Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents

Abstract

Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i)

Using Satellite-Borne Remote Sensing Data in Generating Local Warming Maps with Enhanced Resolution

“…The method of processing these data consisted of two major steps: (i) collecting the Landsat-8 OLI data and comparing the relationships between multi-spectral (MS) (red and NIR spectral bands at 30 m) and panchromatic (PAN) bands at 15 m; and consequently, determining their suitability to enhance the spatial resolution of the MS bands; and (ii) generating the NDVI at 15 m spatial resolution. We observed significant relationships between the red and PAN bands (i.e., the r 2 values were 0.84-0.96 for approximately 95% of the pixels), and weaker relationships between the NIR and PAN bands (i.e., r 2 values were approximately 0.53) [34]. The weak relation was observed due to the fact that these two bands (i.e., NIR and PAN) do not have overlapping wavelengths [34].…”

“…We observed significant relationships between the red and PAN bands (i.e., the r 2 values were 0.84-0.96 for approximately 95% of the pixels), and weaker relationships between the NIR and PAN bands (i.e., r 2 values were approximately 0.53) [34]. The weak relation was observed due to the fact that these two bands (i.e., NIR and PAN) do not have overlapping wavelengths [34]. As a result, we resampled the 30 m NIR band into 15 m, and used them in conjunction with the pan-sharpened red band to generate the NDVI at 15 m spatial resolution.…”

“…The area covers approximately 37,772 km 2 and comprises built up areas, agricultural land, forests, and water bodies (see Figure 1 for details). Climatically, this area is cold, i.e., annual average temperature in the range of −3.6 to 1.1 • C; and dry, i.e., annual average precipitation varies between 377-535 mm [34].…”

“…We obtained a Landsat-derived NDVI image at 15 m spatial resolution, which was developed in one of our earlier studies [34]. The method of processing these data consisted of two major steps: (i) collecting the Landsat-8 OLI data and comparing the relationships between multi-spectral (MS) (red and NIR spectral bands at 30 m) and panchromatic (PAN) bands at 15 m; and consequently, determining their suitability to enhance the spatial resolution of the MS bands; and (ii) generating the NDVI at 15 m spatial resolution.…”

Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT)

“…The QUick Atmospheric Correction (QUAC) was performed on this dataset by using mud filtering to eliminate highly structured materials such as shallow water, mud and vegetation [55]. ASTER data layer stacked of VNIR+SWIR bands with 30-meter spatial dimensions was generated by using Pan-sharpening method [56]. Internal Average Relative Reflectance (IARR) calibration [57] was applied to Crosstalk corrected [58] ASTER data for atmospheric correction.…”