Growing numbers of studies have focused on evaluating the ability of vegetation indices (VIs) to predict biophysical parameters such as leaf area index (LAI) and chlorophyll. In this study, empirical models were used to estimate winter wheat LAI based on three spectral indices [the normalized difference vegetation index (NDVI), the modified simple ratio index (MSR), and the modified soil-adjusted vegetation index (MSAVI)], and three band-selection approaches (the conventional approach, the red edge approach, and the best correlated approach), which were used to calculate VIs. The aim was to enhance the relationships between the indices and LAI values by improving the band-selection approaches so as to produce a suitable VI for winter wheat LAI estimation. Using hyperspectral airborne data and ground-measured spectra as well as ground LAI measurements collected during two field campaigns, winter wheat LAIs were estimated and validated using different VIs calculated by different band combinations.
Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices / Laneve, Giovanni; Xie, Q.; Huang, W.; Zhang, B.; Chen, P.; Song, X.; Pascucci, S.; Pignatti, S.; Dong, Y.. - In: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. - ISSN 1939-1404. - ELETTRONICO. - 99:PP(2016), pp. 1-10. [10.1109/JSTARS.2015.2489718]
Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices
LANEVE, Giovanni;
2016
Abstract
Growing numbers of studies have focused on evaluating the ability of vegetation indices (VIs) to predict biophysical parameters such as leaf area index (LAI) and chlorophyll. In this study, empirical models were used to estimate winter wheat LAI based on three spectral indices [the normalized difference vegetation index (NDVI), the modified simple ratio index (MSR), and the modified soil-adjusted vegetation index (MSAVI)], and three band-selection approaches (the conventional approach, the red edge approach, and the best correlated approach), which were used to calculate VIs. The aim was to enhance the relationships between the indices and LAI values by improving the band-selection approaches so as to produce a suitable VI for winter wheat LAI estimation. Using hyperspectral airborne data and ground-measured spectra as well as ground LAI measurements collected during two field campaigns, winter wheat LAIs were estimated and validated using different VIs calculated by different band combinations.File | Dimensione | Formato | |
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