Pansharpening largely preserves the normalized difference vegetation index: a multi-sensor comparison with PRISMA, Landsat 9, and field spectroscopy - Procházka, Jan; Komárek, Jan; Moravec, David; Rous, Jiří; Klouček, Tomáš (Frontiers in Remote Sensing, 2026)
Pansharpening is widely used to increase the spatial resolution of satellite imagery, yet its effects on vegetation indices derived from hyperspectral sensors remain poorly quantified. This study tests how five standard software pansharpening algorithms alter Normalized Difference Vegetation Index (NDVI) values derived from PRISMA hyperspectral and Landsat 9 multispectral data. We analysed 14 spectrally homogeneous plots within a 6.5 km2 agricultural site in the Czech Republic in 2022 using acquisitions from Landsat 9 (04 August), PRISMA (07 August), ASD FieldSpec 4 field spectrometer, and UAV-borne MicaSense RedEdge-MX measurements (08 August). Across algorithms, deviations in NDVI relative to native imagery were statistically insignificant for both sensors (Landsat 9 and PRISMA); however, algorithm choice affected accuracy. The Principal Component (PC) Nearest Neighbor method had the largest errors (MAE = 0.093 for PRISMA; 0.078 for Landsat 9) and, for PRISMA, a positive bias relative to FieldSpec 4 (bias = +0.111). In contrast, NN Diffuse and Local Mean and Variance Matching (LMVM) produced the lowest errors. For PRISMA, NN Diffuse achieved an MAE of 0.049 and a bias of +0.040, while LMVM yielded an MAE of 0.050 and a bias of +0.016. For Landsat 9, NN Diffuse reached an MAE of 0.052 and a bias of +0.014, and LMVM produced an MAE of 0.059 and a bias of −0.018. UAV-derived NDVI agreed less with ASD FieldSpec 4 than the native satellite data (MAE = 0.056). Our results provide the first empirical assessment of pansharpening effects on PRISMA hyperspectral NDVI and demonstrate that, with robust algorithms and careful preprocessing, pansharpening does not materially distort NDVI. While the study is restricted to a single site, a single growing season, and one vegetation index, it highlights methodological considerations that can inform broader applications of hyperspectral pansharpening not only in vegetation monitoring.