Thermal Satellite Imagery Analysis and Emissivity Characteristics for the Prediction of Oil Reservoirs Existence

Simultaneous transformation of natural land cover contributes significantly to changing the surface phenomena making accurate forecasting difficult. Ground surveys would permit Land Use Land Cover (LULC) classification, but they are burdensome, expensive, and time-consuming and lacks accuracy in the exploration for all target areas at the same time, which highlights satellite imagery an evident and preferred alternative. A new mathematical method has been used for calculating the highest and lowest Land Surface Temperature (LST) to improve model accuracy for a particular location and detect oil seep-induced alteration from Landsat Data Continuity Mission (LDCM). Two sites within different patterns were chosen to represent a diversity of thermal anomalies positions in the study area. The results show that there are no traces of oil in either zone 1 lies which in the Kanoya province and zone 2 located east of the coast of Kagoshima which is overlooking the Pacific Ocean. In the end, the night-time scenes of the Thermal Infrared Sensor (TIRS) achieve a high gain of the thermal image with radiance 3.2-12.65 (Watts/m
²
sr μm). Moreover, the analysis of the (TIRS) image is an optimal explanation for geomorphology and subsurface geology in southern Japan via the potentiality of the simulators set.