Abstract:
Soil moisture takes an important part involving climate, vegetation and drought. This
paper explains how to calculate the soil moisture index and the role of soil moisture. The objective
of this study is to assess the moisture content in soil and soil moisture mapping by using remote
sensing data in the selected study area. We applied the remote sensing technique which relies on
the use of the soil moisture index (SMI) which uses the data obtained from satellite sensors in its
algorithm. The relationship between land surface temperature (LST) and the normalized difference
vegetation index (NDVI) are based on experimental parameterization for the soil moisture index.
Multispectral satellite data (visible, red and near-infrared (NIR) and thermal infrared sensor (TIRS)
bands) were utilized for assessment of LST and to make vegetation indices map. Geographic
Information System (GIS) and image processing software were utilized to determine the LST and
NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical
regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI
values are within range −1 to 1 where negative values present loss of vegetation or contaminated
vegetation, whereas positive values explain healthy and dense vegetation. LST values are the
surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to
quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where
values near 1 are the regions with a low amount of vegetation and surface temperature and present
a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and
surface temperature and present the low level of soil moisture. The results indicate that this method
can be efficiently applied to estimate soil moisture from multi-temporal Landsat images, which is
valuable for monitoring agricultural drought and flood disaster assessment.
