*3.2. Methods*

#### 3.2.1. FLAASH Atmospheric Correction and Remote Sensing Image Fusion

Solar radiation needs to pass through the atmosphere before it is collected by satellites [32]. Due to this, remote sensing images include complex information derived from the atmosphere and the Earth's surface. As this research is focused on the quantitative analysis of surface reflectance, we need to mitigate the influence from the atmosphere. Using the Atmospheric Correction Module, we can compensate for atmospheric effects.

Atmospheric correction can be realized using many available software tools. For example, the Atmospheric Correction Module in the ENVI software [33] provides two atmospheric correction modeling tools for retrieving spectral reflectance from multispectral and hyperspectral radiance images: Quick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The accuracy of FLAASH model is higher than that of QUAC model. The application of QUAC model is simpler than that of FLAASH, and it has less dependence on input parameters and calibration accuracy of instruments [34,35]. FLAASH is a first-principle atmospheric correction tool that corrects wavelengths in the visible through near-infrared and shortwave infrared regions. In this study, the atmospheric correction of Landsat images was carried out using the FLAASH tool within the ENVI software [36].

Image fusion in remote sensing has several application domains. An important domain is multi-resolution image fusion [37]. Many different multi-resolution image fusion methods are available with different characteristics [38], including Gram–Schmidt Pan Sharpening [39], HSV Transformation [40], and Brovey Transformation [41]. Using these image fusion methods, important information from multiple images can be gathered together to form a new image with both high spatial resolution and multispectral characteristics. The OLI has two types of images including panchromatic images and multispectral images. On the basis of comparing different methods, we selected the Gram–Schmidt Pan Sharpening method to fuse images due to its superiority to maintain spatial texture information, especially to keep spectral features with high fidelity [42].
