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Sensors 2015, 15(1), 1925-1944; doi:10.3390/s150101925

Improvement of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (USA) High Plains

1,†,* and 2,†
1
Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
2
Department of Plant and Soil Science, Texas Tech University and Research Center, Texas A&M, Lubbock, TX 79409, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 24 October 2014 / Accepted: 7 January 2015 / Published: 16 January 2015
(This article belongs to the Section Remote Sensors)
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Abstract

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are ongoing. This study aims at developing a new index, the Thermal Ground cover Moisture Index (TGMI), for estimating soil moisture content. This index is based on empirical parameterization of the relationship between raw image digital count (DC) data in the thermal infrared spectral band and ground cover (determined from raw image digital count data in the red and near-infrared spectral bands).The index uses satellite-derived information only, and the potential for its operational application is therefore great. This study was conducted in 18 commercial agricultural fields near Lubbock, TX (USA). Soil moisture was measured in these fields over two years and statistically compared to corresponding values of TGMI determined from Landsat image data. Results indicate statistically significant correlations between TGMI and field measurements of soil moisture (R2 = 0.73, RMSE = 0.05, MBE = 0.17 and AAE = 0.049), suggesting that soil moisture can be estimated using this index. It was further demonstrated that maps of TGMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region. View Full-Text
Keywords: thermal infrared; raw image digital count; soil moisture; estimation thermal infrared; raw image digital count; soil moisture; estimation
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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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MDPI and ACS Style

Shafian, S.; Maas, S.J. Improvement of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (USA) High Plains. Sensors 2015, 15, 1925-1944.

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