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Remote Sensing Technology and Application  2022, Vol. 37 Issue (6): 1460-1471    DOI: 10.11873/j.issn.1004-0323.2022.6.1460
Spatial-temporal Variation of Soil Moisture in Karst Area based on LST-VI Feature Space
Hongbo Yan1,2(),Hao Li1,Xianjian Lu1,2(),Jiahua Wang1
1.College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541004,China
2.Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin 541004,China
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Surface soil moisture is a key parameter of terrestrial systems in terms of ground-air energy exchange, and the surface soil moisture in karst areas is an important factor to promote karst action, affect the soil erosion, and cause karst rocky desertification. Therefore, it is of great significance to accurately determine the distribution of surface soil moisture and its dynamic change in karst areas to the safety of ecological and geological environment and regional climate change. Taking the typical karst area of Guangxi as the study area the LST-VI feature space was constructed using MODIS surface temperature data and vegetation index data. Firstly, the applicability of the four different vegetation indices (NDVI, EVI, SAVI, and FVC) in karst areas was compared, and FVC is selected as the optimal vegetation index for the LST-VI feature space in the study area. The soil moisture index M0 based on the uniform normalized T*-FVC feature space is derived. The track and regularity of M0 with time under different underlying surface types such as forest, farmland and karst areas are studied in detail,as well as the spatial distribution changes of M0 and the causes. The results show that the track and regularity of M0 with time is similar under the same underlying surface type in the T*-FVC feature space, indicating that the underlying surface type is an important factor affecting the change of soil moisture. In terms of spatial distribution, the distribution of M0 in Guangxi has the characteristics of being smaller in summer than in winter, smaller in southwest than in northeast, smaller in karst than in non-karst areas, and the seasonal variation of M0 in farmland is evident. Overall, the spatio-temporal dynamic change of soil moisture in karst areas has been achieved using the normalized T * -FVC feature space.

Key words:  Karst      Soil moisture      Land surface temperature      Vegetation index      LST-VI feature space     
Received:  19 April 2022      Published:  15 February 2023
ZTFLH:  TP79  
Corresponding Authors:  Xianjian Lu     E-mail:;
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Hongbo Yan
Hao Li
Xianjian Lu
Jiahua Wang

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Hongbo Yan,Hao Li,Xianjian Lu,Jiahua Wang. Spatial-temporal Variation of Soil Moisture in Karst Area based on LST-VI Feature Space. Remote Sensing Technology and Application, 2022, 37(6): 1460-1471.

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Fig.1  Guangxi karst landform concentration area
Fig.2  Guangxi weather station distribution map
Fig.3  Schematic diagram of LST-VI triangle feature space
Fig.4  Schematic diagram of LST-VI trapezoidal feature space
Fig.5  T*-VI feature space diagram
Fig.6  Experimental points distribution for soil moisture change analysis
Fig.7  Flow chart of data processing
Fig.8  Characteristic space of surface temperature and four vegetation indices
Fig.9  Feature space of surface temperature and four vegetation indices after removing outliers
原始特征空间NDVI干边y=-11.106 5*x+28.677 80.94
湿边y=-4.578 2*x+18.113 50.40
EVI干边y=2.265 2*x+23.360 30.17
湿边y=3.258 9*x+14.314 90.39
FVC干边y=-6.923 5*x+24.931 70.91
湿边y=-2.992 7*x+16.870 60.46
SAVI干边y=-22.731 4*x+28.932 20.92
湿边y=-4.946 0*x+15.761 30.22
剔除温度异常值的特征空间NDVI干边y=-10.879 6*x+28.036 50.97
湿边y=-5.671 6*x+19.309 40.73
EVI干边y=1.824 5*x+22.834 60.21
湿边y=3.134 7*x+15.149 20.58
FVC干边y=-7.568 5*x+24.587 00.96
湿边y=-3.126 7*x+17.636 70.69
SAVI干边y=-19.954 9*x+27.508 40.89
湿边y=-1.72 9*x+15.708 10.04
Table 1  Fitting equations for the characteristic spatial dry and wet edges of four vegetation indices and surface temperature
Fig.10  Trajectory of M0 with time in forest, farmland, and karst mountains
Fig.11  Spatial distribution map of M0 in Guangxi region in different periods
Fig.12  Statistical chart of M0 value domain percentages for each period of 2009—2010 in Guangxi
Table 2  Meteorological station site information
Fig.13  Plot of soil moisture index M0 versus measured values
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