Abstract

This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods—Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)—and two VIs—Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)—were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery). BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single- and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over- or under-estimated VI values.

Highlights

  • Vegetation Indices (VIs) derived from satellite images are a useful data source for many agricultural, environmental, and climate studies

  • Δ should not be correlated with Normalized Difference Vegetation Index (NDVI) if the mismatching between Pan and I is consistent across all NDVI values

  • This study investigated the effects of pansharpening on Vegetation Index (VI) images

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Summary

Introduction

Vegetation Indices (VIs) derived from satellite images are a useful data source for many agricultural, environmental, and climate studies. VIs have been used extensively for remote estimation of above-ground biomass [1], leaf area index [2], fraction of photosynthetically active radiation [3], net primary productivity [4], crop yields [5], fractional green vegetation cover [6,7], and many other important vegetation parameters for agricultural, ecological, and climate models. High resolution VI data is important for monitoring small agricultural fields/small vegetation patches (to reduce the number of mixed pixels along field or patch boundaries), for monitoring sub-field/sub-patch variability in vegetation (e.g., to identify areas of vegetation stress, disease, or physical damage), and for detecting fine-scale changes in vegetation over time. Moderate resolution satellite imagery (around 10 m or coarser), on the other hand, can be relatively inexpensive or even free. Landsat data is the most commonly-used free moderate resolution data worldwide, and the newest satellite in the series, Landsat 8, has a panchromatic (Pan) band with a spatial resolution of 15 m, eight visible to shortwave-infrared bands with spatial resolutions of 30 m, and two thermal-infrared bands with spatial resolutions of 100 m [8]

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