geographic reference was set as WGS84/UTM zone 48◦N.

**Figure 1.** (**a**) The Digital Elevation Model (DEM) and meteorological station distribution, and (**b**) the vegetation types in Inner Mongolia (A—Hulunbuir, B—Hinggan League, C—Xilingol League, D—Chifeng, E—Tongliao, F—Ulangab League, G—Baotou, H—Hohhot, I—Bayannur League, J—Erdos, K—Wuhai, L—Alxa League).

#### 2.2.2. DEM Data

DEM (Digital Elevation Model) was the Shuttle Radar Topography Mission (SRTM) data with a resolution of 90 m downloaded from the United States Geological Survey (USGS) data portal (http://earthexplorer.usgs.gov). After preprocessing, such as mosaicking and void-filling, the accuracy of the input topographic data had a standard error of 1 m. The DEM data were resampled to 1 km × 1 km and slope and aspect were then derived from the DEM data.

#### 2.2.3. Other Data Sets

Population density data was from the population dataset produced by the Landscan Global team (https://landscan.ornl.gov/), with a spatial resolution of 0.01◦ × 0.01◦ (about 1 km). The data were produced according to the community standard of global population distribution data constructed from multivariable geographic dasymetric modeling and remote sensing image analysis [32]. Land use conversion type can effectively reflect intensity of human activities [33]. It is also an important surface condition for drought research. The land cover data in 2000 and 2018 were from the Resource and Environment Science Data Center of Chinese Academy of Sciences (http://www.resdc.cn). The data set was obtained by visual interpretation of Landsat TM/ETM+ images for different periods and was widely used [25]. Land cover was classified into six basic categories: cultivated land, forest, grassland, water area, construction land, and unused land. A land use conversion map from 2000 to 2018 was generated. Soil sediment contents were from the 1:1 million soil type map and the soil profile data was obtained from the second soil survey (http://www.resdc.cn). Soil texture was classified according to the content of sand, silt, and clay. The content of particles in different soils was shown as a percentage. Spatial data of main rivers and county stations were derived from the 1:4 million vector database provided by the National Geographic Center of China (http://ngcc.sbsm.gov.cn). Distance to Rivers (DTR) and Distance to Cities (DTC) were obtained through buffer zone analysis, and the spatial resolution was set to 1 km × 1 km. The 1:1 million vegetation type data released by the Resource and Environment Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn) were used. The vegetation in Inner Mongolia was reclassified into 8 classes: coniferous forest, broad-leaved forest, shrub, grassland, meadow, swamp, desert, and cultivated vegetation.
