Accuracy of narrow-band spectral indices estimation by wide-band remote sensing data

• Дата: 2022
• Автор: Stankevich, Sergey
• Формат: Стаття
• Мова: English
• Опубліковано: Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine 2022
• Теми: narrow-band spectral index; multispectral imagery; spectral band; regressional dependence; accuracy estimation; spectral library; узкополосный спектральный индекс; многоспектральное изображения; спектральный диапазон; регрессионная зависимость; оценка точности; библиотека спектров; вузькосмуговий спектральний індекс; багатоспектральне зображення; спектральний діапазон; регресійна залежність; оцінка точності; бібліотека спектрів
• Онлайн доступ: https://ujrs.org.ua/ujrs/article/view/209
• Назва журналу: Ukrainian Journal of Remote Sensing of the Earth

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.