Remote Sensing Applications and Decision Support

Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels

[+] Author Affiliations
Mohammad Reza Mobasheri

K.N. Toosi University of Technology, Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, Tehran 19697-15433, Iran

Meisam Amani

Memorial University of Newfoundland, C-CORE, St. John’s, Newfoundland, A1B 3X5, Canada

J. Appl. Remote Sens. 10(2), 026011 (Apr 26, 2016). doi:10.1117/1.JRS.10.026011
History: Received November 13, 2015; Accepted March 7, 2016
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Abstract.  Soil moisture content (SMC) plays an important role in different environmental. In this study, four different soil moisture indices, namely, SOMID, SOMID-FS, SOMID-FT, and CSOMID-FT, were introduced. In this work, the following parameters were used to estimate SMC at a depth of 5 cm: (a) the distance of pixels from the origin in the scatter-plot of near-infrared (NIR) and red bands (SNIR-R), (b) the fraction of soil cover in each pixel, and (c) the land surface temperature. It was concluded that the CSOMID-FT was the most accurate index for estimation of SMC (RMSE=0.045, R=0.92). This index divides the SNIR-R into three separate regions based on the pixels’ normalized difference vegetation index (NDVI) values and assigns a specific regression equation to each region. The results showed that as the NDVI values increase, the accuracy of the proposed indices decreases. Furthermore, the SOMID-FT and CSOMID-FT were used to estimate SMC at five different depths of 5, 10, 20, 50, and 100 cm. It was concluded that the satellite-estimated SMC was highly correlated with the field-measured data at 5-cm soil depth.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Mohammad Reza Mobasheri and Meisam Amani
"Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels", J. Appl. Remote Sens. 10(2), 026011 (Apr 26, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026011


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