
Accuracy of narrow-band spectral indices estimation by wide-band remote sensing data
Author(s) -
Sergey Stankevich
Publication year - 2022
Publication title -
ukraïnsʹkij žurnal distancìjnogo zonduvannâ zemlì
Language(s) - English
Resource type - Journals
ISSN - 2313-2132
DOI - 10.36023/ujrs.2022.9.1.209
Subject(s) - spectral bands , remote sensing , hyperspectral imaging , multispectral image , normalized difference vegetation index , multispectral pattern recognition , spectral line , spectral index , environmental science , geology , physics , oceanography , climate change , astronomy
Narrow-band spectral indices are quite informative and important in various applications of remote sensing – to assess the condition of vegetation, soils, water bodies and other land surface formations. However, direct measurement of narrow-band spectral indices requires hyperspectral imaging. But most of modern multispectral aerospace imaging systems are wide-band. Accordingly, it is not possible to calculate the narrow-band index directly from wide-band remote sensing data. This paper discusses approaches to the narrow-band spectral indices restoration by wide-band remote sensing data using statistical models of interrelations of narrow- and wide-band indices itself, of source wide-band and narrow-band signals in close spectral bands, as well as of land surface reflectance quasi-continuous spectra translation from wide bands to narrow ones.The experimental accuracy estimation of narrow-band spectral indices restoration by wide-band multispectral satellite image is performed. Three most complicated narrow-band spectral indices, which covering a range of spectrum from visible to short-wave infrared, were considered, namely – the transformed chlorophyll absorption in reflectance index (TCARI), the optimized soil-adjusted vegetation index (OSAVI) and the normalized difference nitrogen index (NDNI). All three mentioned methods for narrow-band spectral indices restoration are analyzed. The worst result is demonstrated for regression-restored signals in spectral bands, and the best result is for the spectra translation method. Therefore, the method on the basis of spectra translation is recommended for practical implementation.