A methodology for retrieval of environmental noise equivalent spectra applied to four Hyperion scenes of the same tropical coral reef

The accuracy of environmental variable estimation from remote sensing is strongly related to the capacity of the measurement for separating the desired radiance signal from the overall noise in a pixel or image. Therefore, an algorithm for locating the most suitable spatial areas for environmental noise equivalent retrieval in a remote sensing image was developed and validated, first on a custom-built monochromatic image and then on a multi-temporal, hyperspectral data set. The algorithm, Automated Local Convergence Locator (ALCL), successfully identified areas in the custom-built image where known amounts of added noise equivalents could be accurately retrieved. Applying ALCL to the hyperspectral data (four Hyperion scenes of the same tropical coral reef scene) confirmed that noise equivalent characteristics vary limitedly on a scene-by-scene basis. The four Hyperion noise equivalent spectra collected in locations identified by ALCL as suitable showed consistency in both shape and magnitude despite being acquired 6 months apart, and differed from equivalent spectra from image locations identified as not suitable for noise estimation. The ALCL algorithm and associated methodology required limited user input while objectively determining the areas in an image most suitable for noise equivalent estimation. The ALCL methodology is applicable to studies where noise equivalent quantities in remote sensing imagery need to be objectively determined, such as feasibility studies, environmental monitoring, and sensitivity analysis.