Abstract: 【Objective】A method for fast, non-destructive and accurately monitoring leaf water content (LWC) of wheat (Triticum aestivum L.) was improved with hyperspectra technology in this paper. 【Method】The canopy leaf spectral reflectance in main growing seasons of wheat was collected under the condition of water stress experiment. Using the frist-order derivative spectra, 16 new hyperspectral indices were developed to quantify the wheat’s leaf water content (LWC). These indices were then compared with the commonly used hyperspectral indices including NDII, WBI and NDWI to screen out the spectral index which was sensitive to LWC for modeling. Using the inversion model, the OMIS image was calculated one pixel by one pixel, and the remote sensing mapping for LWC of wheat was accomplished. 【Result】The accuracy of inversion model (logarithmic function) which built by index FD730-955 was higher than that by the hyperspectral indices commonly used, as indicated by a calibration determination coefficient (C-R2) of 0.749 and a validation determination coefficient (V-R2) of 0.742. The eatimation values of 32 samples in prediction set were close to the measured values, and R2 and RMSE of regression fitted model between two dataset were 0.763 and 0.024, respectivly. Furthere more, the prediction accuracy of FD730-955 was least sensitive to the change of LWC and LAI among all of the hyperspectra indices and therefore least affected by the range of sample values and canopy density when used to estimate the LWC of wheat. The R2 and RMSE of the fitting model for the inversion and measured values were 0.635 and 0.027, respectively, and indicated the similarity between the inversion and measured value was high. 【Conclusion】 It is possible to eatimate LWC of wheat by derivative hyperspectra with a high accuracy, and FD730-955 is an optimal index for modeling.

Key words: hyperspectra remote sensing , derivative spectrum , wheat (Triticum aestivum L.) , leaf water content (LWC) , leaf area index (LAI)