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In this study, we used visible/near-infrared (Vis/NIR) hyperspectral imaging technology to measure the reflectance and gray-level texture features of grape leaf surfaces, and we predicted water content based on those features for more effective irrigation control. Grape vine specimens were potted in the actual field environment, and five levels of water treatment with two replicates were applied to create controlled water stresses. The reflectance and gray-level co-occurrence matrix (GLCM) texture features were used to develop partial least squares (PLS) models for water content prediction. The results showed that when using both reflectance and GLCM texture features, the results achieved a correlation coefficient (rp), root mean square error of prediction (RMSEP), bias, and residual predictive deviation (RPD) of 0.900, 0.826, -2.213e-04, and 2.084, respectively. The experimental results indicated that using a Vis/NIR hyperspectral imaging system could enable this combined method of reflectance and GLCM texture to predict water content of grape vines and be beneficial for grape vine management. [ABSTRACT FROM AUTHOR]

Knowledge of phenological events in grapevines is essential for successful vineyard management. Conventional ground-observed phenological measurements are limited mainly due to their spatial coverage. However, satellite data provide access to global spatial coverage with the potential for high temporal resolution. The goal of this study was to evaluate the ability of vegetation indices based on remote sensing to estimate the growing season length of grapes in central Washington State. Several phenological metrics for vineyards located in the Columbia Valley region were derived from the satellite time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) using the normalized difference vegetation index (NDVI). The methodology included exponential smoothing and a moving average to compute both the onset of greenness and the end of greenness. The MODIS NDVI values were evaluated with aerial NDVI images of the same vineyard from August 2011. The average bias was -0.08, the average root mean squared error (RMSE) was 0.16, and the coefficient of determination (R²) was 0.5 (p = 0.06). The results revealed an average growing season duration of 216 days for grapevines in this region for a period of five years. The average starting date of the growing season was April 2, and the computed end of the growing season was November 4. The highest NDVI value was 0.55, which coincided with July 12. On average, the lowest NDVI value was 0.3, and the average range of NDVI was 0.25. The preliminary results of this study showed that MODIS NDVI can be used to monitor vineyard vegetation dynamics in the Columbia Valley and has potential to be applied to other grape-growing regions in the U.S. and across the globe. [ABSTRACT FROM AUTHOR]