Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index
- *Corresponding Author:
- Joseph Essamuah–Quansah
Department of Agricultural and Environmental Sciences
Tuskegee University, Tuskegee, USA
E-mail: [email protected]
Received Date: June 09, 2017; Accepted Date: June 22, 2017; Published Date: June 26, 2017
Citation: Welikhe P, Quansah JE, Fall S, Elhenney WMc (2017) Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index. J Remote Sensing & GIS 6: 200. doi: 10.4172/2469-4134.1000200
Copyright: © 2017 Welikhe P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The global agronomy community needs quick and frequent information on soil moisture variability and spatial trends in order to maximize crop production to meet growing food demands in a changing climate. However, in situ soil moisture measurement is expensive and labor intensive. Remote sensing based biophysical and predictive regression modeling approach have the potential for efficiently estimating soil moisture content over large areas. The study investigates the use of Moisture Stress Index (MSI) to estimate soil moisture variability in Alabama. In situ data were obtained from Soil Climate Analysis Network (SCAN) sites in Alabama and MSI developed from LANDSAT 8 OLI and LANDSAT 5 TM data. Pearson product moment correlation analysis showed that MSI strongly correlates with 16-day average growing season soil moisture measurements, with negative correlations of -0.519, -0.482 and -0.895 at 5, 10, and 20 cm soil depths respectively. The correlations of MSI and growing season moisture were low at sites where soil moisture was extremely low (<-0.3 at all depths). Simple linear regression model constructed for soil moisture at 20 cm depth (R²=0.79, p<0.05) correlated well with MSI values and was successfully used to estimate soil moisture percentage within a standard error of ± 3. Resulting MSI products were used to successfully produce the spatial distribution of soil moisture percentage at 20 cm depth. The study concludes that MSI is a good indicator of soil moisture conditions, and could be efficiently utilized in areas where in situ soil moisture data are unavailable.