Next Article in Journal
Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices
Previous Article in Journal
Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(4), 317;

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
E & J Gallo Winery Viticulture Research, Modesto, CA 95354, USA
USDA-ARS, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USA
California State University, Monterey Bay, School of Natural Resources, Seaside, CA 93933, USA
NASA Ames Research Center, Moffett Field, CA 94035, USA
Author to whom correspondence should be addressed.
Academic Editors: Assefa M. Melesse, Alfredo R. Huete, Clement Atzberger and Prasad S. Thenkabail
Received: 28 November 2016 / Revised: 8 March 2017 / Accepted: 23 March 2017 / Published: 28 March 2017
Full-Text   |   PDF [6655 KB, uploaded 28 March 2017]   |  


Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have 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 vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry. View Full-Text
Keywords: wine grape; satellite remote sensing; NDVI; LAI; yield; field-scale wine grape; satellite remote sensing; NDVI; LAI; yield; field-scale

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Sun, L.; Gao, F.; Anderson, M.C.; Kustas, W.P.; Alsina, M.M.; Sanchez, L.; Sams, B.; McKee, L.; Dulaney, W.; White, W.A.; Alfieri, J.G.; Prueger, J.H.; Melton, F.; Post, K. Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards. Remote Sens. 2017, 9, 317.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top