Herrmann, I., The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990, Israel
Pimstein, A., The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990, Israel
Karnieli, A., The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990, Israel
Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Bonfil, D.J., Field Crops and Natural Resources Department, Agricultural Research Organization, Gilat Research Center, Israel

Leaf Area Index (LAI) is an important variable that governs canopy processes and can be monitored by satellites. The current study aims at exploring the potential and limitations of using the red-edge spectral bands of the forthcoming superspectral satellites, namely-Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2, for assessing LAI in field crops. The research was conducted in experimental plots of wheat and potato in the northwestern Negev, Israel. Continuous spectral data were collected by a field spectrometer and LAI data were obtained by a ceptometer. The spectral data were resampled to the superspectral VENμS and Sentinel-2 resolutions. The data were divided into seven datasets (four seasons, two crops, and one including all data). The LAI prediction abilities by Partial Least Squares (PLS) models for continuous spectra and the resampled spectra were compared and evaluated. For wheat and potato of the continuous, VENμS, and Sentinel-2 data formations, the PLS correlation coefficients (r) values were 0.93, 0.93, and 0.92, respectively. In most cases, the red-edge region was found to be the most important spectral region for the three data formations, according to the Variable Importance in Projection (VIP) analysis. Additionally, Normalized Difference Vegetation Index (NDVI) and the Red-Edge Inflection Point (REIP) were computed for the three data formations in order to observe relation to as well as prediction accuracy in retrieving LAI values. The prediction abilities of the calculated indices by the data formations were compared, peaking for wheat, with r values of 0.91 for the REIP for the three data formations. Therefore, it is concluded that VENμS and Sentinel-2 can spectrally assess LAI as good as a hyperspectral sensor. The REIP was found to be a significantly better predictor than NDVI for wheat data and therefore can potentially be implemented for future LAI monitoring applications by superspectral sensors that contain four red-edge bands. © 2011 Elsevier Inc.