Abstract: Abstract: Hyperspectral remote sensing has been commonly employed for crop LAI estimation in recent years. However, the advantages of hyperspectral data compared with multispectral data in LAI estimation remain debate. To compare multispectral and hyperspectral remote sensing for LAI estimation, five datasets with different spectral resolution, spectral coverage, and band selection were tested for retrieving LAI by inverting the ACRM (A Two-Layer Canopy Reflectance Model) model in this study. The study area is located in Shenzhou, Hebei Province, China. A field experiment was conducted during the jointing and heading stages of winter wheat (Triticum aestivum) in 2014. In situ measurements were performed in five winter wheat cultivars. The canopy spectra and the biophysical variables (LAI, leaf chlorophyll content, and leaf specific weight etc.) were measured. The inversion technique based on a look-up table (LUT) is adopted with the following procedure. Firstly, for determining the free variables of the LUT, sensitivities of the ACRM variables were evaluated using the EFAST algorithm. Two schemes of parameterizations were designed, separately denoted as "S1" and "S2". The scheme S1 had 7 variables, whose EFAST global sensitivity index was larger than 0.1, as free variables. The scheme S2 further was used to fixe leaf mesophyll structure and Markov clumping parameter to their best estimation. Secondly, to select the optimum hyperspectral bands for LAI estimation, stepwise regression was adopted to eliminate the multicollinearity in hyperspectral data. The results of stepwise regression were further adjusted to avoid errors in spectral simulation. Thirdly, five datasets, separately denoted as B1 to B5, were composed based on the in situ measured hyperspectral spectra and the result of band selection, including B1: the synthetic Landsat 5 TM data; B2: hyperspectral data (5 nm spectral resolution) of visible light and near inferred (VNIR, 445-1 065 nm); B3: hyperspectral data covering the sensitive bands of TM within VNIR (445-945 nm); B4: the selected hyperspectral bands for LAI estimation; B5: multispectral data of 20 nm spectral resolution, with their center wavelengths located at the selected hyperspectral bands. The accuracy and stability between LAI retrieval based on the two schemes of ACRM parameterization and using the five datasets were compared. The experiments showed that: first, within the range of VNIR, LAI estimation did not benefit from the wider spectral coverage of in situ measured hyperspectral data than the synthetic TM data. Second, if the bands participating in the inversion were properly selected and the uncertainty in the parameterization of the ACRM model was fairly low, remote sensing data of higher spectral resolution would generally result in a more accurate LAI estimation. In this case, the effects of spectral resolution to the inversion accuracy were not linear. With the increase of spectral resolution, the benefit from higher spectral resolution could decrease. For instance, B5 yielded significantly more accurate LAI estimations than B1; however, B4 performed merely slightly better than B5. Third, if the bands for retrieving LAI were not properly selected (for instance, using dataset B3), or the parameterization of ACRM model was fairly uncertain (for instance, using the scheme S1), remotely sensed data with higher spectral resolution could not result in more accurate LAI estimation. In conclusion, remotely sensed data with higher spectral resolution generally yielded more accurate LAI estimation only when the band selection was properly performed and the uncertainty of the parameters was fairly low. Otherwise, there was no significant difference between multispectral and hyperspectral data for crop LAI retrieval. This study provides information for the advantages of using hyperspectral data to estimate LAI. Moreover, this study reveals the great potential to enhance the accuracy of LAI estimation by using multispectral data with relevantly high spectral resolution, for instance, MODIS, Landsat 8 OLI and WorldView 3.