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Remote Sens. 2015, 7(3), 2352-2372; doi:10.3390/rs70302352

Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains

Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
Department of Plant and Soil Science, Texas Tech University and Research Center, Texas A&M, Lubbock, TX 79409, USA
Author to whom correspondence should be addressed.
Academic Editors: George P. Petropoulos, Yoshio Inoue and Prasad S. Thenkabail
Received: 12 September 2014 / Revised: 15 January 2015 / Accepted: 29 January 2015 / Published: 26 February 2015
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The growth and yield of crops in the arid and semi-arid regions of the world is driven by the amount of soil moisture available to the crop through rainfall and irrigation. Various methods have been developed for quantifying the soil moisture status of agricultural crops. Recent technological advances in remote sensing have shown that soil moisture can be measured with a variety of remote sensing techniques, each with its own strengths and weaknesses. In this study, building on of the strengths of multispectral satellite imagery, a new approach is suggested for estimating soil moisture content. A soil moisture index, the Perpendicular Soil Moisture Index (PSMI), is proposed; it is evaluated using raw image digital count (DC) data in the red, near-infrared, and thermal infrared spectral bands. To test this approach, soil moisture was measured in 18 agricultural fields in the semi-arid Texas High Plains over two years and compared to corresponding PSMI values determined from Landsat image data. These results showed that PSMI was strongly correlated (R2 = 0.79) with observed soil moisture. It was further demonstrated that maps of PSMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region. While further study is needed to determine the exact relationship between PSMI and soil moisture in larger areas with different climates, this study suggests that PSMI is a good indicator of soil moisture and has potential for operationally monitoring soil moisture conditions at the field to regional scales. View Full-Text
Keywords: soil moisture; thermal infrared; raw image digital count; Perpendicular Index soil moisture; thermal infrared; raw image digital count; Perpendicular Index

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shafian, S.; Maas, S.J. Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains. Remote Sens. 2015, 7, 2352-2372.

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