Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images
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ORIGINAL PAPER
Measurement and zonation of soil surface moisture in arid and semi-arid regions using Landsat 8 images Reza Dehghani Bidgoli 1 & Hamidreza Koohbanani 2 & Ali Keshavarzi 3 & Vinod Kumar 4 Received: 23 April 2019 / Accepted: 12 August 2020 # Saudi Society for Geosciences 2020
Abstract Monitoring of soil surface moisture is an imperative factor in water and energy cycle. Due to the variability of soil characteristics such as topography, vegetation, and climate dynamics, this important factor varies with respect to time and place. Measuring methods can provide soil moisture information in a wide range of short intervals with reasonable accuracy. In present research, Landsat 8 satellite data with various soil moisture content estimation methods were tested. In order to evaluate the accuracy of each method, the real-field data used 80 samples of volumetric soil moisture content in suburban areas of Semnan city that were collected at the time of satellite passage of the area. Some of the indicators used in this study are normalized vegetation index, NDTI index, NDMI index, PSMI index (use full form of these indices), surface temperature, and SMSWIR index. The SMSWIR index with correlation coefficient was 0.78, and the correlation coefficient of regression model was 0.61, and RMSE was 3.69. The results of the regression model and real data were estimated to be 3.69, which are recommended for assessing surface soil moisture in arid and desert regions. Three indicators of SMSWIR index, NDTI index, and NDMI index with a small difference are not suitable indices for measuring soil moisture content in desert areas with vegetation cover. By employing multivariable regression models, soil moisture model was also prepared by using the studied indices. The findings of this research indicate that the simultaneous correlation model is superior to the surface soil moisture mapping. Keywords Remote sensing . SMSWIR . LST . Landsat . Multivariate regression models
Introduction Soil surface moisture is a key variable for describing water and energy exchanges between the earth and atmosphere (Sun et al. 2012). Time-spatial and spatial distribution data of soil surface moisture variables are used in some studies, such as Responsible Editor: Biswajeet Pradhan * Reza Dehghani Bidgoli [email protected] Vinod Kumar [email protected] 1
Department of Rangeland and Watershed Management, University of Kashan, Kashan, Iran
2
Faculty of Desert Studies, University of Semnan, Semnan, Iran
3
Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box: 4111, Karaj 31587-77871, Iran
4
Department of Botany, DAV University, Jalandhar, Punjab, 144012, India
drought monitoring (Zhang et al. 2015), amount of rainfall and distribution (Van Rooy 1965), and evapotranspiration evaluation (Pengxin et al. 2003). Many studies indicate that there is a close relationship between soil moisture content and plant biomass volume (Koohbanani et al. 2018). The ability to measure and monitor soil mois
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