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Analysis on Topographic Effect of Soil Moisture in Mountainous Areas based on Landsat-8 OLI Images

Ecological environmental worsening and water-soil loss occur frequently in mountainous areas in recent years. As an important index of land deterioration and degree of drought, soil moisture can influence vegetation growth, hydrological cycle and water resource distribution in mountainous areas significantly. However, topographic effect of soil moisture under complicated mountainous terrain conditions still remains unknown. In the present study, a quantitative inversion on surface soil moisture content in a typical mountainous area of Dengfeng city, Henan province (China) in 2013 and 2017 was carried out by using temperature vegetation dryness index method based on Landsat-8 OLI (Operational Land Imager) remote sensing images and digital elevation model (DEM). Results were conducive to analyze effects of terrain factors on spatial-temporal distribution of soil moisture and improve ecological environment in mountainous area. Besides, a quantitative analysis on topographic effect (e.g. elevation, slope and slope orientation) on soil moisture in mountainous area was performed based on DEM data. Results demonstrate that topographic factors (e.g. elevation, slope and slope orientation) can cause violent variations of soil moisture in mountainous areas. Soil moisture is positively correlated with elevation and slope. In view of slope orientations, shady slope generally presents the highest soil moisture, followed by semi-shady slope, sunny slope and semi-sunny slope successively. In 2017, the heat island effect in the study area is stronger and the arid area is significantly larger than those in 2013. Additionally, soil moisture in the region with elevation lower than 750 m and slope smaller than 25° is lower in relative to that in 2013, but it is higher in the region with elevation higher than 750 m and slope larger than 25°. This study discloses the influencing law of terrain factors on spatial-temporal distribution of soil moisture in mountainous areas. Research conclusions provide some scientific references to water conservation and ecological environmental protection in mountainous areas.


Introduction
As an important component of water cycle of terrestrial ecosystems, soil moisture is a comprehensive reflection of natural conditions, including climate, vegetation, terrain and soil factors. Moreover, soil moisture is an important index to measure land deterioration and degree of drought, especially in complicated mountainous areas. Due to topographic relief, surface moisture, energy and other key elements have uneven spatial distributions in mountainous areas, which further influences vegetation growth, water resource distribution and local microclimate [1][2][3][4]. Therefore, studying spatial-temporal distribution characteristics of soil moisture in mountainous areas has important scientific and practical significance to ecological environmental protection in mountainous area.
In existing soil moisture monitoring methods, optical remote sensing and microwave remote sensing are the most potential two quantitative monitoring methods of soil moisture [5][6][7][8]. With strong penetration through clouds, fogs and surface, microwave remote sensing can offer services 24h a day and under all weather conditions, and it enjoys promising application prospects in soil moisture monitoring. Khedri et al. [9] implemented inversion of surface soil moisture by using a support vector machine (SVM) regression model based on AIRSAR data of wavebands C, L and P in 2003. Based on SAR image data, Holtgrave et al. [10] calculated influences of compensation vegetation of normalized differential vegetation index (NDVI) on backscatter of SAR images by using Landsat-8 data. They have achieved good results from inversion of soil moisture in flooding regions based on SVM model. However, these studies have a high research cost due to the expensive radar images. Moreover, radar images are sensitive to terrain, surface roughness, vegetation coverage and topographic parameters. Radar parameters have complicated relations with the target characteristic parameters. These disadvantages restricted the wide application of radar image to some extent [11]. Optical remote sensing has certain penetrating power to soil layer and it can extract soil moisture information in superficial layers to provide references for drought monitoring, vegetation growth and improvement of ecological environment. The temperature vegetation dryness index ( ) is an extensively used method which has relatively mature theory on remote sensing monitoring of soil moisture based on visible light and thermal infrared band. method couples the surface temperature ( ) and vegetation index ( ) and realizes quantitative inversion of soil moisture through changing characteristics of characteristic space. This method decreases parameters for soil moisture inversion and has a low research cost. Currently, method has achieved some success in soil moisture inversion, drought monitoring and crop output estimation in different spatial scales [9,10,[12][13][14][15]. Spatial distribution of soil moisture is mainly influenced by meteorological factors (e.g. precipitation, solar radiation and air temperature), underlying properties (e.g. physical properties of soil, surface vegetation and land use) and topographic conditions (e.g. slope, slope orientation, elevation and slope position) [16]. Yisok et al. [17] taking the bare soil experimental area as an example, retrieved the soil moisture content based on the microwave remote sensing data, and achieving a high accuracy. Lievens et al. [18] carried out a soil moisture inversion in the vegetation coverage regions with underlying vegetation based on HH polarization data of ALOS/PALSAR radar by combining water-cloud model and effective roughness parameters, and the lowest inversion error was close to 5.5 vol%. However, terrain factors are important factors that influence spatial distribution of soil moisture in complicated mountainous regions in addition to meteorological factors and underlying properties. Currently, a series of studies on land use, vegetation coverage and drainage basin distribution based on terrain factors have been reported. Wu et al. [19] analyzed terrain gradient characteristics of ecological land use changes in plateau regions on dams from perspectives of elevation, slope and slope orientation. They found that distribution of ecological land use in plateau regions on dams was significantly sensitive to terrain factors. These studies mainly discuss terrain gradient characteristics of ecological land use, but have not discussed influences of terrain on soil moisture distribution. Tian et al. [20] analyzed influences of slope position on soil moisture and spatial vegetation distribution in Inner Mongolia grassland, and disclosed an order of different slopes with respect to average soil moisture in the 0-50 cm soil layer: slope bottom > shady slope >sunny slope and slope top. This study mainly analyzed influences of slope position on soil moisture distribution, but it didn't investigate influences of slope and slope orientation on soil moisture distribution. Luo [21] analyzed the relationship between soil moisture and terrain factors in a small-scaled sampling region, finding a negative correlation between soil moisture and slope. The highest soil moisture was on the east slope, followed by the northeast slope. However, this study involved a small study area and soil moisture was gained through interpolation of sampling points, which was different from terrain in mountainous region to some extent.
Although there are many studies on soil moisture and the application fields are extensive, these studies mainly focus on single aspects, such as inversion method of soil moisture, influencing factor of soil moisture, etc. Therefore, it is necessary to strengthen not only studies on topographic effect and spatial-temporal distribution features of soil moisture in complicated mountainous areas, but also strengthen ecological environmental governance in mountainous areas based on research results. Based on existing scientific understanding and research results, this study further explored topographic effect and spatialtemporal distribution laws of soil moisture in Dengfeng city which is a complicated mountainous region in China from perspectives of elevation, slope and slope orientation. In this study, surface temperature, soil moisture and terrain factors in the study area were used as the main research indexes. Research conclusions can provide some scientific references to improve ecological environment in mountainous region.
The reminder of this study is organized as follows. Section 2 introduces the study area, preprocessing of remote sensing images and terrain factors, as well as inversion method of surface temperature and soil moisture. Section 3 is a quantitative inversion of surface temperature and soil moisture in the study area through radiation transfer method and method, which discloses the spatial-temporal distribution laws of surface temperature and soil moisture. In addition, influencing laws of elevation, slope and slope orientation on distribution of soil moisture are analyzed. Countermeasures to improve ecological environment in mountainous region are proposed by combining relevant results. Section 4 summarizes conclusions.

Study area
The study area was chosen at Dengfeng city (112°49′~ 113°19′E, 34°35′~34°15′N), which was subordinated to Zhengzhou city, Henan province, China. The study area is next to Xinmi in the east, Yichuan county in the west, Yuzhou city and Ruzhou city in the south, and Yanshi city and Gongyi city in the north. It is 58 km long from the east to west, and 36 km wide from south to north, covering an area of 1220 km 2 . Elevation of this study area ranges between 156~1459 m. Dengfeng city has significant different terrains and great topographic relief. There is Songshan in the north, and Jishan and Xiongshan in the south, all of which run from east to west. The general terrain declines to hills and rivers from north-south to middle gradually. According to terrains, the study area can be divided into deep mountains, shallow mountains, hills and flat regions. Among them, the deep mountains account for 17% of total area in the Dengfeng city, while shallow mountains, hills and flat regions account for 30%, 36% and 17% of total areas, respectively. Slopes of hills are mainly larger than 6°. Dengfeng city belongs to warm temperate continental monsoon climate and there are four distinctive seasons, with great temperature differences. The annual average sunshine duration is 2297 hours. The annual average temperature is 14.2℃, with extreme temperatures of 40.5℃ and -15.1℃, respectively. The frost-free season lasts for 238 days in a year and the annual average precipitation is 614 mm.

Data acquisition and preprocessing
Data used in this study included Landsat-8 OLI remote sensing image data and DEM data in winter of 2013 and 2017.

Satellite remote sensing data and preprocessing
In this study, Landsat-8 OLI remote sensing data were downloaded from the website of geographic space data cloud platform (http//:www.gscloud.cn) on December 29th, 2013 and on December 24th, 2017. Spatial resolution of images was 30 m. Image stripe number was 124/036. The longitude and latitude of image centers were 113.45752° and 34.61065°, respectively. The mean cloudiness was lower than 10%. Preprocessing of Landsat-8 OLI images was mainly implemented in ENVI5.3, including radiometric calibration, atmospheric correction, image registration and s T NDVI s T NDVI -TVDI TVDI image cropping. Specifically, radiometric calibration of image data was realized by the command of radiometric calibration, and atmospheric correction of radiance data was performed by the command of FLAASH atmospheric correction. Subsequently, remote sensing images in 2017 were used as the benchmark and remote sensing images in 2013 were applied for registration. Finally, image cropping was conducted based on vector data of administrative zoning in Dengfeng city, thus getting remote sensing image data of the study area. Meanwhile, conventional meteorological data at satellite transition were collected.

Data of terrain factors and preprocessing
Terrain factor data in this study mainly covered data about elevation, slope and slope orientation. The DEM data in this study was downloaded from the geographic space data cloud platform (http//:www.gscloud.cn) and spatial resolution of images was 30 m. Since elevation of the study area ranged between 156~1459 m, DEM was divided into 5 levels at an interval of 250 m: <250 m, 250~500 m, 500~750 m, 750~1000 m and >1000 m. Slope data was gained from DEM processing by using Slope tools in ArcGIS10.2. According to regulations in General Rules for Comprehensive Governance and Planning of Soil and Water Retention (GB/T 15772-2008), slope data were divided into five levels, including flat slope (<5°), gentle slope (5°~15°), relatively gentle slope (15°~25°), abrupt slope (25°~35°) and extremely abrupt slope (>35°). Slope orientation data was gained from processing of DEM data by using Aspect tool in ArcGIS10.2 and it was also divided into five levels: flat slope (-1°), sunny slope (135°~225°), shady slope (315°~45°), semi-sunny slope (90°~135° and 225°~270°) and semi-shady slope (45°~90° and 270°~315°). Terrain factor data were cropped by the vector data of administrative zoning in the study area, thus getting elevation, slope and slope orientation diagrams of the study area ( Fig.1).

Surface temperature inversion based on radioactive transfer equation
Surface temperature in the study area was inversed by radioactive transfer equation (RTE). RTE, or known as atmospheric correction method, calculates effects of atmosphere on surface thermal radiation in the atmospheric radioactive transfer model according to real-time atmospheric sounding data or standard atmospheric profile, and then corrects results to real surface temperature according to surface specific radiation [22]. The calculation formula is: where is the surface temperature (K). and are constants, where and .
is the Planck black-body radiance When the surface temperature is , which can be gained from the deformed RTE: where is the spectral radiance of thermal infrared waveband which is received by the sensor.
, and are ascending and descending radiances of atmosphere as well as atmosphere transmissivity. is the surface specific radiance which is estimated by the method proposed by Sobrino [22] and Qin [23]. The calculation formula is: where refers to the normalized difference vegetation index, and is the vegetation coverage and it is determined according to . The calculation formula is: where and are values of pixels of pure naked soil and pure vegetation. is the reflectance of near infrared band and is the reflectance of infrared band.

Soil moisture inversion based on TVDI
Soil moisture inversion was conducted by temperature vegetation dryness index ( ). method was proposed by Sandholt [8]. When they were simplifying the characteristic space from a trapezoid into a triangle based on existing trapezoid characteristic space. method not only decreases parameters for soil moisture inversion, but also saves accumulation of years of historical image data, thus claiming a low cost. The calculation formula of is: where is surface temperature, is the minimum surface temperature (℃) under the same conditions, that is, the wet edge of the characteristic space.
is the maximum surface temperature (℃) under the same conditions, that is, the dry edge of the characteristic space. Eqs. (7) and (8) were brought into the Eq. (6), thus getting the calculation formula of : where , , and are coefficient of fitting equations of dry and wet edges, respectively.
value of dry edge is 1, while value of wet edge is 0. value is negatively correlated with soil moisture.

Spatial-temporal distribution changes of surface temperatures
According to Spatial-temporal distribution changes of surface temperature in 2013 and 2017 were discussed through data preprocessing in Section 2.2 and the surface temperature inversion based on RTE in Section 2. It can be seen from Fig. 2 that surface temperature in the study area ranged between -7.16°~17.03° in 2013, and it concentrated at 0°~10° in most regions. Temperature changed greatly in the whole study area. There's relatively low temperature in north and southwest edges of the study area, but relatively high temperature in the center and north regions. Surface temperature in urban area of Dengfeng city was about 10° higher than that in surrounding rural areas and  In addition, surface temperature in north and southwest edges of the study area was relatively low due to the low solar radiation and air temperature in winter.

Spatial-temporal variations of soil moisture
According to data preprocessing in Section 2.2, surface temperature inversion based on RTE in Section 2.3 and soil moisture inversion based on in Section 2.4, spatialtemporal variation characteristics of soil moisture in 2013 and 2017 were discussed in this section. Based on the inversed surface temperature and , the fitting equation of dry and wet edges of characteristic space were calculated in the ENVI5.3-IDL environment by using ZONALMAX and ZONALMIN functions. According to principle of , the calculated equations of dry and wet edges were brought to the Eq.(9), thus getting spatial distribution of in the study area. Moreover, water and urban land in the study area were extracted by supervised classification technique to reduce their influences on analysis results. Mask document was prepared to hide water and urban land in the study area. Subsequently, soil moisture of natural surface types was analyzed. The spatial distribution of in the study area after masking is shown in Fig. 3. In the study area, numerical value of ranged between 0~1.
was used as the drought grading index and it was divided into five levels [4]: wet (0< ≤0.2), normal (0.2< ≤0.4), slightly drought (0.4< ≤ 0.6), drought (0.6< ≤ 0.8), and severe drought (0.8< ≤1). Results are shown in Fig.4. In addition, a statistical analysis on number of pixels in each level and proportions was carried out. Results are listed in Table 1.   TVDI   NDVI  TVDI   TVDI   TVDI   TVDI   TVDI  TVDI  TVDI  TVDI  TVDI   TVDI  TVDI It can be seen from Fig. 4 and Table 1 that drought area had an extensive distribution range in 2013 and 2017, which accounted for a high percentage of the study area (59.4% in 2013 and 65.03% in 2017). Drought areas were mainly in the Midwest and south regions of the study area. Slightly drought areas ranked the second, followed by severe drought areas and normal areas. Wet area accounted for the smallest percentage of the study, which was only 0.09% in 2013 and 0.14% in 2017. Wet areas were mainly in north and southwest regions of the study area. Severe drought area in 2017 increased by as high as 9.06% compared with that in 2013. The increased severe drought areas were mainly in middle and east regions of the study area. The drought area ranked the second in expansion and wet region was expanded slightly. However, normal region and slight drought area were narrowed significantly. Specifically, the slightly drought area was decreased the mostly by 12.01%. According to comparison of two images and data statistics, soil moisture in the study area decreased continuously from 2013 to 2017, while the drought area region increased continuously.

Effects of elevation on soil moisture distribution in mountainous areas
Based on elevation grading in Section 2.2 and soil moisture inversion results in Section 2.4, the influencing law of elevation on soil moisture distribution in mountainous region was disclosed by analyzing soil moisture in different elevation levels. Elevation grading diagram was overlapped with the TVDI diagrams in 2013 and 2017 to make statistics on mean TVDI in different elevation levels. Meanwhile, the relation curve between TVDI and elevation was drawn (Fig.5).

Effects of slope on soil moisture distribution in mountainous areas
Based on elevation grading in Section 2.2 and soil moisture inversion results in Section 2.4, the influencing law of slope on soil moisture distribution in mountainous region was disclosed by analyzing soil moisture in different slope levels. Slope grading diagram was overlapped with the diagrams in 2013 and 2017 to make statistics on mean in different slope levels. Meanwhile, the relation curve between and slope was drawn (Fig. 6). In Fig.6, value in 2013 and 2017 declined gradually with the increase of slope. The lowest soil moisture was in the region with slope smaller than 5°, accompanied with the highest value, reaching 0.66 in 2013 and 0.74 in 2017. The highest soil moisture was in the region with slope higher than 35°, while the values were the lowest, which was 0.58 in 2013 and 2017. In region with slopes of 5° and 5°~15°, values in 2017 were higher than those in 2013, indicating that soil moisture in 2017 decreased than that in 2013. In particular, soil moisture in the region with slope smaller than 5° showed the highest difference (0.08) between 2013 and 2017. In regions with slopes of 15°~25° and >35°, value in 2017 was equal to that in 2013. In the region with slope of 25°~35°, value in 2017 was slightly lower than that in 2013. Generally speaking, soil moisture increased gradually with the increase of slope. Soil moisture in the <25° region in 2017 was lower than that in 2013, but the soil moisture in regions >25° in 2017 was higher compared to those in 2013.

Effects of slope orientation on soil moisture distribution in mountainous areas
Based on elevation grading in Section 2.2 and soil moisture inversion results in Section 2.4, the influencing law of slope orientation on soil moisture distribution in mountainous region was disclosed by analyzing soil moisture in different slope orientations. Slope orientation grading diagram was overlapped with the diagrams in 2013 and 2017 to   TVDI   TVDI   TVDI  TVDI   TVDI   TVDI  TVDI   TVDI   TVDI   TVDI   TVDI   TVDI  TVDI TVDI make statistics on mean in different slope orientations. Meanwhile, the relation curve between and slope orientation was drawn (Fig.7). values on semi-sunny slope and semi-shady slope were the second highest. The lowest values were on the shady slope, which were 0.55 in 2013 and 0.62 in 2017. This reflected that soil moisture on shady slope was the highest, followed by those on semi-shady slope and semi-sunny slope, and then sunny slope successively. In other words: there's an order of soil moisture: shady slope > semi-shady slope > semi-sunny slope > sunny slope. This trend can be explained as follows. Slope orientation influences soil moisture distribution by influencing the solar radiation received by surface. Sunny slope generally are exposed to solar radiation completely, so the soil moisture is the lowest. On contrary, shady slope is easy to be blocked by mountains and receives less solar radiation, thus having relatively high soil moisture. This conforms to the geographical differentiation law of shady and sunny slopes in geography.
values under different slope orientations in 2017 were higher than those in 2013. The maximum difference (0.13) of values in 2013 and 2013 was detected on flat slopes. differences on shady slope, semi-shady slope, semi-sunny slope and sunny slow in 2013 and 2017 fluctuated slightly within 0.05~0.07. Generally speaking, soil moistures on different slope orientations in 2017 were lower than those in 2013 to different extent. In a word, shady abrupt slopes at middle and high elevation are the region of forest vegetation in mountainous areas and thereby have relatively higher soil moisture. However, these slopes are typical ecological vulnerable region. Ecologies in these slopes are difficult to be restored once they are destroyed. Hence, it shall try to decrease artificial interference activity, strengthen protection to forest vegetation, planting trees and grasses to increase soil infiltration, lower soil washing by surface runoff, and improve ecological environment in mountainous areas.

Conclusion
To disclose topographic effect of soil moisture in mountainous areas with complicated topographic conditions, a quantitative inversion of soil moisture in winter in Dengfeng city, Henan province in 2013 and 2017 is carried out through method based on Landsat-8 OLI remote sensing images and DEM data as well as and surface temperature of the study area. Influences of terrain factors on spatial distribution of soil moisture in mountainous region are gained through overlapping analysis of elevation, slope and slope orientation. Based on analysis results, some countermeasures to improve ecological environment in mountainous areas are proposed. This study finally concludes: (1) Surface temperature in the study area ranges between -7.16°~19.75° and temperature changes significantly in the whole study area. Generally speaking, surface temperature increases gradually from south-north to middle. Surface temperature in urban areas is about 10° higher than those in surrounding rural areas and suburbs. This forms significant urban heat island effects. From 2013 to 2017, surface temperature increases dramatically and the distribution range of high temperature expands significantly. The heat island effect has been strengthening continuously.
(2) In 2013 and 2017, drought area accounts for the largest area of total study area, followed by slightly drought area, severe drought area and normal area successively. The wet area accounts for the smallest proportion of the study area. The severe drought area has been expanded mostly from 2013 to 2017, followed by drought area and wet area. Normal area and slightly drought area have been decreased. To sum up, soil moisture in the study area decreases continuously, while drought area expands year by year.
(3) Elevation and slope can influence spatial distribution characteristics of surface soil moisture. With the increase of elevation and slope, soil moisture increases gradually. Effects of slope orientation on soil moisture are mainly manifested by higher soil moistures on shady and semishady slopes, but low on sunny and semi-sunny slopes. Generally, there's an order of soil moisture: shady slope > semi-shady slope > semi-sunny slope > sunny slope.
To sum up, the topographic effect on soil moisture in complicated mountainous region is disclosed by analyzing influences of terrain factors. Theoretical references are provided to formulate water conservation and ecological environmental protection programs in mountainous areas by analyzing distribution laws of soil moisture on different elevations, slopes and slope orientations. Terrain is an important factor that influences soil moisture distribution in mountainous areas. However, spatial distribution of soil moisture is related with surface temperature and vegetation coverage types to some extent, thus resulting in the uncertainty of soil moisture distribution. Influences of surface temperature and vegetation coverage type on soil moisture in mountainous area shall be considered in future studies.