Precision irrigation for jujube crop in southern Xinjiang, China, is underlying to optimize the water use in such a drought-affected region. Water stress can be remotely assessed by evaluating the leaf moisture content using spectroscopy. These measurements are however affected by the presence of coarse sand and dust of the leaves induced by dry climates. This paper studied different methods to correct the spectral data in order to reduce the scattering noise with a baseline induced by such a jujube leaf covering. The reflectance of 120 leaf samples were measured by means of a near-infrared spectrometer (1 000～1 800 nm) and their moisture content was obtained by conventional drying method. The original reflectance spectrums were pre-processed by the normalization method, the moving smoothing method, the Savitzky-Golay (SG) convolution smoothing method, the SG first derivative method, the standard normal variables (SNV) method and the multiple scatter correction (MSC) method. The results of these different methods were compared and analyzed by means of partial leastsquares regressions (PLSR) allowing selecting sensitive spectral bands and establishing prediction models. The results showed that a significant reflectance peak related to the water content of the jujube leaves was located at 1 443 nm and that a local minimum of reflectance occurred at 1 661 nm. The predicition model based on the MSC method presented the best scattering noise reduction. The model performances were R2=0.750 4, RMSEP=0.034 3 and RMSEPCV=0.021 5. The five characteristic wavelengths were 1 002, 1 383, 1 411, 1 443 and 1 661 nm. In this experiment, the MSC method had a good ability to reduce the scattering noise generated by sand and dust covering. The preprocessing improved the selection ability of characteristic wavelengths and the accuracy of the prediction model. The results can therefore provide an effective detection method for the jujube leaf water in the sandy and dusty area of Southern Xinjiang, China.