Soil moisture variations at different topographic domains and land use types in the semi-arid Loess Plateau, China

CATENA ◽  
2018 ◽  
Vol 165 ◽  
pp. 125-132 ◽  
Author(s):  
Bowei Yu ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Xiaoping Wang ◽  
Jiuliang Feng ◽  
...  
2019 ◽  
Vol 50 (5) ◽  
pp. 1281-1292 ◽  
Author(s):  
Bowei Yu ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Chong Huang ◽  
He Li

Abstract Deep soil moisture is fundamental to hydrological cycle and ecosystem sustainability in arid and semi-arid regions. This study examined the combined effects of topographic domain and land use on the spatial variability of deep soil moisture (0–5 m) on the semi-arid Loess Plateau of China. Our results showed that deep soil moisture was generally temporally stable due to the thick loess soil in the plateau region. The depth-averaged soil moisture was slightly lower in the gully domain compared to the hillslope domain but was dependent on the soil depths. Soil moisture variability was clearly larger in the gully domain when compared with that in the hillslope domain in the 0–5 m profile. The mean soil moisture contents in comparable soil depths were lower in forestland than in grassland (and farmland), particularly in the hillslope domain. Land uses had similar vertical distribution characteristics of deep soil moisture for each topographic domain. Soil moisture showed highly significant positive correlations with slope aspect in the hillslope domain and with profile curvature in the gully domain.


2012 ◽  
Vol 475 ◽  
pp. 111-122 ◽  
Author(s):  
Lei Yang ◽  
Wei Wei ◽  
Liding Chen ◽  
Baoru Mo

2017 ◽  
Vol 37 (6) ◽  
Author(s):  
索立柱 SUO Lizhu ◽  
黄明斌 HUANG Mingbin ◽  
段良霞 DUAN Liangxia ◽  
张永坤 ZHANG Yongkun

2018 ◽  
Vol 19 (3) ◽  
pp. 1179-1189 ◽  
Author(s):  
Bowei Yu ◽  
Gaohuan Liu ◽  
Qingsheng Liu ◽  
Chong Huang ◽  
He Li ◽  
...  

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.


2019 ◽  
Vol 17 (1) ◽  
pp. 105-115 ◽  
Author(s):  
Jiao-Jiao Han ◽  
Xu Duan ◽  
Yang-Yi Zhao ◽  
Meng Li

AbstractSoil moisture, stable hydrogen, and oxygen isotopes were sampled and determined in a demonstration area of soil and moisture conservation at the Laocheng Town of Yuanmou County in Chuxiong Prefecture, Yunnan of three land use types: Leucaena Benth artificial forest, Heteropogon contortus grass field, and farmland. The characteristics of stable hydrogen and oxygen isotopes of soil moisture in these different land use types at different soil depths were analyzed to investigate the regularities in the quantitative formation of soil moisture balance. In terms of forest land, we found that the variable coefficient of hydrogen isotopes in the 0-20 cm soil layer was the smallest, but decreased with depth under 20 cm. While in grassland, the variable coefficient in 80-100 cm was the largest, and decreased with depth above 80 cm. As for farmland, the variable coefficient in the top 20 cm was the largest, followed by 40-60 cm, and the medium 20-40 cm was the smallest. The soil moisture hydrogen isotope values of three land use type were different at surface layer, but prone to be consistent in each type. Along the soil depth in forest land, the hydrogen isotope increased first and then decreased, while increased in the end, and the maximum appeared in 80-100 cm. In grassland, the hydrogen isotope increased initially as the forest land but then decreased continuously, so the maximum was found at 20-40 cm. And in grassland, the hydrogen isotope of all depths were higher than which of forest land and farmland. In same land use type, the hydrogen isotope of soil moisture changed significantly at the surface, and the variation of hydrogen isotopes was obviously decreased along the depth. Our findings could provide reference data which would contribute to the assessment of regional groundwater resources in the dry-hot valley of Yuanmou in this study.


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