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

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dc.creatorShafian, Sanaz (TTU)
dc.creatorMaas, Stephen J. (TTU)
dc.date.accessioned2023-01-31T17:21:24Z
dc.date.available2023-01-31T17:21:24Z
dc.date.issued2015
dc.description© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.description.abstractThe 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.en_US
dc.identifier.citationShafian S, Maas SJ. Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains. Remote Sensing. 2015; 7(3):2352-2372. https://doi.org/10.3390/rs70302352en_US
dc.identifier.urihttps://doi.org/10.3390/rs70302352
dc.identifier.urihttps://hdl.handle.net/2346/90497
dc.language.isoengen_US
dc.subjectSoil Moistureen_US
dc.subjectThermal Infrareden_US
dc.subjectRaw Image Digital Counten_US
dc.subjectPerpendicular Indexen_US
dc.titleIndex of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plainsen_US
dc.typeArticleen_US

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