Leaf Area Index (LAI) is a key variable for spatiotemporal modelling and analysis of several land surface processes. LAI can be successfully estimate by means of Vegetation Indices (VIs), retrieved from multispectral satellite images, however the different VIs show variable estimation uncertainty in relation to vegetation characteristics and soil background condition. In particular, VIs can show saturation behaviour at medium/high vegetation density. Thus, in this study we aimed at implementing parametric approach considering VIs belonging to three different classes computed on visible, red-edge and short-wave infrared spectral band combination provided by (multi spectral instrument) MSI sensor onboard Sentinel-2 satellites constellation. Results show that all VIs are generally well correlated to ground LAI, among the 11 tested ones EVI, NDI45 and NBR shows best results for the three considered categories.
Multi Crop Estimation of LAI from Sentinel-2 VIs with Parametric Regression Approach: Comparison of Performances and VIs Sensitivity / M. De Peppo, F. Nutini, G. Candiani, G. Ragaglini, A. Taramelli, F. Filipponi, M. Boschetti (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Communications in Computer and Information Science / [a cura di] E. Borgogno-Mondino, P. Zamperlin. - [s.l] : Springer, 2022. - ISBN 978-3-031-17438-4. - pp. 222-234 (( Intervento presentato al 25. convegno Geomatics for Green and Digital Transition tenutosi a Genova nel 2022 [10.1007/978-3-031-17439-1_16].
Multi Crop Estimation of LAI from Sentinel-2 VIs with Parametric Regression Approach: Comparison of Performances and VIs Sensitivity
G. Ragaglini;
2022
Abstract
Leaf Area Index (LAI) is a key variable for spatiotemporal modelling and analysis of several land surface processes. LAI can be successfully estimate by means of Vegetation Indices (VIs), retrieved from multispectral satellite images, however the different VIs show variable estimation uncertainty in relation to vegetation characteristics and soil background condition. In particular, VIs can show saturation behaviour at medium/high vegetation density. Thus, in this study we aimed at implementing parametric approach considering VIs belonging to three different classes computed on visible, red-edge and short-wave infrared spectral band combination provided by (multi spectral instrument) MSI sensor onboard Sentinel-2 satellites constellation. Results show that all VIs are generally well correlated to ground LAI, among the 11 tested ones EVI, NDI45 and NBR shows best results for the three considered categories.
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