An adaptive spectral index for carbonate rocks using OLI Landsat-8 imagery

The accurate characterization of carbonate rocks is crucial for determining the potential for exploring outcrops in the oil industry. Modern remote sensing instruments have played an important role in estimating mineral characteristics in the most varied scales. However, mathematical expressions suggested for converting radiance values to mineral information are often designed to work in only one particular territory. At the same time, the few general solutions existing tend to achieve overall poor results. In this paper, we propose an adaptive approach to estimate subpixel amounts of carbonate rocks using OLI-Landsat 8 image data. The main formulation is derived from exhaustive analysis of the spectral behavior of several carbonate materials. The formulation is subsequently adapted through the use of a genetic algorithm to work on diverse situations. Experiments have demonstrated the adaptability of the method for different environments, as well as its higher performance when compared with existing carbonate indices.

Automated summary

Accurate characterization of carbonate rocks is essential for evaluating potential outcrops in the oil industry. Remote sensing instruments have played a key role in estimating mineral characteristics at various scales. However, existing mathematical expressions for converting radiance values to mineral information are often limited to specific regions and general solutions tend to yield poor results. This paper proposes an adaptive approach using OLI-Landsat 8 image data to estimate subpixel amounts of carbonate rocks. The method is derived from extensive spectral analysis and further optimized through a genetic algorithm, demonstrating its adaptability and superior performance compared to existing carbonate indices.