Evaluation of multi-temporal sentinel-2 capabilities for estimation of leaf chlorophyll concentration

Remote sensing can provide a fast and non-destructive estimation of leaf chlorophyll content and consequently can be used in plant nitrogen status evaluation. Such evaluation can be of valuable assistance in modifying the fertilization strategy in an area of interest. Sentinel-2 has the advantage of the high spatial, spectral and temporal resolution and therefore can be considered a powerful tool in such studies. Furthermore, it incorporates three new spectral bands in the red-edge region which enhances its capabilities for vegetation studies. The present work addresses the comparison between the sensitivity of three approaches in processing Sentinel 2 data for the estimation of leaf chlorophyll concentrations of wheat and olive cultivated in a selected area in the North West of Sinai Peninsula, Egypt. These approaches included the use of reflectance data of selected bands and vegetation indices with or without the red edge bands. The proposed approach has an overall accuracy expressed as coefficient of determination of 0.90 between the actual and predicted chlorophyll. This approach utilized neural networks and used two vegetation indices the Chlorophyll vegetation index (CVI) and the Inverted Red-Edge Chlorophyll Index (IRECI) in addition to the plant type.