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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #334924

Title: Daily mapping of 30m LAI and NDVI for grape yield prediction in California vineyards

item SUN, L - US Department Of Agriculture (USDA)
item Gao, Feng
item Anderson, Martha
item Kustas, William - Bill
item ALSINA, MIMAR - E & J Gallo Winery
item SANCHEZ, L. - E & J Gallo Winery
item SAMS, BRENT - E & J Gallo Winery
item McKee, Lynn
item Dulaney, Wayne
item White, William - Alex
item Alfieri, Joseph
item Prueger, John
item MELTON, F. - National Aeronautics And Space Administration (NASA) - Johnson Space Center

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/23/2017
Publication Date: 3/28/2017
Citation: Sun, L., Gao, F.N., Anderson, M.C., Kustas, W.P., Alsina, M., Sanchez, L., Sams, B., Mckee, L.G., Dulaney, W.P., White, W.A., Alfieri, J.G., Prueger, J.H., Melton, F. 2017. Daily mapping of 30m LAI and NDVI for grape yield prediction in California vineyards. Remote Sensing. doi:10.3390/rs9040317.

Interpretive Summary: The ability to accurately and efficiently monitor vine development and estimate grape yields within season has significant benefit to the wine producing industry. Daily monitoring of vineyard conditions is required for optimal grape management, but ground-based measurements are not likely to be representative of whole-field conditions and are costly to install and maintain. Remotely sensed data offer strong advantages over other monitoring techniques by providing the up-to-date overview of actual crop growing conditions during the growing season. This paper focuses on the spatial variability of grape yields for 2013 and 2014 achieved in two adjacent Pinot Noir vineyards located in the Central Valley of California, USA. The spatial correlations between yield and NDVI, LAI were evaluated and analyzed. Based on the findings, a simple grape yield prediction strategy is proposed for improving grape yield estimation which is critical for wine grape growers and also required by the National Agricultural Statistics Service.

Technical Abstract: Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are normally very sparse and cannot capture the spatial variability of vine conditions. Remote sensing data acquired from visible and near infrared bands provide detailed spatial and temporal information regarding vine development that may be useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot noir vineyards in California, USA. Daily satellite indices were generated through interpolation using an adaptive Savitzky–Golay (SG) filtering function, and used to extract key phenological stages of vine growth. The spatial correlation between yield maps and these daily time series (LAI, NDVI) was quantified. NDVI and LAI were found to have the similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative satellite indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined by using a simple strategy calibrated with a few ground measurements. This strategy may improve accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.